Lab_10_SVM.ipynb

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      "source": [
        "<h1 align=\"center\">Zewail City of Science and Technology</h1>\n",
        "<h2 align=\"center\">CIE 417 (Fall 2020)</h2>\n",
        "<h2 align=\"center\">Lab 10: SVM </h2>\n",
        "<h3 align=\"center\">Eng. Ahmed Wael</h3>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ag6RPIIg0WLq"
      },
      "source": [
        "## Import the needed libraries"
      ]
    },
    {
      "cell_type": "code",
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          "base_uri": "https://localhost:8080/"
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        "outputId": "26e8feee-b2dc-4fff-9ce3-6abcac239970"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import itertools\n",
        "\n",
        "from sklearn import datasets\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import metrics\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.gridspec as gridspec\n",
        "\n",
        "from mlxtend.plotting import plot_decision_regions\n",
        "from mlxtend.preprocessing import shuffle_arrays_unison\n",
        "import seaborn as sns\n",
        "\n",
        "from tqdm.notebook import tqdm_notebook as tqdm\n",
        "\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "from google.colab import files\n",
        "\n",
        "!pip install PyStemmer\n",
        "import Stemmer\n",
        "\n",
        "from sklearn.naive_bayes import GaussianNB\n"
      ],
      "execution_count": null,
      "outputs": [
        {
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          "text": [
            "Collecting PyStemmer\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/55/b2/c3aeebfe4a60256ddb72257e750a94c26c3085f017b7e58c860d5aa91432/PyStemmer-2.0.1.tar.gz (559kB)\n",
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            "\u001b[?25hBuilding wheels for collected packages: PyStemmer\n",
            "  Building wheel for PyStemmer (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for PyStemmer: filename=PyStemmer-2.0.1-cp36-cp36m-linux_x86_64.whl size=423622 sha256=12c1e9d2f48ebe39f87eef18322cf049284927b8bb1432755a91b7084a824885\n",
            "  Stored in directory: /root/.cache/pip/wheels/f3/3c/11/ee323a09706e17a649c2730bd8819b06e887411ff7507acf7a\n",
            "Successfully built PyStemmer\n",
            "Installing collected packages: PyStemmer\n",
            "Successfully installed PyStemmer-2.0.1\n"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ucDjKzoLOajd"
      },
      "source": [
        "# Outline:\n",
        "\n",
        "*   SVM Intuition\n",
        "\n",
        "\n",
        "*   Hard vs Soft Margins\n",
        "\n",
        "\n",
        "*   C hyper-parameter\n",
        "\n",
        "\n",
        "*   Kernel Trick\n",
        "\n",
        "\n",
        "*   Kernel Types\n",
        "\n",
        "\n",
        "*   SVM with Linear Dataset\n",
        "\n",
        "\n",
        "*   SVM with Non-linear Dataset\n",
        "\n",
        "\n",
        "*   Hyper-parameter Tuning & Cross Validation\n",
        "\n",
        "\n",
        "*   Multi-class SVM\n",
        "\n",
        "\n",
        "*   SVM with Regression\n",
        "\n",
        "\n",
        "*   Tasks\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "02XFhcDE0WLr"
      },
      "source": [
        "# SVM Intuition:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4eCXsCgo0WLr"
      },
      "source": [
        "## What is the best hyper-plane?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gM-0Pnz-0WLs"
      },
      "source": [
        "### Case 1: Correct Classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c4rkWK710WLs"
      },
      "source": [
        "<img src=https://www.analyticsvidhya.com/wp-content/uploads/2015/10/SVM_21.png>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ML0XzENs0WLs"
      },
      "source": [
        "**The best hyper-plane is the one that separates the two classes better**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c4Dd7u8X0WLt"
      },
      "source": [
        "**Therefore, B is best one in this case**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nQygFHbn0WLt"
      },
      "source": [
        "### Case 2: Maximizing the Margin"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KZ83y6bM0WLu"
      },
      "source": [
        "<img src=https://www.analyticsvidhya.com/wp-content/uploads/2015/10/SVM_3.png>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qry7OJih0WLv"
      },
      "source": [
        "**The three hyper-planes separate the two classes, but which of them is the best one?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G2MvWKHo0WLv"
      },
      "source": [
        "**We care about maximizing the distances between the nearest data point (either class) and hyper-plane**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NODcO20Z0WLw"
      },
      "source": [
        "**We call this distance The Margin**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PLUZdVyk0WLw"
      },
      "source": [
        "<img src=https://www.analyticsvidhya.com/wp-content/uploads/2015/10/SVM_4.png>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IksU_FBN0WLx"
      },
      "source": [
        "**Thus, C is the best hyper-plane.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MFIFwqD_0WLx"
      },
      "source": [
        "### Case 3: Correct Classification vs Maximizing the Margin"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9bhzIkie0WLx"
      },
      "source": [
        "<img src=https://www.analyticsvidhya.com/wp-content/uploads/2015/10/SVM_5.png>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zk5GORuv0WLy"
      },
      "source": [
        "**By default, SVM gives more weight to correct classification than margin maximization.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oMf6p-A_0WLy"
      },
      "source": [
        "**Thus, A is the best classifier in this case**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QaiPT5DR0WLy"
      },
      "source": [
        "### Case 4: Outliers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PqHIBwVB0WLy"
      },
      "source": [
        "<img src=https://www.analyticsvidhya.com/wp-content/uploads/2015/10/SVM_71.png>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nueZdIom0WLz"
      },
      "source": [
        "**SVM gives more weight to correct classification than margin maximization, right?**\n",
        "\n",
        "\n",
        "**However, this also means that margin maximization is important!**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0QNwfwGs0WLz"
      },
      "source": [
        "**Thus, SVM can detect outliers and ignore them**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MI6ywxe00WLz"
      },
      "source": [
        "### Case 5: Non-linear hyper-plane:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DofhGdKx0WLz"
      },
      "source": [
        "<img src=https://www.analyticsvidhya.com/wp-content/uploads/2015/10/SVM_8.png>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pnFTPXWp0WL0"
      },
      "source": [
        "**SVM solves this by introducing a new feature which is**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-lXfb1v40WL0"
      },
      "source": [
        "$$z = x^2 + y^2$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VVj9LyrH0WL0"
      },
      "source": [
        "<img src=https://www.analyticsvidhya.com/wp-content/uploads/2015/10/SVM_9.png>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cktw4RPv0WL0"
      },
      "source": [
        "**This feature is NOT added manually**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ETBfH74M0WL1"
      },
      "source": [
        "**It is done using the KERNEL!**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B4X6LJXN0WL1"
      },
      "source": [
        "## What is the SVM Kernel?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dKAQcNXk0WL1"
      },
      "source": [
        "- It takes low dimensional input space and transforms it to a higher dimensional space\n",
        "\n",
        "\n",
        "- Converts not separable problem to separable problem.\n",
        "\n",
        "\n",
        "- Mostly useful in non-linear separation problem."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9qBVk1yc0WL1"
      },
      "source": [
        "## Why Maximize Margin?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vOuLA2ku0WL1"
      },
      "source": [
        "<img src=https://i.ibb.co/Qf5bzR2/1.jpg>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sc9y6SKf0WL2"
      },
      "source": [
        "**This line not only separates\n",
        "the two classes but also\n",
        "stays as far away from the\n",
        "closest training instances\n",
        "as possible**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LtYggQ-k0WL2"
      },
      "source": [
        "**Robust even with high margin of noise**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ryxyEvoU0WL2"
      },
      "source": [
        "# Hard vs Soft Margins"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yp7g2BIt0WL2"
      },
      "source": [
        "## Margin Equation:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UoeF35WR0WL3"
      },
      "source": [
        "<img src=https://i.ibb.co/d7wXYw0/2.jpg>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x6XuHym80WL3"
      },
      "source": [
        "**if we want to Maximize M**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "os9n3YeE0WL3"
      },
      "source": [
        "$$M = \\frac{2}{|w|}$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eI-9v1ut0WL3"
      },
      "source": [
        "**This means we want to minimize:**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0cMk5uLx0WL3"
      },
      "source": [
        "$$ \\frac{1}{2}w^Tw$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jDzDUg8R0WL4"
      },
      "source": [
        "**So, we have an optimization problem as follows:**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I0WMvHrZ0WL4"
      },
      "source": [
        "Find w and b such that:\n",
        "\n",
        "\n",
        "- $$\\phi(w) = \\frac{1}{2}w^Tw$$ is minimized\n",
        "\n",
        "\n",
        "- For all $${(x_i ,y_i)}: y_i (w^Tx_i + b) ≥ 1$$\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j9So1WGI0WL4"
      },
      "source": [
        "- Need to optimize a quadratic function subject to linear constraints.\n",
        "\n",
        "\n",
        "- Quadratic optimization problems are a well-known class of mathematical programming problems.\n",
        "\n",
        "\n",
        "- The solution involves constructing a dual problem where a Lagrange multiplieris associated with every constraint in the primary problem.\n",
        "\n",
        "**This is out of the scope for the course, but will find in the resources at the end of the notebook how all is this is done mathemtically**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YV60-IU00WL5"
      },
      "source": [
        "<img src=https://miro.medium.com/max/1172/1*PiGj6vEyBhxbXfK4bzwwTg.png>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ujRFcqZt0WL5"
      },
      "source": [
        "**Hard Margin Requires ALL data points to be classified correctly**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eyL5VKS20WL5"
      },
      "source": [
        "**What if the training set is noisy?**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DeRCo-H20WL5"
      },
      "source": [
        "**The kernel will be very complex in order to fit all the data correctly**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FyF8h0Dp0WL5"
      },
      "source": [
        "**Thus, will result in OVERFITTING**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gEdz91Mh0WL6"
      },
      "source": [
        "<img src=https://i.ibb.co/C6YcH9j/3.jpg>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l1zcn9wY0WL6"
      },
      "source": [
        "**Slack variables ε can be added to allow misclassification of difficult or noisy examples.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hYgpXbJX0WL6"
      },
      "source": [
        "<img src=https://i.ibb.co/Qbb8D7g/4.jpg>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FGxBwDv50WL6"
      },
      "source": [
        "**Now, we want to minimze:**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GZ1mI-eW0WL7"
      },
      "source": [
        "$$ \\frac{1}{2}w^Tw + C \\sum{\\varepsilon_k}$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6VdI8OPY0WL7"
      },
      "source": [
        "**So, the new formulation for the optimization problem:**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eCJWi69e0WL7"
      },
      "source": [
        "Find w and b such that:\n",
        "\n",
        "\n",
        "- $$\\phi(w) = \\frac{1}{2}w^Tw + C \\sum{\\varepsilon_k}$$ is minimized\n",
        "\n",
        "\n",
        "- For all $${(x_i ,y_i)}: y_i (w^Tx_i + b) ≥ 1 - \\varepsilon_i$$ \n",
        "\n",
        "$$ \\varepsilon_i ≥ 0 $$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gPvyzVvz0WL7"
      },
      "source": [
        "# C Hyperparameter"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N9UUDWdw0WL8"
      },
      "source": [
        "- **Parameter C can be viewed as a way to control overfitting**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tq3hLpvG5oxp"
      },
      "source": [
        "- **C bounds the sum of the εi ’s, and so it determines the number and severity of the violations to the margin (and to the hyperplane) that we will tolerate.**\n",
        "\n",
        "\n",
        "- **Increasing C, increases bias, reduces the variance (we become more tolerant of violations to the margin, margin will widen)**\n",
        "\n",
        "\n",
        "- **Decreasing C, reduces bias, increases variance (we become less tolerant of violations to the margin, margin narrows)**\n",
        "\n",
        "\n",
        "- **C is generally chosen via cross-validation.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ah6yVPmdPIw3"
      },
      "source": [
        "# Kernel Trick"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e5auqXA8PTgB"
      },
      "source": [
        "**After Solving the lagrange optimization problem, we will have**\n",
        "$$min_\\alpha = -\\sum{\\alpha_i} + \\frac{1}{2}\\sum_{i=1}^{n}\\sum_{j=1}^{n}{y^i y^j \\alpha^i \\alpha^j <X^i, X^j> } $$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gyypcWZxQVVi"
      },
      "source": [
        "Where $$ <X^i, X^j > $$ is the dot product between two examples, subject to $$ \\alpha_i > 0$$ $$\\sum{\\alpha_i y^i} = 0$$\n",
        "and \n",
        "$$ \\alpha$$\n",
        "is the a slack variable introduced in the lagrangian, which we are solving for"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W0f7GfBGYqnB"
      },
      "source": [
        "<img src = https://i.ibb.co/rZMd7mS/6.jpg>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1mwrb3PcQVXr"
      },
      "source": [
        "**Now, the kernel Trick**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "53y8RB4RQVa0"
      },
      "source": [
        "Imagine a function φ that maps the data into another space"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vx4NsVqkZQ-Q"
      },
      "source": [
        "\n",
        "<img src= https://i.ibb.co/7QvhKPR/7.jpg pos=10>\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AjFmFn-QZkWk"
      },
      "source": [
        "The function we want to optimize is the **dot product** of the two feature vectors."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6fwasoyYZkZV"
      },
      "source": [
        "If we now transform to φ, instead of computing this dot product (xi•xj) we will have to compute (φ (xi)• φ (xj))."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ssZdPLrTZkcS"
      },
      "source": [
        "But how can we do this? This is **expensive** and **time consuming**\n",
        "\n",
        "(suppose φ is a quartic polynomial … or worse, we don’t know the\n",
        "function explicitly."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CFW-3g2MaM-c"
      },
      "source": [
        "If there is a **\"kernel function\"** K such that $$K(x_i,x_j) = φ (x_i)• φ (x_j)$$ \n",
        "\n",
        "Then **we do not need to knowor compute φ at all!!**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NPrgANPiaNBg"
      },
      "source": [
        "That is, the kernel function defines **inner products** in the transformed space.\n",
        "\n",
        "Or, it defines **similarity** in the transformed space."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MsAjuPUHa1cr"
      },
      "source": [
        "Thus, we have now:\n",
        "$$min_\\alpha = -\\sum{\\alpha_i} + \\frac{1}{2}\\sum_{i=1}^{n}\\sum_{j=1}^{n}{y^i y^j \\alpha^i \\alpha^j K(<X^i, X^j>) } $$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vuZjR4WEavPy"
      },
      "source": [
        "## Kernel Trick Example"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WM2fr6KibGhs"
      },
      "source": [
        "$$K(x_i,x_j) = (<x_i,x_j> + 1)^p$$, where p is a tunable parameter\n",
        "\n",
        "\n",
        "**Evaluating K only requires ONE ADDITION and ONE EXPONENTATION\n",
        "more than the original dot product**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CYAnnwiebGtK"
      },
      "source": [
        "# Kernel Types:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Bzv6le8bGwF"
      },
      "source": [
        "## Polynomial Kernel:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sb6GRSLpb4i-"
      },
      "source": [
        "Very cool visualization video!\n",
        "https://www.youtube.com/watch?v=3liCbRZPrZA"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tQMKWue4cFSL"
      },
      "source": [
        "$$K(x_i,x_j) = (<x_i,x_j> + b)^d$$\n",
        "where *d* is the degree of the ploynomial and *b* is a free parameter trading off the influence of higher-order versus lower-order terms in the polynomial"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rgvp_IRXcFPE"
      },
      "source": [
        "What if 𝜑(x<sup>i</sup>) degree is really big?\n",
        "\n",
        "\n",
        "For example, if you have 16*16 pixel image and you\n",
        "apply polynomial with degree of 5. this will result in\n",
        "10<sup>10</sup> features!!!\n",
        "\n",
        "**Without the Kernel trick, this would have been impossible!**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WYnVvbsEcFMe"
      },
      "source": [
        "## Gaussian Radial Basis Function Kernel:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jw7jUKROdv9p"
      },
      "source": [
        "Another technique to tackle nonlinear problems is to add features computed using a similarity function\n",
        "\n",
        "\n",
        "Check this very cool video https://www.youtube.com/watch?v=Z2_yh2sice8"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kWMb_44RdwAw"
      },
      "source": [
        "**Similarity function** measures how much each instance resembles a particular landmark."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O6F0zJd2dwDe"
      },
      "source": [
        "$$𝜑(x_i , l )  = exp^{(γ|x^i - l|)}$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B3eImCnpdwGC"
      },
      "source": [
        "Where \n",
        "$$ l$$ is the landmark\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gLJzHz9QfIXf"
      },
      "source": [
        "### Gamma Hyper-parameter"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "etJ_3y8-fIaH"
      },
      "source": [
        "Gamma is a hyperparameter that controls the distance of influence of a single training point.\n",
        "\n",
        "- Low values of gamma indicates a large similarity radius which results in more points being grouped together.\n",
        "\n",
        "- For high values of gamma, the points need to be very close to each other in order to be considered in the same group (or class). \n",
        "\n",
        "- Therefore, models with very large gamma values tend to overfit."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v9nTajaXfIc-"
      },
      "source": [
        "<img src = https://i.ibb.co/d0C8Dh4/8.jpg>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZaGQSe4Mj2xB"
      },
      "source": [
        "### Trade-off between Gamma and C"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tVd6p5QGjrQs"
      },
      "source": [
        "<img src=https://i.ibb.co/xHRWwBj/9.jpg>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cnC2x5GAj9GR"
      },
      "source": [
        "- Smaller gamma value makes the bell shaped curve wider\n",
        "- Instances have a larger range of influence\n",
        "- The decision boundary ends up smoother.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XVelyVsUkHVF"
      },
      "source": [
        "- Bigger gamma makes the bell shaped curve narrower.\n",
        "- Each instane's range of influence is smaller.\n",
        "- The decision boundary ends up being more irregular, wiggling around individual instances."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HW-Lo0ZckY5B"
      },
      "source": [
        "## Sigmoid Kernel:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VOj6K05xkY18"
      },
      "source": [
        "$$ 𝝋(x_i,x_j)= \\tanh(ax_i^𝑇 x_𝑗+𝑐)$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1XPP0p5rkYzY"
      },
      "source": [
        "- Neural networks use sigmoid as activation function\n",
        "\n",
        "\n",
        "- SVM with a sigmoid kernel is equivalent to 2 layer perceptron"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R8-sQ5QKlKlW"
      },
      "source": [
        "## Cosine Similarity kernel\n",
        "$$ 𝝋(𝑥_𝑖,𝑥_𝑗)= \\frac{𝑥_𝑖^𝑇𝑥_𝑖}{||𝑥_𝑖|||𝑥_𝑗||}$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qeDKmoa2lON7"
      },
      "source": [
        "- Popular choice of measuring similarity of text documents\n",
        "- 𝐿2 norm project vectors onto the unit sphere, their dot product is the cosine of the\n",
        "angle between the vectors."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ww4B5Qw2lWcd"
      },
      "source": [
        "## Other Kernel methods:\n",
        "\n",
        "- Chi Square\n",
        "\n",
        "- String \n",
        "\n",
        "- Tree\n",
        "\n",
        "- Graph\n",
        "\n",
        "- Wavelet\n",
        "\n",
        "- and many many more..."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lM3qdFc78L6a"
      },
      "source": [
        "# CODE: SVM with Linear Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EuSFvToL8Tdn"
      },
      "source": [
        "- We are going to work with Iris Dataset\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ylnvmpYc8rqL"
      },
      "source": [
        "## Loading the data\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bs9i1AGl8KyI"
      },
      "source": [
        "iris = datasets.load_iris()\n",
        "X = iris.data[:, [0, 2]]\n",
        "y = iris.target\n",
        "\n",
        "X, y = shuffle_arrays_unison(arrays=[X, y], random_seed=42)\n",
        "\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(X)\n",
        "\n",
        "X = scaler.transform(X)\n",
        "X_train, y_train = X[:100], y[:100]\n",
        "X_test, y_test = X[100:], y[100:]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W3NYwDnk8yKq"
      },
      "source": [
        "## Training a classifier\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RqpS3KfJ8Ykn",
        "outputId": "a21b7ac1-1376-4d94-f296-23294894f418"
      },
      "source": [
        "svm = SVC(C=0.5, kernel='linear')\n",
        "svm.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=0.5, kernel='linear')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 620
        },
        "id": "vlA7rUQm8b_R",
        "outputId": "8dea2743-22df-4a71-cde7-cfddc18919ed"
      },
      "source": [
        "\n",
        "# Plotting decision regions\n",
        "plot_decision_regions(X_train, y_train, clf=svm, legend=2)\n",
        "\n",
        "# Adding axes annotations\n",
        "plt.xlabel('sepal length [cm]')\n",
        "plt.ylabel('petal length [cm]')\n",
        "plt.title('SVM on Iris')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kw2YnVi39q7f"
      },
      "source": [
        "## Trying different combinations:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "YNWXJOXZ9qf5",
        "outputId": "e6efa6e0-af39-47f6-995a-11f266d64c53"
      },
      "source": [
        "gs = gridspec.GridSpec(2, 2)\n",
        "\n",
        "plt.rcParams['figure.figsize'] = (20, 10)\n",
        "\n",
        "clf1 = SVC(C=0.1, kernel='linear')\n",
        "clf2 = SVC(C=10, kernel='linear')\n",
        "clf3 = SVC(C=20, kernel='poly')\n",
        "clf4 = SVC(C=20, kernel='rbf')\n",
        "scatter_highlight_kwargs = {'s': 50, 'label': 'Test data', 'alpha': 0.7, 'linestyle':'-'}\n",
        "\n",
        "labels = ['C = 0.1 , Linear Kernel' , 'C = 10, Linear Kernel', 'C = 20 , Polynomial Kernel'  ,'C =20 , RBF Kernel']\n",
        "for clf, lab, grd in zip([clf1, clf2, clf3, clf4],\n",
        "                         labels,\n",
        "                         itertools.product([0, 1], repeat=2)):\n",
        "\n",
        "    clf.fit(X_train, y_train)\n",
        "    ax = plt.subplot(gs[grd[0], grd[1]])\n",
        "    fig = plot_decision_regions(X=X_train, y=y_train, clf=clf, legend=2, markers='x^sv<>')\n",
        "    plt.title(lab)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HsiDrq1m-8hi"
      },
      "source": [
        "- Increasing C, increases bias, reduces the variance (we become more tolerant of violations to the margin, margin will widen)\n",
        "\n",
        "- Decreasing C, reduces bias, increases variance (we become less tolerant of violations to the margin, margin narrows)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hpUYwT58p3s3"
      },
      "source": [
        "# CODE: SVM with a non-linear Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oo8oz-CNp9oN"
      },
      "source": [
        "We are going to work with a synthatic dataset composed of the XOR gate"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_n7rhYnSqPW1"
      },
      "source": [
        "<img src = https://www.bogotobogo.com/python/scikit-learn/images/svm2/XOR-GATE.png>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ULMQN1Tdqmt0"
      },
      "source": [
        "## Generate the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N9b1uqzylJSB"
      },
      "source": [
        "np.random.seed(0) ## to reproduce the results\n",
        "X_xor = np.random.randn(500, 2) ## Generate 500 itegers\n",
        "y_xor = np.logical_xor(X_xor[:, 0] > 0, X_xor[:,1] > 0, dtype = int) ## Output\n",
        "y_xor = np.where(y_xor, 0 , 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-jUR8_hKJd5z"
      },
      "source": [
        "## Split the dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CDA_5kW_JjrD"
      },
      "source": [
        "\n",
        "X_xor, y_xor = shuffle_arrays_unison(arrays=[X_xor, y_xor], random_seed=42)\n",
        "\n",
        "X_train, y_train = X_xor[:400], y_xor[:400]\n",
        "X_test, y_test = X_xor[400:], y_xor[400:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZT3PrYMjJjHl"
      },
      "source": [
        "## Train Different Kernels"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "w-fYQ27IJisw",
        "outputId": "6dcac3ac-29db-43d9-d689-ec68b26a8a5d"
      },
      "source": [
        "gs = gridspec.GridSpec(2, 2)\n",
        "\n",
        "fig = plt.figure(figsize=(16,16))\n",
        "\n",
        "clf1 = SVC(C=0.1, kernel='linear')\n",
        "clf2 = SVC(C=10, kernel='linear')\n",
        "clf3 = SVC(C=20, kernel='poly')\n",
        "clf4 = SVC(C=20, kernel='rbf')\n",
        "scatter_highlight_kwargs = {'s': 50, 'label': 'Test data', 'alpha': 0.7, 'linestyle':'-'}\n",
        "\n",
        "labels = ['C = 0.1 , Linear Kernel' , 'C = 10, Linear Kernel', 'C = 20 , Polynomial Kernel'  ,'C =20 , RBF Kernel']\n",
        "for clf, lab, grd in zip([clf1, clf2, clf3, clf4],\n",
        "                         labels,\n",
        "                         itertools.product([0, 1], repeat=2)):\n",
        "\n",
        "    clf.fit(X_train, y_train)\n",
        "    ax = plt.subplot(gs[grd[0], grd[1]])\n",
        "    fig = plot_decision_regions(X=X_train, y=y_train, clf=clf, legend=2, markers='x^sv<>')\n",
        "    plt.title(lab)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "C:\\Users\\Fatma Moanes\\anaconda3\\lib\\site-packages\\mlxtend\\plotting\\decision_regions.py:244: UserWarning: No contour levels were found within the data range.\n",
            "  ax.contour(xx, yy, Z, cset.levels,\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x1152 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Vgsx4jQQJdj_",
        "outputId": "b0876527-7d6e-48e6-9c55-3f666592855f"
      },
      "source": [
        "gs = gridspec.GridSpec(2, 2)\n",
        "\n",
        "fig = plt.figure(figsize=(16,16))\n",
        "\n",
        "clf1 = SVC(C=0.5, kernel='rbf', gamma = 0.5)\n",
        "clf2 = SVC(C=5, kernel='rbf', gamma = 5)\n",
        "clf3 = SVC(C=0.5, kernel='rbf', gamma = 5)\n",
        "clf4 = SVC(C=5, kernel='rbf', gamma = 0.5)\n",
        "scatter_highlight_kwargs = {'s': 50, 'label': 'Test data', 'alpha': 0.7, 'linestyle':'-'}\n",
        "\n",
        "labels = ['C = 0.5 , Gamma = 0.5 , RBF Kernel' , 'C = 5, Gamma = 5 , RBF Kernel', 'C = 0.5 , Gamma = 5 , RBF Kernel'  ,'C =5 , Gamma = 0.5, RBF Kernel']\n",
        "for clf, lab, grd in zip([clf1, clf2, clf3, clf4],\n",
        "                         labels,\n",
        "                         itertools.product([0, 1], repeat=2)):\n",
        "\n",
        "    clf.fit(X_train, y_train)\n",
        "    ax = plt.subplot(gs[grd[0], grd[1]])\n",
        "    fig = plot_decision_regions(X=X_train, y=y_train, clf=clf, legend=2, markers='x^sv<>')\n",
        "    plt.title(lab)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x1152 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y07ZiroMM_Su"
      },
      "source": [
        "- Smaller gamma value makes the bell shaped curve wider\n",
        "- Instances have a larger range of influence\n",
        "- The decision boundary ends up smoother."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ptGtig_JR3DH"
      },
      "source": [
        "# Cross Validation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1NezpujVUULG"
      },
      "source": [
        "## Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oDDf_WETUFcY"
      },
      "source": [
        "We are going to use a Speech Recoginition Dataset!\n",
        "\n",
        "We will follow [this](https://www.kaggle.com/nirajvermafcb/support-vector-machine-detail-analysis) Kernel"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CPAFQqAGUQS9"
      },
      "source": [
        "**[Gender Recognition by Voice and Speech Analysis](https://www.kaggle.com/primaryobjects/voicegender)**\n",
        "\n",
        "- This database was created to identify a voice as male or female, based upon acoustic properties of the voice and speech. \n",
        "\n",
        "\n",
        "- The dataset consists of 3,168 recorded voice samples, collected from male and female speakers. \n",
        "\n",
        "\n",
        "- The voice samples are pre-processed by acoustic analysis in R using the seewave and tuneR packages, with an analyzed frequency range of 0hz-280hz"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r3JAKWaQUte-"
      },
      "source": [
        "### Dataset Features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "riPxv5P1UhRY"
      },
      "source": [
        "The following acoustic properties of each voice are measured and included within the CSV:\n",
        "\n",
        "- meanfreq: mean frequency (in kHz)\n",
        "- sd: standard deviation of frequency\n",
        "- median: median frequency (in kHz)\n",
        "- Q25: first quantile (in kHz)\n",
        "- Q75: third quantile (in kHz)\n",
        "- IQR: interquantile range (in kHz)\n",
        "- skew: skewness (see note in specprop description)\n",
        "- kurt: kurtosis (see note in specprop description)\n",
        "- sp.ent: spectral entropy\n",
        "- sfm: spectral flatness\n",
        "- mode: mode frequency\n",
        "- centroid: frequency centroid (see specprop)\n",
        "- peakf: peak frequency (frequency with highest energy)\n",
        "- meanfun: average of fundamental frequency measured across acoustic signal\n",
        "- minfun: minimum fundamental frequency measured across acoustic signal\n",
        "- maxfun: maximum fundamental frequency measured across acoustic signal\n",
        "- meandom: average of dominant frequency measured across acoustic signal\n",
        "- mindom: minimum of dominant frequency measured across acoustic signal\n",
        "- maxdom: maximum of dominant frequency measured across acoustic signal\n",
        "- dfrange: range of dominant frequency measured across acoustic signal\n",
        "- modindx: modulation index. Calculated as the accumulated absolute - difference between adjacent measurements of fundamental frequencies divided by the frequency range\n",
        "\n",
        "\n",
        "- **label: male or female**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lrwIXrD_hiDa"
      },
      "source": [
        "## Load the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8F3FZp-LhiDa",
        "outputId": "193ac9cc-7712-4b89-98b9-16121a4eecac"
      },
      "source": [
        "df = pd.read_csv(\"voice.csv\")\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "[Errno 2] No such file or directory: 'voice.csv'",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[1;32m<ipython-input-10-7f7086d48236>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"voice.csv\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    684\u001b[0m     )\n\u001b[0;32m    685\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 686\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    687\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    688\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    450\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    451\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 452\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    453\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    454\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    944\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    945\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 946\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    947\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    948\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1176\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"c\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1177\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"c\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1178\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1179\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1180\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"python\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   2006\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"usecols\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2007\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2008\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2009\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2010\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[1;34m()\u001b[0m\n",
            "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[1;34m()\u001b[0m\n",
            "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'voice.csv'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "54lh5fbshiDa"
      },
      "source": [
        "## Finding correlations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g1QBJ6tPhiDb"
      },
      "source": [
        "df.corr()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p_dWjrBJhiDb"
      },
      "source": [
        "## Any null values?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oog8Bv32hiDb"
      },
      "source": [
        "print(df.isnull().sum())\n",
        "print(\"Total number of labels: {}\".format(df.shape[0]))\n",
        "print(\"Number of male: {}\".format(df[df.label == 'male'].shape[0]))\n",
        "print(\"Number of female: {}\".format(df[df.label == 'female'].shape[0]))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K5vuJPq_hiDc"
      },
      "source": [
        "## Data Seperating, Encoding, and Standarization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i1MvRD5NhiDc"
      },
      "source": [
        "X=df.iloc[:, :-1]\n",
        "X.head()\n",
        "\n",
        "## Encoding\n",
        "y=df.iloc[:,-1]\n",
        "gender_encoder = LabelEncoder()\n",
        "y = gender_encoder.fit_transform(y)\n",
        "\n",
        "## Standard\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(X)\n",
        "X = scaler.transform(X)\n",
        "\n",
        "## Splitting\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZGlRYyiYhiDc"
      },
      "source": [
        "## Running SVM with default hyperparamters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZiqvtDFKhiDc",
        "scrolled": true
      },
      "source": [
        "svc=SVC() #Default hyperparameters\n",
        "svc.fit(X_train,y_train)\n",
        "y_pred=svc.predict(X_test)\n",
        "print('Accuracy Score for default parameters:')\n",
        "print(metrics.accuracy_score(y_test,y_pred))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZY39O-A_hiDc"
      },
      "source": [
        "## Running SVM with different Kernels but with their default hyperparameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GHjWg-BZhiDc"
      },
      "source": [
        "svc=SVC(kernel='linear')\n",
        "svc.fit(X_train,y_train)\n",
        "y_pred=svc.predict(X_test)\n",
        "print('Accuracy Score Linear:')\n",
        "print(metrics.accuracy_score(y_test,y_pred))\n",
        "svc=SVC(kernel='rbf')\n",
        "svc.fit(X_train,y_train)\n",
        "y_pred=svc.predict(X_test)\n",
        "print('Accuracy Score RBF:')\n",
        "print(metrics.accuracy_score(y_test,y_pred))\n",
        "svc=SVC(kernel='poly')\n",
        "svc.fit(X_train,y_train)\n",
        "y_pred=svc.predict(X_test)\n",
        "print('Accuracy Score Polynomial:') #Polynomial kernel is performing poorly.The reason behind this maybe it is overfitting the training dataset\n",
        "print(metrics.accuracy_score(y_test,y_pred))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k918liCThiDd"
      },
      "source": [
        "**From the above analysis, we will investigate different hyperparameters for Linear and RBF kernels only, since Polynomial performance is much worse.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6DEhTRpkhiDd"
      },
      "source": [
        "## CV with Linear Kernel"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SoQONDBkhiDd"
      },
      "source": [
        "svc=SVC(kernel='linear')\n",
        "scores = cross_val_score(svc, X, y, cv=10, scoring='accuracy') #cv is cross validation\n",
        "print(scores)\n",
        "print(scores.mean())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nve0DR3LhiDd"
      },
      "source": [
        "## CV with RBF Kernel"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0quNnOsphiDe"
      },
      "source": [
        "svc=SVC(kernel='rbf')\n",
        "scores = cross_val_score(svc, X, y, cv=10, scoring='accuracy') #cv is cross validation\n",
        "print(scores)\n",
        "print(scores.mean())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0FMC8A8dhiDe"
      },
      "source": [
        "## Check the C hyperparameter in depth for the Linear Kernel"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mYoF8bbihiDe"
      },
      "source": [
        "C_range=list(range(1,26))\n",
        "acc_score=[]\n",
        "for c in C_range:\n",
        "    svc = SVC(kernel='linear', C=c)\n",
        "    scores = cross_val_score(svc, X, y, cv=10, scoring='accuracy')\n",
        "    acc_score.append(scores.mean())\n",
        "print(acc_score)    \n",
        "# plot the value of C for SVM (x-axis) versus the cross-validated accuracy (y-axis)\n",
        "plt.plot(C_range,acc_score)\n",
        "plt.xticks(np.arange(0,27,2))\n",
        "plt.xlabel('Value of C for SVC')\n",
        "plt.ylabel('Cross-Validated Accuracy')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vLLsYKFbhiDf"
      },
      "source": [
        "**From the above plot we can see that accuracy has been close to 97% for C=1 and C=6 and then it drops around 96.8% and remains constant.**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T2jXTS3bhiDf"
      },
      "source": [
        "C_range=list(np.arange(0.1,6,0.1))\n",
        "acc_score=[]\n",
        "for c in C_range:\n",
        "    svc = SVC(kernel='linear', C=c)\n",
        "    scores = cross_val_score(svc, X, y, cv=10, scoring='accuracy')\n",
        "    acc_score.append(scores.mean())\n",
        "print(acc_score)   \n",
        "# plot the value of C for SVM (x-axis) versus the cross-validated accuracy (y-axis)\n",
        "plt.plot(C_range,acc_score)\n",
        "plt.xticks(np.arange(0.0,6,0.3))\n",
        "plt.xlabel('Value of C for SVC ')\n",
        "plt.ylabel('Cross-Validated Accuracy')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c9qN7EsehiDf"
      },
      "source": [
        "## Check The Gamma hyperparameter in RBF Kernel"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p7UVlPPAhiDf"
      },
      "source": [
        "gamma_range=[0.0001,0.001,0.01,0.1,1,10,100]\n",
        "acc_score=[]\n",
        "for g in gamma_range:\n",
        "    svc = SVC(kernel='rbf', gamma=g)\n",
        "    scores = cross_val_score(svc, X, y, cv=10, scoring='accuracy')\n",
        "    acc_score.append(scores.mean())\n",
        "print(acc_score)    \n",
        "\n",
        "plt.plot(gamma_range,acc_score)\n",
        "plt.xlabel('Value of gamma for SVC ')\n",
        "plt.xticks(np.arange(0.0001,100,5))\n",
        "plt.ylabel('Cross-Validated Accuracy')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xgMxXc3UhiDg"
      },
      "source": [
        "**We can see that for gamma=10 and 100 the kernel is performing poorly.We can also see a slight dip in accuracy score when gamma is 1.Let us look into more details for the range 0.0001 to 0.1.**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HTVgK-o1hiDh"
      },
      "source": [
        "gamma_range=[0.0001,0.001,0.01,0.1]\n",
        "acc_score=[]\n",
        "for g in gamma_range:\n",
        "    svc = SVC(kernel='rbf', gamma=g)\n",
        "    scores = cross_val_score(svc, X, y, cv=10, scoring='accuracy')\n",
        "    acc_score.append(scores.mean())\n",
        "print(acc_score)    \n",
        "# plot the value of C for SVM (x-axis) versus the cross-validated accuracy (y-axis)\n",
        "plt.plot(gamma_range,acc_score)\n",
        "plt.xlabel('Value of gamma for SVC ')\n",
        "plt.ylabel('Cross-Validated Accuracy')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8uiZ7fxphiDh"
      },
      "source": [
        "**The score increases steadily and raches its peak at 0.01 and then decreases till gamma=1.Thus Gamma should be around 0.01.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Gv34PmDhiDh"
      },
      "source": [
        "gamma_range=[0.01,0.02,0.03,0.04,0.05]\n",
        "acc_score=[]\n",
        "for g in gamma_range:\n",
        "    svc = SVC(kernel='rbf', gamma=g)\n",
        "    scores = cross_val_score(svc, X, y, cv=10, scoring='accuracy')\n",
        "    acc_score.append(scores.mean())\n",
        "print(acc_score)    \n",
        "gamma_range=[0.01,0.02,0.03,0.04,0.05]\n",
        "\n",
        "# plot the value of C for SVM (x-axis) versus the cross-validated accuracy (y-axis)\n",
        "plt.plot(gamma_range,acc_score)\n",
        "plt.xlabel('Value of gamma for SVC ')\n",
        "plt.ylabel('Cross-Validated Accuracy')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ApFP-zj_hiDi"
      },
      "source": [
        "## Grid Search"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m_U9zOAihiDi"
      },
      "source": [
        "tuned_parameters = {\n",
        "    'C':  (np.arange(0.1,1,0.1)),\n",
        "        'gamma':[0.01,0.02,0.03,0.04,0.05],\n",
        "    \n",
        "     \"kernel\":[ 'rbf', 'linear']\n",
        "\n",
        "}\n",
        "svm_model= SVC()\n",
        "\n",
        "model_svm = GridSearchCV(svm_model, tuned_parameters,cv=10,scoring='accuracy')\n",
        "model_svm.fit(X_train, y_train)\n",
        "print(model_svm.best_score_)\n",
        "print(model_svm.best_score_)\n",
        "print(model_svm.best_params_)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "horquEEVhiDi"
      },
      "source": [
        "y_pred= model_svm.predict(X_test)\n",
        "print(metrics.accuracy_score(y_pred,y_test))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wn_ajifDR38Y"
      },
      "source": [
        "# Multi-class SVM"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KrMPHqUMR7IW"
      },
      "source": [
        "SVM only considers two classes\n",
        "\n",
        "For m-class classification problem:\n",
        "- SVM 1 learns “Output==1” vs “Output != 1”\n",
        "- SVM 2 learns “Output==2” vs “Output != 2”\n",
        "\n",
        "- SVM m learns “Output==m” vs “Output != m”\n",
        "\n",
        "\n",
        " To predict the output for a new input, just predict with each SVM\n",
        "and find out which one puts the prediction the furthest into the\n",
        "positive region"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bARt2jLmhiDj"
      },
      "source": [
        "# SVM for Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eDgKABguhiDj"
      },
      "source": [
        "We can use SVMs for regression instead of classification.\n",
        "\n",
        "- the trick is to reverse the objective\n",
        "\n",
        "\n",
        "- instead of trying to fit the largest possible margin between two classes while limiting margin violations.\n",
        "\n",
        "\n",
        "- SVM Regression tries to fit as many instances as possible on the street while limiting margin violations (i.e., instances off the street)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "14ri0f3lhiDj"
      },
      "source": [
        "<img src=https://i.ibb.co/v1X2V9P/10.jpg>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wfQCkURnR6_Q"
      },
      "source": [
        "# Resources:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Ax61AxdO32G"
      },
      "source": [
        "This lab took a lot of time and resources to prepare. \n",
        "Take a look at the resourcses below. They are super helpful!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FVaRn5qsPD63"
      },
      "source": [
        "http://rasbt.github.io/mlxtend/user_guide/plotting/plot_decision_regions\n",
        "\n",
        "\n",
        "https://www.bogotobogo.com/python/scikit-learn/scikit_machine_learning_Support_Vector_Machines_SVM_2.php\n",
        "\n",
        "\n",
        "https://towardsdatascience.com/understanding-the-kernel-trick-e0bc6112ef78\n",
        "\n",
        "\n",
        "https://www.datasciencecentral.com/profiles/blogs/implementing-a-soft-margin-kernelized-support-vector-machine\n",
        "https://towardsdatascience.com/support-vector-machines-svm-c9ef22815589\n",
        "\n",
        "https://www.cs.umd.edu/~samir/498/SVM.pdf\n",
        "\n",
        "https://people.csail.mit.edu/dsontag/courses/ml14/slides/lecture2.pdf\n",
        "\n",
        "https://www.vebuso.com/2020/02/a-top-machine-learning-algorithm-explained-support-vector-machines-svms/\n",
        "\n",
        "\n",
        "https://www.analyticsvidhya.com/blog/2017/09/understaing-support-vector-machine-example-code/\n",
        "\n",
        "https://towardsdatascience.com/support-vector-machine-simply-explained-fee28eba5496\n",
        "\n",
        "\n",
        "https://towardsdatascience.com/support-vector-machines-svm-c9ef22815589\n",
        "\n",
        "http://web.mit.edu/6.034/wwwbob/svm.pdf\n",
        "\n",
        "Hands on machine learning with scikit learn and tensorflow by Aurélien Géron , Chapter\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Q2pn11GSAFy"
      },
      "source": [
        "# Tasks"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6l4nx-wnhiDm"
      },
      "source": [
        "- Task 1  [7 Points]: Work with [Amazon Fine Food Reviews dataset](https://www.kaggle.com/snap/amazon-fine-food-reviews)\n",
        "    - Perform proper preprocessing and visualization\n",
        "    - Perform proper hyperparameter tuning **-SVM-**. JUSTIFY ALL THE HYPERPARAMETER VALUES\n",
        "    - **Hint**: Check [this](https://towardsdatascience.com/support-vector-machines-svm-c9ef22815589) tutorial and this [kernel](https://www.kaggle.com/laowingkin/amazon-fine-food-review-sentiment-analysis) for help "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hjh9mnP4zlMo"
      },
      "source": [
        "## Reading the Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "id": "Yi7ME0fvjHzu",
        "outputId": "bc0ec742-e3d6-4b74-afa7-67e65845aed3"
      },
      "source": [
        "! pip install -q kaggle\n",
        "\n",
        "files.upload() # Browse for the kaggle.json file that you downloaded\n",
        "\n",
        "# Make directory named kaggle, copy kaggle.json file there, and change the permissions of the file.\n",
        "! mkdir ~/.kaggle\n",
        "! cp kaggle.json ~/.kaggle/\n",
        "! chmod 600 ~/.kaggle/kaggle.json\n",
        "\n",
        "# You can check if everything's okay by running this command.\n",
        "! kaggle datasets list\n",
        "\n",
        "# Download and unzip sign-language-mnist dataset into '/usr/local'\n",
        "! kaggle datasets download -d snap/amazon-fine-food-reviews "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-d59d8b73-5b92-40e9-8ba5-10c8419b4806\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-d59d8b73-5b92-40e9-8ba5-10c8419b4806\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle (1).json\n",
            "mkdir: cannot create directory ‘/root/.kaggle’: File exists\n",
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.10 / client 1.5.4)\n",
            "ref                                                            title                                               size  lastUpdated          downloadCount  \n",
            "-------------------------------------------------------------  -------------------------------------------------  -----  -------------------  -------------  \n",
            "arashnic/hr-analytics-job-change-of-data-scientists            HR Analytics: Job Change of Data Scientists        295KB  2020-12-07 00:25:10           1192  \n",
            "babyoda/access-to-computers-from-home-oecd                     Access to Computers From Home OECD                   3KB  2020-12-04 10:50:09            100  \n",
            "shashwatwork/impact-of-covid19-pandemic-on-the-global-economy  Impact of Covid-19 Pandemic on the Global Economy    1MB  2020-11-29 14:16:30            602  \n",
            "emmanuelleai/world-marathons-majors                            World Marathons Majors                               7KB  2020-12-06 19:24:28            118  \n",
            "mrmorj/dataset-of-songs-in-spotify                             Dataset of songs in Spotify                          3MB  2020-12-06 09:46:55            282  \n",
            "boramalper/scihub-papers                                       Sci-Hub Papers                                       6GB  2020-12-06 04:10:54              8  \n",
            "juicobowley/drake-lyrics                                       Drake Lyrics                                       764KB  2020-11-27 01:00:52             59  \n",
            "kavisekar/oecd-unemployment-rate-2020                          OECD Unemployment rate (2020)                        3KB  2020-12-07 15:37:38            108  \n",
            "kingabzpro/datascience-survey-on-kaggle                        data science survey on Kaggle                      129KB  2020-11-24 19:04:19             53  \n",
            "sakshigoyal7/credit-card-customers                             Credit Card customers                              379KB  2020-11-19 07:38:44          12286  \n",
            "szymonjanowski/internet-articles-data-with-users-engagement    Internet news data with readers engagement           3MB  2020-11-21 17:09:57           3459  \n",
            "sootersaalu/amazon-top-50-bestselling-books-2009-2019          Amazon Top 50 Bestselling Books 2009 - 2019         15KB  2020-10-13 09:39:21          10621  \n",
            "alexgude/california-traffic-collision-data-from-switrs         California Traffic Collision Data from SWITRS        1GB  2020-11-22 16:51:55           2136  \n",
            "babyoda/women-entrepreneurship-and-labor-force                 Women Entrepreneurship and Labor Force               1KB  2020-11-21 08:38:51           5110  \n",
            "shivamb/netflix-shows                                          Netflix Movies and TV Shows                        971KB  2020-01-20 07:33:56          76854  \n",
            "yamaerenay/spotify-dataset-19212020-160k-tracks                Spotify Dataset 1921-2020, 160k+ Tracks             16MB  2020-11-25 21:14:12          13081  \n",
            "patrickb1912/ipl-complete-dataset-20082020                     IPL Complete Dataset (2008-2020)                     1MB  2020-11-23 06:53:37           2719  \n",
            "manchunhui/us-election-2020-tweets                             US Election 2020 Tweets                            353MB  2020-11-09 18:51:59           5435  \n",
            "imoore/2020-us-general-election-turnout-rates                  2020 US General Election Turnout rates               4KB  2020-11-26 00:21:15           3610  \n",
            "google/tinyquickdraw                                           QuickDraw Sketches                                  11GB  2018-04-18 19:38:04           2653  \n",
            "amazon-fine-food-reviews.zip: Skipping, found more recently modified local copy (use --force to force download)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z30knzuvxN63",
        "outputId": "559619b6-ef7f-4d0b-bb6d-270b4a26f26d"
      },
      "source": [
        "! mkdir train\n",
        "! unzip amazon-fine-food-reviews.zip -d train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mkdir: cannot create directory ‘train’: File exists\n",
            "Archive:  amazon-fine-food-reviews.zip\n",
            "replace train/Reviews.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: no\n",
            "replace train/database.sqlite? [y]es, [n]o, [A]ll, [N]one, [r]ename: no\n",
            "replace train/hashes.txt? [y]es, [n]o, [A]ll, [N]one, [r]ename: no\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WL8BCyPMoz9G"
      },
      "source": [
        "df = pd.read_csv('train/Reviews.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "deE9yUECjNyb"
      },
      "source": [
        "## Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "id": "izqUtbwhjLEl",
        "outputId": "fa0f1429-bc75-4402-8c7f-552c33489554"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>ProductId</th>\n",
              "      <th>UserId</th>\n",
              "      <th>ProfileName</th>\n",
              "      <th>HelpfulnessNumerator</th>\n",
              "      <th>HelpfulnessDenominator</th>\n",
              "      <th>Score</th>\n",
              "      <th>Time</th>\n",
              "      <th>Summary</th>\n",
              "      <th>Text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>B001E4KFG0</td>\n",
              "      <td>A3SGXH7AUHU8GW</td>\n",
              "      <td>delmartian</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1303862400</td>\n",
              "      <td>Good Quality Dog Food</td>\n",
              "      <td>I have bought several of the Vitality canned d...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>B00813GRG4</td>\n",
              "      <td>A1D87F6ZCVE5NK</td>\n",
              "      <td>dll pa</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1346976000</td>\n",
              "      <td>Not as Advertised</td>\n",
              "      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>B000LQOCH0</td>\n",
              "      <td>ABXLMWJIXXAIN</td>\n",
              "      <td>Natalia Corres \"Natalia Corres\"</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1219017600</td>\n",
              "      <td>\"Delight\" says it all</td>\n",
              "      <td>This is a confection that has been around a fe...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>B000UA0QIQ</td>\n",
              "      <td>A395BORC6FGVXV</td>\n",
              "      <td>Karl</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1307923200</td>\n",
              "      <td>Cough Medicine</td>\n",
              "      <td>If you are looking for the secret ingredient i...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>B006K2ZZ7K</td>\n",
              "      <td>A1UQRSCLF8GW1T</td>\n",
              "      <td>Michael D. Bigham \"M. Wassir\"</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1350777600</td>\n",
              "      <td>Great taffy</td>\n",
              "      <td>Great taffy at a great price.  There was a wid...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Id  ...                                               Text\n",
              "0   1  ...  I have bought several of the Vitality canned d...\n",
              "1   2  ...  Product arrived labeled as Jumbo Salted Peanut...\n",
              "2   3  ...  This is a confection that has been around a fe...\n",
              "3   4  ...  If you are looking for the secret ingredient i...\n",
              "4   5  ...  Great taffy at a great price.  There was a wid...\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iC9KIUWzyeVr",
        "outputId": "585fa01c-20a3-445b-bfa9-557cc19ec64d"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 568454 entries, 0 to 568453\n",
            "Data columns (total 10 columns):\n",
            " #   Column                  Non-Null Count   Dtype \n",
            "---  ------                  --------------   ----- \n",
            " 0   Id                      568454 non-null  int64 \n",
            " 1   ProductId               568454 non-null  object\n",
            " 2   UserId                  568454 non-null  object\n",
            " 3   ProfileName             568438 non-null  object\n",
            " 4   HelpfulnessNumerator    568454 non-null  int64 \n",
            " 5   HelpfulnessDenominator  568454 non-null  int64 \n",
            " 6   Score                   568454 non-null  int64 \n",
            " 7   Time                    568454 non-null  int64 \n",
            " 8   Summary                 568427 non-null  object\n",
            " 9   Text                    568454 non-null  object\n",
            "dtypes: int64(5), object(5)\n",
            "memory usage: 43.4+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "BPW03Eb6dcD3",
        "outputId": "345b3749-70a7-442d-ba3c-850086141235"
      },
      "source": [
        "df.describe(percentiles=[0.25,0.5,0.75,0.8,0.85,0.9,0.95])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>HelpfulnessNumerator</th>\n",
              "      <th>HelpfulnessDenominator</th>\n",
              "      <th>Score</th>\n",
              "      <th>Time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>568454.000000</td>\n",
              "      <td>568454.000000</td>\n",
              "      <td>568454.00000</td>\n",
              "      <td>568454.000000</td>\n",
              "      <td>5.684540e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>284227.500000</td>\n",
              "      <td>1.743817</td>\n",
              "      <td>2.22881</td>\n",
              "      <td>4.183199</td>\n",
              "      <td>1.296257e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>164098.679298</td>\n",
              "      <td>7.636513</td>\n",
              "      <td>8.28974</td>\n",
              "      <td>1.310436</td>\n",
              "      <td>4.804331e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>9.393408e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>142114.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.271290e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>284227.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.311120e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>426340.750000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.00000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.332720e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80%</th>\n",
              "      <td>454763.400000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.00000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.336608e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85%</th>\n",
              "      <td>483186.050000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.00000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.340582e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90%</th>\n",
              "      <td>511608.700000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.00000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.344470e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95%</th>\n",
              "      <td>540031.350000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>9.00000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.348013e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>568454.000000</td>\n",
              "      <td>866.000000</td>\n",
              "      <td>923.00000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.351210e+09</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Id  HelpfulnessNumerator  ...          Score          Time\n",
              "count  568454.000000         568454.000000  ...  568454.000000  5.684540e+05\n",
              "mean   284227.500000              1.743817  ...       4.183199  1.296257e+09\n",
              "std    164098.679298              7.636513  ...       1.310436  4.804331e+07\n",
              "min         1.000000              0.000000  ...       1.000000  9.393408e+08\n",
              "25%    142114.250000              0.000000  ...       4.000000  1.271290e+09\n",
              "50%    284227.500000              0.000000  ...       5.000000  1.311120e+09\n",
              "75%    426340.750000              2.000000  ...       5.000000  1.332720e+09\n",
              "80%    454763.400000              2.000000  ...       5.000000  1.336608e+09\n",
              "85%    483186.050000              3.000000  ...       5.000000  1.340582e+09\n",
              "90%    511608.700000              4.000000  ...       5.000000  1.344470e+09\n",
              "95%    540031.350000              7.000000  ...       5.000000  1.348013e+09\n",
              "max    568454.000000            866.000000  ...       5.000000  1.351210e+09\n",
              "\n",
              "[12 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ct7XY3mKdcD3",
        "outputId": "4fa48b30-d3fd-49a2-daff-f6b365e17dda"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id                         0\n",
              "ProductId                  0\n",
              "UserId                     0\n",
              "ProfileName               16\n",
              "HelpfulnessNumerator       0\n",
              "HelpfulnessDenominator     0\n",
              "Score                      0\n",
              "Time                       0\n",
              "Summary                   27\n",
              "Text                       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rh8wWAvydcD4",
        "outputId": "630c919e-1c08-4fec-81e3-711333cb714c"
      },
      "source": [
        "df.duplicated().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qkPGtlORdcD6",
        "outputId": "73b697c6-2438-4ec7-a56b-595d31e5d882"
      },
      "source": [
        "df['ProfileName'].duplicated().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "350037"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bNZ3xmcEdcD6",
        "outputId": "d0afb2c2-faab-40c0-cbe4-8c24404e9b39"
      },
      "source": [
        "df['ProductId'].duplicated().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "494196"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "trNOUjYvdcD7",
        "outputId": "93b5f31c-92a1-45df-eca5-89767e790350"
      },
      "source": [
        "df_text_dupl = df[df['Text'].duplicated()]\n",
        "df_text_dupl.head(20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>HelpfulnessDenominator</th>\n",
              "      <th>Score</th>\n",
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              "      <th>Summary</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>30</td>\n",
              "      <td>B0001PB9FY</td>\n",
              "      <td>A3HDKO7OW0QNK4</td>\n",
              "      <td>Canadian Fan</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1107820800</td>\n",
              "      <td>The Best Hot Sauce in the World</td>\n",
              "      <td>I don't know if it's the cactus or the tequila...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>574</th>\n",
              "      <td>575</td>\n",
              "      <td>B000G6RYNE</td>\n",
              "      <td>A3PJZ8TU8FDQ1K</td>\n",
              "      <td>Jared Castle</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>1231718400</td>\n",
              "      <td>One bite and you'll become a \"chippoisseur\"</td>\n",
              "      <td>I'm addicted to salty and tangy flavors, so wh...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>603</th>\n",
              "      <td>604</td>\n",
              "      <td>B000G6RYNE</td>\n",
              "      <td>A3PJZ8TU8FDQ1K</td>\n",
              "      <td>Jared Castle</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1229385600</td>\n",
              "      <td>One bite and you'll become a \"chippoisseur\"</td>\n",
              "      <td>I'm addicted to salty and tangy flavors, so wh...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1973</th>\n",
              "      <td>1974</td>\n",
              "      <td>B0017165OG</td>\n",
              "      <td>A2EPNS38TTLZYN</td>\n",
              "      <td>tedebear</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1312675200</td>\n",
              "      <td>Pok Chops</td>\n",
              "      <td>The pork chops from Omaha Steaks were very tas...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2309</th>\n",
              "      <td>2310</td>\n",
              "      <td>B0001VWE0M</td>\n",
              "      <td>AQM74O8Z4FMS0</td>\n",
              "      <td>Sunshine</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1127606400</td>\n",
              "      <td>Below standard</td>\n",
              "      <td>Too much of the white pith on this orange peel...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2323</th>\n",
              "      <td>2324</td>\n",
              "      <td>B0001VWE0C</td>\n",
              "      <td>AQM74O8Z4FMS0</td>\n",
              "      <td>Sunshine</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1127606400</td>\n",
              "      <td>Below standard</td>\n",
              "      <td>Too much of the white pith on this orange peel...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2336</th>\n",
              "      <td>2337</td>\n",
              "      <td>B0001FQVCK</td>\n",
              "      <td>A5D06XJHDXK75</td>\n",
              "      <td>C. Po</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1190592000</td>\n",
              "      <td>Baci's are pure heaven - great gift, stocking ...</td>\n",
              "      <td>My family has been in love with Baci's ever si...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2647</th>\n",
              "      <td>2648</td>\n",
              "      <td>B0016FY6H6</td>\n",
              "      <td>A2NLZ3M0OJV9NX</td>\n",
              "      <td>Mark Bodzin</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1313107200</td>\n",
              "      <td>way too weak for my tastes</td>\n",
              "      <td>What can i say, I love iced tea. I drink a lot...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2653</th>\n",
              "      <td>2654</td>\n",
              "      <td>B0016FY6H6</td>\n",
              "      <td>A3I4PCBRENJNG2</td>\n",
              "      <td>L. Cain</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1300579200</td>\n",
              "      <td>quality lightly sweetened green tea powder</td>\n",
              "      <td>lots of uses for this green tea...&lt;br /&gt;&lt;br /&gt;...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2946</th>\n",
              "      <td>2947</td>\n",
              "      <td>B0002TJAZK</td>\n",
              "      <td>A2ISKAWUPGGOLZ</td>\n",
              "      <td>M. S. Handley</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1310774400</td>\n",
              "      <td>Kitty Junk Food</td>\n",
              "      <td>We have five cats - one an elderly cat of 15 y...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2947</th>\n",
              "      <td>2948</td>\n",
              "      <td>B0002TJAZK</td>\n",
              "      <td>A3TVZM3ZIXG8YW</td>\n",
              "      <td>christopher hayes</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1291420800</td>\n",
              "      <td>Filler food is empty, leaves your cat always n...</td>\n",
              "      <td>This review will make me sound really stupid, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3885</th>\n",
              "      <td>3886</td>\n",
              "      <td>B005GX7GVW</td>\n",
              "      <td>AS1FCKNKY95ID</td>\n",
              "      <td>Juli A. Lee \"JingleJL\"</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1336953600</td>\n",
              "      <td>Great and good price!</td>\n",
              "      <td>I love these noodles.  They are really great f...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3886</th>\n",
              "      <td>3887</td>\n",
              "      <td>B005GX7GVW</td>\n",
              "      <td>A1I34N9LFOSCX7</td>\n",
              "      <td>Smeggy</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1349136000</td>\n",
              "      <td>Yum!</td>\n",
              "      <td>This soup cooks up quickly and is very yummy! ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4640</th>\n",
              "      <td>4641</td>\n",
              "      <td>B0002NYO9I</td>\n",
              "      <td>A5DVX3B075B09</td>\n",
              "      <td>Patricia Kays</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1338940800</td>\n",
              "      <td>LOVELY JUNIPER BERRIES</td>\n",
              "      <td>Dried berries, still with texture and the quin...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4641</th>\n",
              "      <td>4642</td>\n",
              "      <td>B0002NYO9I</td>\n",
              "      <td>A376TWN7I4HMZ8</td>\n",
              "      <td>helios</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1324252800</td>\n",
              "      <td>Exaclty what i ordered</td>\n",
              "      <td>Again, exactly what I ordered. No fuss, no mus...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4642</th>\n",
              "      <td>4643</td>\n",
              "      <td>B0002NYO9I</td>\n",
              "      <td>A29CO2S0VTT0C5</td>\n",
              "      <td>S. Choo</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1326758400</td>\n",
              "      <td>get a new package</td>\n",
              "      <td>Berries are find, but the package is bad. Unab...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5397</th>\n",
              "      <td>5398</td>\n",
              "      <td>B00622CYVS</td>\n",
              "      <td>ATIHDHZYNQ0EI</td>\n",
              "      <td>Kristen O'donnell \"twinsmom\"</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1270425600</td>\n",
              "      <td>Organic and Tasty</td>\n",
              "      <td>I'd continue to buy but I'm moving over to mor...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5451</th>\n",
              "      <td>5452</td>\n",
              "      <td>B00622CYVI</td>\n",
              "      <td>ATIHDHZYNQ0EI</td>\n",
              "      <td>Kristen O'donnell \"twinsmom\"</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>1270425600</td>\n",
              "      <td>Organic and Tasty</td>\n",
              "      <td>I'd continue to buy but I'm moving over to mor...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5463</th>\n",
              "      <td>5464</td>\n",
              "      <td>B00622CYVI</td>\n",
              "      <td>A2ETT9SL9AN39O</td>\n",
              "      <td>Danielle L Bostic</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1333324800</td>\n",
              "      <td>Great Product</td>\n",
              "      <td>My one year old loves this product. She eats o...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5476</th>\n",
              "      <td>5477</td>\n",
              "      <td>B00622CYVI</td>\n",
              "      <td>A2MF0C4E7GYCI</td>\n",
              "      <td>VW \"VW\"</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1298678400</td>\n",
              "      <td>My child loves this food!</td>\n",
              "      <td>Great value if you buy it as a subscribe and s...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Id  ...                                               Text\n",
              "29      30  ...  I don't know if it's the cactus or the tequila...\n",
              "574    575  ...  I'm addicted to salty and tangy flavors, so wh...\n",
              "603    604  ...  I'm addicted to salty and tangy flavors, so wh...\n",
              "1973  1974  ...  The pork chops from Omaha Steaks were very tas...\n",
              "2309  2310  ...  Too much of the white pith on this orange peel...\n",
              "2323  2324  ...  Too much of the white pith on this orange peel...\n",
              "2336  2337  ...  My family has been in love with Baci's ever si...\n",
              "2647  2648  ...  What can i say, I love iced tea. I drink a lot...\n",
              "2653  2654  ...  lots of uses for this green tea...<br /><br />...\n",
              "2946  2947  ...  We have five cats - one an elderly cat of 15 y...\n",
              "2947  2948  ...  This review will make me sound really stupid, ...\n",
              "3885  3886  ...  I love these noodles.  They are really great f...\n",
              "3886  3887  ...  This soup cooks up quickly and is very yummy! ...\n",
              "4640  4641  ...  Dried berries, still with texture and the quin...\n",
              "4641  4642  ...  Again, exactly what I ordered. No fuss, no mus...\n",
              "4642  4643  ...  Berries are find, but the package is bad. Unab...\n",
              "5397  5398  ...  I'd continue to buy but I'm moving over to mor...\n",
              "5451  5452  ...  I'd continue to buy but I'm moving over to mor...\n",
              "5463  5464  ...  My one year old loves this product. She eats o...\n",
              "5476  5477  ...  Great value if you buy it as a subscribe and s...\n",
              "\n",
              "[20 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "MIXNxG69dcD7",
        "outputId": "7f544c4b-427f-4490-b565-64fce3802d7e"
      },
      "source": [
        "df_text_dupl.sort_values('Summary').head(20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>ProductId</th>\n",
              "      <th>UserId</th>\n",
              "      <th>ProfileName</th>\n",
              "      <th>HelpfulnessNumerator</th>\n",
              "      <th>HelpfulnessDenominator</th>\n",
              "      <th>Score</th>\n",
              "      <th>Time</th>\n",
              "      <th>Summary</th>\n",
              "      <th>Text</th>\n",
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              "  </thead>\n",
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              "      <td>! Mmmmmmmmmm !</td>\n",
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              "      <td>! Mmmmmmmmmm !</td>\n",
              "      <td>What a wonderful surprise to find LUCY's on Am...</td>\n",
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              "      <td>C. Clinger \"CatMa\"</td>\n",
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              "      <td>5</td>\n",
              "      <td>1240012800</td>\n",
              "      <td>! Mmmmmmmmmm !</td>\n",
              "      <td>What a wonderful surprise to find LUCY's on Am...</td>\n",
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              "    <tr>\n",
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              "      <td>5</td>\n",
              "      <td>1240012800</td>\n",
              "      <td>! Mmmmmmmmmm !</td>\n",
              "      <td>What a wonderful surprise to find LUCY's on Am...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>288541</th>\n",
              "      <td>288542</td>\n",
              "      <td>B000ENUC3S</td>\n",
              "      <td>APWCOAVILK94B</td>\n",
              "      <td>Real Named Person \"wowzee\"</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>1289952000</td>\n",
              "      <td>!!!!!!! Flavor review !!!!!!!!</td>\n",
              "      <td>Best Flavor yet and instant favorite, cant sto...</td>\n",
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              "      <td>!!!!!!! Flavor review !!!!!!!!</td>\n",
              "      <td>Best Flavor yet and instant favorite, cant sto...</td>\n",
              "    </tr>\n",
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              "      <td>98649</td>\n",
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              "      <td>Real Named Person \"wowzee\"</td>\n",
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              "      <td>!!!!!!! Flavor review !!!!!!!!</td>\n",
              "      <td>Best Flavor yet and instant favorite, cant sto...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>542833</th>\n",
              "      <td>542834</td>\n",
              "      <td>B000GB0TMW</td>\n",
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              "      <td>Michael</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1349568000</td>\n",
              "      <td>!st time matcha buyer</td>\n",
              "      <td>This is quality matcha tea at a very low price...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>324586</th>\n",
              "      <td>324587</td>\n",
              "      <td>B000GB0UGC</td>\n",
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              "      <td>Michael</td>\n",
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              "      <td>5</td>\n",
              "      <td>1349568000</td>\n",
              "      <td>!st time matcha buyer</td>\n",
              "      <td>This is quality matcha tea at a very low price...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>411645</td>\n",
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              "      <td>Michael</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1349568000</td>\n",
              "      <td>!st time matcha buyer</td>\n",
              "      <td>This is quality matcha tea at a very low price...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>356390</th>\n",
              "      <td>356391</td>\n",
              "      <td>B003SSWCJI</td>\n",
              "      <td>AQ73BMYWH1EM8</td>\n",
              "      <td>Peter D. Mccorkle</td>\n",
              "      <td>39</td>\n",
              "      <td>47</td>\n",
              "      <td>5</td>\n",
              "      <td>1242518400</td>\n",
              "      <td>\" A MIRACLE SUPPLEMENT!!\"</td>\n",
              "      <td>I'm a 32 year old man. Construction Electricia...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>257922</th>\n",
              "      <td>257923</td>\n",
              "      <td>B00125ZCWI</td>\n",
              "      <td>AQ73BMYWH1EM8</td>\n",
              "      <td>Peter D. Mccorkle</td>\n",
              "      <td>39</td>\n",
              "      <td>47</td>\n",
              "      <td>5</td>\n",
              "      <td>1242518400</td>\n",
              "      <td>\" A MIRACLE SUPPLEMENT!!\"</td>\n",
              "      <td>I'm a 32 year old man. Construction Electricia...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>360277</th>\n",
              "      <td>360278</td>\n",
              "      <td>B000WVYB8Y</td>\n",
              "      <td>AQ73BMYWH1EM8</td>\n",
              "      <td>Peter D. Mccorkle</td>\n",
              "      <td>39</td>\n",
              "      <td>47</td>\n",
              "      <td>5</td>\n",
              "      <td>1242518400</td>\n",
              "      <td>\" A MIRACLE SUPPLEMENT!!\"</td>\n",
              "      <td>I'm a 32 year old man. Construction Electricia...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>466144</th>\n",
              "      <td>466145</td>\n",
              "      <td>B001E18K8S</td>\n",
              "      <td>AQ73BMYWH1EM8</td>\n",
              "      <td>Peter D. Mccorkle</td>\n",
              "      <td>39</td>\n",
              "      <td>47</td>\n",
              "      <td>5</td>\n",
              "      <td>1242518400</td>\n",
              "      <td>\" A MIRACLE SUPPLEMENT!!\"</td>\n",
              "      <td>I'm a 32 year old man. Construction Electricia...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>323193</th>\n",
              "      <td>323194</td>\n",
              "      <td>B000OQ2DL4</td>\n",
              "      <td>AQ73BMYWH1EM8</td>\n",
              "      <td>Peter D. Mccorkle</td>\n",
              "      <td>39</td>\n",
              "      <td>47</td>\n",
              "      <td>5</td>\n",
              "      <td>1242518400</td>\n",
              "      <td>\" A MIRACLE SUPPLEMENT!!\"</td>\n",
              "      <td>I'm a 32 year old man. Construction Electricia...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>186303</th>\n",
              "      <td>186304</td>\n",
              "      <td>B002LN1ME2</td>\n",
              "      <td>AQ73BMYWH1EM8</td>\n",
              "      <td>Peter D. Mccorkle</td>\n",
              "      <td>39</td>\n",
              "      <td>47</td>\n",
              "      <td>5</td>\n",
              "      <td>1242518400</td>\n",
              "      <td>\" A MIRACLE SUPPLEMENT!!\"</td>\n",
              "      <td>I'm a 32 year old man. Construction Electricia...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>188916</th>\n",
              "      <td>188917</td>\n",
              "      <td>B001E0TBA0</td>\n",
              "      <td>AQ73BMYWH1EM8</td>\n",
              "      <td>Peter D. Mccorkle</td>\n",
              "      <td>39</td>\n",
              "      <td>47</td>\n",
              "      <td>5</td>\n",
              "      <td>1242518400</td>\n",
              "      <td>\" A MIRACLE SUPPLEMENT!!\"</td>\n",
              "      <td>I'm a 32 year old man. Construction Electricia...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394044</th>\n",
              "      <td>394045</td>\n",
              "      <td>B000WVY4PE</td>\n",
              "      <td>AQ73BMYWH1EM8</td>\n",
              "      <td>Peter D. Mccorkle</td>\n",
              "      <td>39</td>\n",
              "      <td>47</td>\n",
              "      <td>5</td>\n",
              "      <td>1242518400</td>\n",
              "      <td>\" A MIRACLE SUPPLEMENT!!\"</td>\n",
              "      <td>I'm a 32 year old man. Construction Electricia...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198968</th>\n",
              "      <td>198969</td>\n",
              "      <td>B000OQ2DJQ</td>\n",
              "      <td>AQ73BMYWH1EM8</td>\n",
              "      <td>Peter D. Mccorkle</td>\n",
              "      <td>39</td>\n",
              "      <td>47</td>\n",
              "      <td>5</td>\n",
              "      <td>1242518400</td>\n",
              "      <td>\" A MIRACLE SUPPLEMENT!!\"</td>\n",
              "      <td>I'm a 32 year old man. Construction Electricia...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Id  ...                                               Text\n",
              "117168  117169  ...  What a wonderful surprise to find LUCY's on Am...\n",
              "426709  426710  ...  What a wonderful surprise to find LUCY's on Am...\n",
              "134062  134063  ...  What a wonderful surprise to find LUCY's on Am...\n",
              "262196  262197  ...  What a wonderful surprise to find LUCY's on Am...\n",
              "262677  262678  ...  What a wonderful surprise to find LUCY's on Am...\n",
              "288541  288542  ...  Best Flavor yet and instant favorite, cant sto...\n",
              "482377  482378  ...  Best Flavor yet and instant favorite, cant sto...\n",
              "98648    98649  ...  Best Flavor yet and instant favorite, cant sto...\n",
              "542833  542834  ...  This is quality matcha tea at a very low price...\n",
              "324586  324587  ...  This is quality matcha tea at a very low price...\n",
              "411644  411645  ...  This is quality matcha tea at a very low price...\n",
              "356390  356391  ...  I'm a 32 year old man. Construction Electricia...\n",
              "257922  257923  ...  I'm a 32 year old man. Construction Electricia...\n",
              "360277  360278  ...  I'm a 32 year old man. Construction Electricia...\n",
              "466144  466145  ...  I'm a 32 year old man. Construction Electricia...\n",
              "323193  323194  ...  I'm a 32 year old man. Construction Electricia...\n",
              "186303  186304  ...  I'm a 32 year old man. Construction Electricia...\n",
              "188916  188917  ...  I'm a 32 year old man. Construction Electricia...\n",
              "394044  394045  ...  I'm a 32 year old man. Construction Electricia...\n",
              "198968  198969  ...  I'm a 32 year old man. Construction Electricia...\n",
              "\n",
              "[20 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YcwnYpEFdcD8",
        "outputId": "ad30c956-dc46-4158-d520-629a9e9f5f85"
      },
      "source": [
        "#Check the number of unique values in each column\n",
        "df.nunique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id                        568454\n",
              "ProductId                  74258\n",
              "UserId                    256059\n",
              "ProfileName               218416\n",
              "HelpfulnessNumerator         231\n",
              "HelpfulnessDenominator       234\n",
              "Score                          5\n",
              "Time                        3168\n",
              "Summary                   295742\n",
              "Text                      393579\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4SpCvjAcdcD8",
        "outputId": "d665dd36-75d3-47b7-aea7-22ed09adeb59"
      },
      "source": [
        "#Checking the values' counts for each column\n",
        "v_c = {}\n",
        "for col in df.columns:\n",
        "    v_c [col] = [df[col].value_counts().sort_values()]\n",
        "v_c"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'HelpfulnessDenominator': [181         1\n",
              "  131         1\n",
              "  113         1\n",
              "  436         1\n",
              "  416         1\n",
              "          ...  \n",
              "  4       22306\n",
              "  3       34394\n",
              "  2       61482\n",
              "  1      112753\n",
              "  0      270052\n",
              "  Name: HelpfulnessDenominator, Length: 234, dtype: int64],\n",
              " 'HelpfulnessNumerator': [491         1\n",
              "  258         1\n",
              "  354         1\n",
              "  524         1\n",
              "  866         1\n",
              "          ...  \n",
              "  4       16963\n",
              "  3       29007\n",
              "  2       53727\n",
              "  1      116467\n",
              "  0      303826\n",
              "  Name: HelpfulnessNumerator, Length: 231, dtype: int64],\n",
              " 'Id': [2047     1\n",
              "  16376    1\n",
              "  18423    1\n",
              "  20470    1\n",
              "  22517    1\n",
              "          ..\n",
              "  12294    1\n",
              "  10245    1\n",
              "  8196     1\n",
              "  20490    1\n",
              "  2049     1\n",
              "  Name: Id, Length: 568454, dtype: int64],\n",
              " 'ProductId': [B008057XGQ      1\n",
              "  B001EO5WHC      1\n",
              "  B0067MCIK0      1\n",
              "  B000B6MUGU      1\n",
              "  B000QWBUR4      1\n",
              "               ... \n",
              "  B0026RQTGE    632\n",
              "  B002QWP8H0    632\n",
              "  B002QWP89S    632\n",
              "  B002QWHJOU    632\n",
              "  B007JFMH8M    913\n",
              "  Name: ProductId, Length: 74258, dtype: int64],\n",
              " 'ProfileName': [Josh Rogan                                  1\n",
              "  Smelly Gostard                              1\n",
              "  Keith Schneider                             1\n",
              "  Jacqueline Clayton                          1\n",
              "  Brian J Alinovi                             1\n",
              "                                           ... \n",
              "  Chris                                     363\n",
              "  Rebecca of Amazon \"The Rebecca Review\"    365\n",
              "  Gary Peterson                             389\n",
              "  O. Brown \"Ms. O. Khannah-Brown\"           421\n",
              "  C. F. Hill \"CFH\"                          451\n",
              "  Name: ProfileName, Length: 218416, dtype: int64],\n",
              " 'Score': [2     29769\n",
              "  3     42640\n",
              "  1     52268\n",
              "  4     80655\n",
              "  5    363122\n",
              "  Name: Score, dtype: int64],\n",
              " 'Summary': [Melba Toast                                 1\n",
              "  Not happy. Hate artificial sweetener.       1\n",
              "  Cabernet Sauvignon                          1\n",
              "  So easy and So good                         1\n",
              "  Blue Agave Nectar                           1\n",
              "                                           ... \n",
              "  Great product                            1091\n",
              "  Yummy                                    1268\n",
              "  Yummy!                                   1594\n",
              "  Delicious                                2316\n",
              "  Delicious!                               2462\n",
              "  Name: Summary, Length: 295742, dtype: int64],\n",
              " 'Text': [I was reluctant to buy one after reading the negative reviews but I was also encouraged by the positive ones.  I'm glad I decided to take the chance!  I had it mailed directly to my Mother as a gift. Like the other reviews say, it arrived quickly and was packaged extremely well.  Not a single branch was damaged and the pot also made the trip in perfect condition.  Given the fact that this is February, I expected it to arrive baren of any foliage.  It actually is so healthy it's full of little green buds.  The branches are pruned to give the tree a very nice shape, and the soil (according to my Mom) is the correct type of soil it SHOULD be in and not just some temporary crud made to last only as long as the shipping time.  Thank you for making my gift a nice one.  My Mom really loves it.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       1\n",
              "  I guess I should have looked around more, I was expecting them in a tub. Instead they were in 3 baggies with sticky ties and it made me feel a bit uneasy.... I was thinking:  who packaged these, where were these packaged and why don't I have any product information included?<br />I don't believe the price was any better than what I could of found in a local candy store or drugstore.  I've purchased more product for less money before.  The Christmas tree shop in Wiliston, VT sells a 4 lb. tub for $5.99  but since I just moved to FL... my local knowledge of stores is limited.<br /><br />I bought these for a specific reason, I am attending a memorial service for a dear relative (who loved these) and I was planning on bringing them and putting them in dishes for those attending the service, instead because of the small size of them (I've seen larger ones) and because they have too much sugar on them, I will be keeping them at home and buying others somewhere else.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      1\n",
              "  Four stars only, however -- not b/c of slice-and-wrap issues, rather b/c I have had better -- this is not the VERY best available (though at that price it should be!).<br />Nevertheless it is indeed a very good product, better even when&if the deli staff learn to slice it as thin as possible and, not lay slice atop slice making separation nearly impossible; instead using plenty of the heavy wax paper between layers of meat.  Like: a dozen of sheets per pound if done right (I'm guesstimating -- they still wont let me near big dangerous machines, darn it! ;)<br /><br />This is Italian Prosciutto, not some American boiled ham.  No one should be handling it unless properly trained, fGs!<br />Come on now people.  Get with the program.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 1\n",
              "  Andes chips are by far one of the best products I've ever tried.  They are delicious,they melt in your mouth.  Great for baking my cupcakes. In my country there is nothing similar to this and I'm in love with this product. I highly recommend it, you won't regret it...I received them in good conditions, they were not melted...I will definitely keep purchasing this product.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              1\n",
              "  This tea is amazing!  A friend gave me a couple of bags to try and I am hooked!  The name says it all - sweet & spicy, very flavorul and full, just an awesome and relaxing cup of tea.  Excited to see it on Amazon - my friend bought it at a specialty grocery store that isn't easy to get to. Try it - you won't be disappointed.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              1\n",
              "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   ... \n",
              "  I'm addicted to salty and tangy flavors, so when I opened my first bag of Sea Salt & Vinegar Kettle Brand chips I knew I had a perfect complement to my vegetable trays of cucumber, carrot, celery and cherry tomatoes. Skip the dip; balance the tangy chips by alternating bites of raw vegetable.<br /><br />As an Oregonian, I'm proud to share these delectable snacks with friends, especially those living outside our state and who haven't experienced gourmet chips. I tell them Kettle Brand does for potato chips what microbrews did for beer.<br /><br />Kettle Brand potato chips are unmistakable--a light gold color, rich flavor and amazing crunch. Kettle Brand chips are also a healthier snacking option than the major chip brands. Kettle Brand chips don't have trans fats, MSG or artificial flavors and colorings. The company also has a line of organic potato chips and all of their products are certified Kosher.<br /><br />I also recommend <a href=\"http://www.amazon.com/gp/product/B000G6MBV4\">Kettle Chips Honey Dijon</a> and <a href=\"http://www.amazon.com/gp/product/B000G6Q4GM\">Kettle Chips Spicy Thai</a>.<br /><br />Annette Solomon, a reporter for the Salem Statesman Journal recently noted that a glass of wine goes nicely with these chips. Solomon wrote, \"...you could be missing out on a wonderful pairing. These chips are spicy, so you would want to select a semi-sweet white wine. Also, a moderate amount of acid will subdue the strong flavors of ginger, lime, garlic and cilantro without over-powering them. Classically, a German-style Riesling fits these parameters perfectly.\"                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           42\n",
              "  The pet food industry can be one of the most infuriating as you start doing research and discovering what trash manufacturers (and the vets who get paid off by the manufacturers) push onto the unsuspecting public. For reference, don't ask your vet - do some Internet research at sites like, for example, [...]. What you'll find is that even the supposed \"high-end\" lines from basically every major supermarket label are horrible for your fuzzy friends. People who trust the marketing or trust their vets overpay and feed their cats foods that are full of carcinogens, cheap ingredients that are inappropriate for a cat's digestive system, and byproducts that provide nothing of value and are basically a way to make cheap profit off of literal garbage. It's a tragedy, as these same cats end up with shorter lifespans, kidney problems, UTIs, cancer, and a host of other issues, all of which could have been avoided by a high quality food.<br /><br />This is an A-Grade food. What you'll find here is very high protein content, many meat meals, good carbs and oils, and no grains, which is great as a cat is not an omnivore, it's a carnivore, and it simply does not need grains. It has 50 percent protein, which is among the highest you'll see in cat food. The first 4 ingredients are all meats.<br /><br />What you will NOT find here is the kind of junk you'll see in things like Purina One and Science Diet - byproducts (aka beaks, feathers, bird feet), brewers rice (aka the waste product from making beer), wheat flour (a leading cause of pet allergies), corn gluten (a cheap waste of space), and things like \"liver flavor\" (because they don't want to pay for the real thing). I didn't just pull those ingredients out of the air - I was reading the ingredients list of Purina One. It's pathetic.<br /><br />Don't feed your cats trash. Pay a little more to feed them good food now, and they'll lead longer, healthier, more satisfied lives as a result. It's worth it.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   45\n",
              "  I have two cats, one 6 and one 2 years old. Both are indoor cats in excellent health. I saw the negative review and talked to my vet about it. I've also asked a number of veterinary professionals what to feed my cats and they all answer the same thing: Science Diet. Sure, you'll see stories of how one person's cat had issues, but even if that's 100% true, it's 1 case out of millions. Science and fact aren't based on someone's experience.<br /><br />So my point is, I love my cats and I'm very concerned about their health. I trust people who actually have medical degrees and experience with a wide range of animals. My only caution is do not fall for some hype or scare tactic that recommends some unproven or untested food or some fad diet for your pet. Don't listen to me, don't listen to the negative review. ASK YOUR VET what they recommend, and follow their instructions. My guess is you'll end up buying the Science Diet anyhow.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        51\n",
              "  Diamond Almonds<br />Almonds are a good source of magnesium. One ounce contain approximately 20% of the RDA for an adult recommended dietary allowance (RDA)<br />Recommended Dietary Allowance (RDA) the amounts of selected nutrients considered adequate to meet the known nutrient needs of healthy people. The RDA is based on scientific knowledge and has been presented by a committee of the Food and Nutrition Board (FNB) of the National Academy of Sciences (NAS). The Canadian equivalent is the Recommended Nutrient Intakes. RDA is generally accepted throughout the world as a valid source of information. At least 40 different nations have as well as organizations have published standards similar to the RDA.<br /><br />Magnesium is the fourth most abundant mineral in the body and is essential to good health. Approximately 50% of total body magnesium is found in bone. The other half is found predominantly inside cells of body tissues and organs. Only 1% of magnesium is found in blood, but the body works very hard to keep blood levels of magnesium constant.<br />Magnesium is needed for more than 300 biochemical reactions in the body. It helps maintain normal muscle and nerve function, keeps heart rhythm steady, supports a healthy immune system, and keeps bones strong. Magnesium also helps regulate blood sugar levels, promotes normal blood pressure, and is known to be involved in energy metabolism and protein synthesis. There is an increased interest in the role of magnesium in preventing and managing disorders such as hypertension, cardiovascular disease, and diabetes. Dietary magnesium is absorbed in the small intestines. Are you taking enough magnesium in the diet? The recommended daily allowance is 300mgs/day for men, 350mgs for women. Magnesium is extremely safe by mouth - too much simply causes diarrhea. Try increasing the amount of magnesium you take by mouth until it causes diarrhea, then reduce the dose slightly so it does not. This is called taking magnesium to bowel tolerance (just like using vitamin C to bowel tolerance.<br /><br />Almonds are a good source of Fiber.<br />One ounce contains approximately 12% of the RDA for an adult.<br />So, in summary, Almonds are a very healthy snack, so what's left to say?<br /><br />Two things:<br />1st be cautious about \"bowel tolerance\" or \"happy bowels\" if you prefer.<br />2nd Taste.  I realize that taste is a personal thing.  That's why they make chocolate, strawberry, and vanilla.<br /><br />Here's my current favorites\"<br /><br /> 1st Blue Diamond Dark mint dark chocolate (reminds me of the chocolate mint Girl Scout Cookies with less sugar).<br /><br />2nd Blue diamond Dark Chocolate Oven Roasted<br />3rd  Blue diamond Blue Butter Toffee<br />4thd Blue Diamond Almonds Bold Habanero BBQ (reminds me of  BBQ potato chips again with less sugar).<br />5th Bold Blazin Buffalo Wing<br />6th Blue Diamond Almonds Cinnamon Brown Sugar<br />7th  Blue Diamond Almonds Lime `n Chili<br />8th  Blue Diamond Almonds Smokehouse<br />9th  Bold Wasabi & Soy Sauce<br /><br />But that's just me: you might have a completely different take on these flavors.<br /> 4th  and 5th are very close.<br /><br />Gunner<br />May, 2012                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               126\n",
              "  This review will make me sound really stupid, but whatever. I don't really care as long as people find out what's real and can avoid my mistakes.<br /><br />I got my wonderful little sweet Bella Bean when she was a few days shy of three years old. She had been bounced around from house to house and eating whatever was cheap. I have had cats around me my entire life, for about twenty-five years now. My mother always just fed them whatever, the kinds of food you buy in the supermarket - Friskies, Nine Lives, Kit & Kaboodle, stuff like that. And our cats were always fine, at least in terms of their eating habits. They would eat in the morning, stop when they were done, come back, and eat some more when they got hungry.<br /><br />My housemate at the time was working for Hill's and assured me that this was the best food ever made, so great, so on and so forth. I now know that she is an utter buffoon, but I initially trusted her judgment, which is so unfortunate because she doesn't think. She also had plenty of coupons for free or deeply discounted bags, which made it a much more attractive choice.<br /><br />I first tried feeding the little Bean an unmeasured amount of Science Diet in a bowl, but that didn't work, as she would devour it in one sitting. So then I took to measuring it, and she did the same thing. Then I started parsing it out to twice a day. That didn't work either, because she would start going crazy in the middle of the day, running around, intentionally destroying things, deliberately spilling her water, crying, etc., until she got more food. So then I split it into three servings. Same thing. Then it got to be four servings. That was a little better, but it was too much maintenance and unrealistic to be around every day to feed her four times. So then it went back to three. All the while, I was trying to reduce the amount of food I was feeding her to less than 3/4 of a cup because she was a little chubby. Reducing was hell. She became even more hungry, but I figured she would get used to it. Not really.<br /><br />For over a year, she would wake me up every morning looking for food in a serious way, knocking things off my desk, ripping up any paper she could find, scratching at the door and committing general acts of mischief. As soon as she got food, she was back to her sweet self, but only for three or four hours.<br /><br />We thought she was bored, we thought she was a little nutty, and maybe even had a kitty eating disorder. She always wanted food. It was kind of funny but in the end it was just sad.<br /><br />A few weeks back, we took her to a new (good, non-money factory) vet for her second checkup since I've had her. I talked with him about her being always hungry. He asked what she ate, and I told him the adult indoor Science Diet. Without saying as much, he basically told me that this food is garbage and I should look for something else. He said cat food should have a protein followed by a carbohydrate as the first two ingredients. Science Diet does, in a very loose, by-product kind of way - ground up slaughterhouse leftovers and corn dust. Then they put a bunch of vitamins in it to make it \"healthy,\" instead of just using good ingredients from the beginning. Not that I care about spending money on the Bean, but this food is way too expensive for what it is.<br /><br />So we began transitioning her onto Wellness indoor formula about two weeks ago. She is still eating 50 percent Science Diet with 50 percent Wellness (you really shouldn't just give a cat different food one day out of the blue) but SHE NEVER FREAKS OUT ANYMORE. It's amazing. And it's 100 percent because she is eating real food now, along with that sawdust and chicken hearts I still regrettable have to feed her. We have her down to eating twice a day, only 1/3 cup in TOTAL. She was eating three times a day, 5/8 of a cup in total. Now she eats some in the morning, walks away, eats some more a few hours later, and then looks for dinner about twelve hours after her initial feeding. No more knocking things over, no more trashing Dad's papers on the desk, no more howling, no more deliberately spilled water on the floor. It's incredible.<br /><br />I feel so bad that I was doing this to her for so long. We really thought she was just being dramatic or whatever. But no, she was genuinely hungry because she wasn't eating any real food.<br /><br />Do your cat a favor - buy her or him so food made with real ingredients, things you would eat - Wellness, Halo, Innova, Evo, whatever. Figure it out for yourself, but please don't feed your cat this. It's garbage.    199\n",
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            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oVTc9kKydcD8",
        "outputId": "1411b5c2-db22-4591-fdaa-969f64e69f63"
      },
      "source": [
        "#Checking the duplicated values for each column\n",
        "v_c = {}\n",
        "for col in df.columns:\n",
        "    v_c [col] = [df[col].duplicated().sum()]\n",
        "v_c"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'HelpfulnessDenominator': [568220],\n",
              " 'HelpfulnessNumerator': [568223],\n",
              " 'Id': [0],\n",
              " 'ProductId': [494196],\n",
              " 'ProfileName': [350037],\n",
              " 'Score': [568449],\n",
              " 'Summary': [272711],\n",
              " 'Text': [174875],\n",
              " 'Time': [565286],\n",
              " 'UserId': [312395]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 877
        },
        "id": "QZcFssxmdcD9",
        "outputId": "2002b923-0ca9-4b1d-ed1c-8e50f31d97e4"
      },
      "source": [
        "#Visulaize the attributes\n",
        "df.hist(bins=50, figsize=(20,15))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1080 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bvSUBKTDjLpJ"
      },
      "source": [
        "### Assessing\n",
        "\n",
        "1. Null values in `ProfileName` and `Summary` columns.\n",
        "\n",
        "\n",
        "2. * No 'whole' duplicated rows.\n",
        "\n",
        "   * There are rows that have duplicated values for the `ProductId`, `UserId`, `ProfileName`, `Score`, `Summary` and `Text`. Their `HelpfulnessNumerator` and `HelpfulnessDenominator` differ according to the `Time`. (The row that has a greater value in `Time` column has a greater `HelpfulnessDenominator` and greater or same value for `HelpfulnessNumerator`).\n",
        "   * Other rows only differ in `Id` (which is an unneccessary column). \n",
        "   * Also some differ in `ProductId`, `HelpfulnessNumerator` and `HelpfulnessDenominator`.\n",
        "   * Moreover, some rows differ in `Id` and `ProductId` only!!\n",
        "\n",
        "\n",
        "3. There are some unneccessary columns: `Id`, `Time` and `UserId` **or** `ProfileName` (It's better to keep the `UserId`).\n",
        "\n",
        "\n",
        "4. `ProfileName` values are inconsistent and do not abide to a certain Name format and some values are not meaningful.\n",
        "\n",
        "\n",
        "5. From the percentiles, there is a huge gap between the **95% value** and **max value** of `HelpfulnessNumerator` and `HelpfulnessDenumerator`, which indicates the existence of outlieres within these columns.\n",
        "\n",
        "\n",
        "6. It will be more useful to store `HelpfulnessNumerator`/`HelpfulnessDenominator`. It denotes the percentage of people who found the product helpful compared to all who voted for helpful or not."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iLFHYhQvkWDK"
      },
      "source": [
        "### Cleaning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U_8aJcfYdcD_"
      },
      "source": [
        "# First create a copy of the dataset\n",
        "df_clean = df.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fFH1IHxCdcD_"
      },
      "source": [
        "1. * `ProfileName` column will be dropped so we don't have to care about its null values.\n",
        "   * We can fill in the null values in the `Summary` column using regex. However, it's beyond the scope of this course."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pblOT-9cdcEA"
      },
      "source": [
        "2. * Drop the rows that have the same `UserId`, `ProfileName`, `Score`, `Summary` and `Text` using `Time` column, leave the row that has the highest `Time` value. (Sometimes `ProductId` is the same in these rows and sometimes not. Therefore, one random `ProductId` will remain, it doesn't matter which one).\n",
        "\n",
        "![title](dupl.png)\n",
        "\n",
        "   If they have the same `Time`, leave the **highest** `HelpfulnessDenominator`. As it is clear from these rows, at **Time = 1229385600** there was **1** `HelpfulnessDenominator`, then at **Time = 1231718400** we have two rows, one that has **0** `HelpfulnessDenominator` and the other has **2**. Therefore, the value **2 is the correct one**, as the `HelpfulnessDenominator` cannot get smaller as time passes!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "awiw1nxKdcEA",
        "outputId": "32952d46-07f0-48f6-fb0a-2b5e430e6e2c"
      },
      "source": [
        "#df2 = df_clean[df_clean.duplicated(subset=['UserId', 'ProfileName', 'Score', 'Summary', 'Text'],  keep=False)]\n",
        "\n",
        "# Sort the df descendingly according to the Time\n",
        "df_clean.sort_values(by=['Time','HelpfulnessDenominator'], ascending=False, inplace=True)\n",
        "\n",
        "# Drop the duplicates keeping the first one (highest Time/ HelpfulnessDenominator)\n",
        "df_clean.drop_duplicates(subset=['UserId', 'ProfileName', 'Score', 'Summary', 'Text'], keep='first', inplace=True)\n",
        "\n",
        "#Check the number of unique values in each column\n",
        "df_clean.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 395052 entries, 492288 to 150523\n",
            "Data columns (total 10 columns):\n",
            " #   Column                  Non-Null Count   Dtype \n",
            "---  ------                  --------------   ----- \n",
            " 0   Id                      395052 non-null  int64 \n",
            " 1   ProductId               395052 non-null  object\n",
            " 2   UserId                  395052 non-null  object\n",
            " 3   ProfileName             395041 non-null  object\n",
            " 4   HelpfulnessNumerator    395052 non-null  int64 \n",
            " 5   HelpfulnessDenominator  395052 non-null  int64 \n",
            " 6   Score                   395052 non-null  int64 \n",
            " 7   Time                    395052 non-null  int64 \n",
            " 8   Summary                 395049 non-null  object\n",
            " 9   Text                    395052 non-null  object\n",
            "dtypes: int64(5), object(5)\n",
            "memory usage: 33.2+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RlHH7uTzdcEA",
        "outputId": "d9e0f613-3e34-4f9c-d78e-b881be175e10"
      },
      "source": [
        "#Checking the duplicated values for each column\n",
        "v_c = {}\n",
        "for col in df_clean.columns:\n",
        "    v_c [col] = [df_clean[col].duplicated().sum()]\n",
        "v_c"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'HelpfulnessDenominator': [394818],\n",
              " 'HelpfulnessNumerator': [394821],\n",
              " 'Id': [0],\n",
              " 'ProductId': [327310],\n",
              " 'ProfileName': [176635],\n",
              " 'Score': [395047],\n",
              " 'Summary': [99309],\n",
              " 'Text': [1473],\n",
              " 'Time': [391885],\n",
              " 'UserId': [138993]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8CJlIL2EdcEA"
      },
      "source": [
        "* Drop the columns that have the same `UserId`, `Summary` and `Text` (Keep the highest Score if the scores are different)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SpNQ1gxZdcEA"
      },
      "source": [
        "# Sort the df descendingly according to the Time then Score\n",
        "df_clean.sort_values(by=['Time','Score'], ascending=False, inplace=True)\n",
        "\n",
        "# Drop the duplicates keeping the first one (highest Time/ HelpfulnessDenominator)\n",
        "df_clean.drop_duplicates(subset=['UserId', 'Summary', 'Text'], keep='first', inplace=True)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IhJ4TbksdcEB"
      },
      "source": [
        "* Drop the columns that have the same `ProfileName`, `Summary` and `Text` (Keep the highest Score if the scores are different)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aVnnpk3xdcEB"
      },
      "source": [
        "# Sort the df descendingly according to the Time then Score\n",
        "df_clean.sort_values(by=['Time','Score'], ascending=False, inplace=True)\n",
        "\n",
        "# Drop the duplicates keeping the first one (highest Time/ HelpfulnessDenominator)\n",
        "df_clean.drop_duplicates(subset=['ProfileName', 'Summary', 'Text'], keep='first', inplace=True)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8hp0sBb0dcEB"
      },
      "source": [
        "*  **Testing**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VGOistuadcEC",
        "outputId": "8eaa81af-81f5-4211-f983-d68f248bb14b"
      },
      "source": [
        "df_clean.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 394973 entries, 566797 to 150523\n",
            "Data columns (total 10 columns):\n",
            " #   Column                  Non-Null Count   Dtype \n",
            "---  ------                  --------------   ----- \n",
            " 0   Id                      394973 non-null  int64 \n",
            " 1   ProductId               394973 non-null  object\n",
            " 2   UserId                  394973 non-null  object\n",
            " 3   ProfileName             394962 non-null  object\n",
            " 4   HelpfulnessNumerator    394973 non-null  int64 \n",
            " 5   HelpfulnessDenominator  394973 non-null  int64 \n",
            " 6   Score                   394973 non-null  int64 \n",
            " 7   Time                    394973 non-null  int64 \n",
            " 8   Summary                 394970 non-null  object\n",
            " 9   Text                    394973 non-null  object\n",
            "dtypes: int64(5), object(5)\n",
            "memory usage: 33.1+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W3roa_MxdcEC",
        "outputId": "ceafad21-169e-4bf5-bedd-80cc02d10c5a"
      },
      "source": [
        "#Checking the duplicated values for each column\n",
        "v_c = {}\n",
        "for col in df_clean.columns:\n",
        "    v_c [col] = [df_clean[col].duplicated().sum()]\n",
        "v_c"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'HelpfulnessDenominator': [394739],\n",
              " 'HelpfulnessNumerator': [394742],\n",
              " 'Id': [0],\n",
              " 'ProductId': [327247],\n",
              " 'ProfileName': [176561],\n",
              " 'Score': [394968],\n",
              " 'Summary': [99230],\n",
              " 'Text': [1394],\n",
              " 'Time': [391806],\n",
              " 'UserId': [138915]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8pJ7lkvadcEC"
      },
      "source": [
        "* Drop the columns that have the same `Text` (Keep the highest Score if the scores are different)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uxyV1B0UdcEC"
      },
      "source": [
        "# Sort the df descendingly according to the Time then Score\n",
        "df_clean.sort_values(by=['Time','Score'], ascending=False, inplace=True)\n",
        "\n",
        "# Drop the duplicates keeping the first one (highest Time/ HelpfulnessDenominator)\n",
        "df_clean.drop_duplicates(subset=['Text'], keep='first', inplace=True)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "igCGhzmkdcEC"
      },
      "source": [
        "* **Testing**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8DgTvpWidcED",
        "outputId": "ebc8c134-1e3c-4041-8329-41dab826285b"
      },
      "source": [
        "df_clean.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 393579 entries, 566797 to 150523\n",
            "Data columns (total 10 columns):\n",
            " #   Column                  Non-Null Count   Dtype \n",
            "---  ------                  --------------   ----- \n",
            " 0   Id                      393579 non-null  int64 \n",
            " 1   ProductId               393579 non-null  object\n",
            " 2   UserId                  393579 non-null  object\n",
            " 3   ProfileName             393568 non-null  object\n",
            " 4   HelpfulnessNumerator    393579 non-null  int64 \n",
            " 5   HelpfulnessDenominator  393579 non-null  int64 \n",
            " 6   Score                   393579 non-null  int64 \n",
            " 7   Time                    393579 non-null  int64 \n",
            " 8   Summary                 393576 non-null  object\n",
            " 9   Text                    393579 non-null  object\n",
            "dtypes: int64(5), object(5)\n",
            "memory usage: 33.0+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nBGLhD7GdcED",
        "outputId": "10b617d6-783a-4efb-a312-0f19e4d88dc1"
      },
      "source": [
        "#Checking the duplicated values for each column\n",
        "v_c = {}\n",
        "for col in df_clean.columns:\n",
        "    v_c [col] = [df_clean[col].duplicated().sum()]\n",
        "v_c"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'HelpfulnessDenominator': [393345],\n",
              " 'HelpfulnessNumerator': [393348],\n",
              " 'Id': [0],\n",
              " 'ProductId': [326041],\n",
              " 'ProfileName': [175181],\n",
              " 'Score': [393574],\n",
              " 'Summary': [98881],\n",
              " 'Text': [0],\n",
              " 'Time': [390412],\n",
              " 'UserId': [137539]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RR2SoD_4dcED"
      },
      "source": [
        "* Checking if there are any other duplicates that need to be dropped."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 47
        },
        "id": "Gh6_EUAzdcED",
        "scrolled": true,
        "outputId": "53af5aa0-3e1f-4f71-e8ca-1669855164bf"
      },
      "source": [
        "df_clean[df_clean.duplicated(subset=['Summary', 'Text'],  keep=False)]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>ProductId</th>\n",
              "      <th>UserId</th>\n",
              "      <th>ProfileName</th>\n",
              "      <th>HelpfulnessNumerator</th>\n",
              "      <th>HelpfulnessDenominator</th>\n",
              "      <th>Score</th>\n",
              "      <th>Time</th>\n",
              "      <th>Summary</th>\n",
              "      <th>Text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Id, ProductId, UserId, ProfileName, HelpfulnessNumerator, HelpfulnessDenominator, Score, Time, Summary, Text]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "rgbudQ2ydcEG",
        "outputId": "c969ab28-df51-44c2-f385-d8bb6d1f2a60"
      },
      "source": [
        "df_clean[df_clean['Summary'].duplicated(keep=False)].sort_values(by='Summary', ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>ProductId</th>\n",
              "      <th>UserId</th>\n",
              "      <th>ProfileName</th>\n",
              "      <th>HelpfulnessNumerator</th>\n",
              "      <th>HelpfulnessDenominator</th>\n",
              "      <th>Score</th>\n",
              "      <th>Time</th>\n",
              "      <th>Summary</th>\n",
              "      <th>Text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>48131</th>\n",
              "      <td>48132</td>\n",
              "      <td>B004SRH2B6</td>\n",
              "      <td>A14CS7QYJVLIX1</td>\n",
              "      <td>Susan M. Barry \"suzieb\"</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1152057600</td>\n",
              "      <td>zico</td>\n",
              "      <td>I drink this product after hot Yoga and immedi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47893</th>\n",
              "      <td>47894</td>\n",
              "      <td>B004SRH2B6</td>\n",
              "      <td>A2651U83ADWP6R</td>\n",
              "      <td>aggie</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1348790400</td>\n",
              "      <td>zico</td>\n",
              "      <td>I love ZICO. I have Sjogrens and can get dehyd...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>555584</th>\n",
              "      <td>555585</td>\n",
              "      <td>B001SAVRMQ</td>\n",
              "      <td>A2PB7547K54Y26</td>\n",
              "      <td>Amos Quito</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1322870400</td>\n",
              "      <td>zero stars</td>\n",
              "      <td>poor quality/old product.  Upon grating, nutme...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>469036</th>\n",
              "      <td>469037</td>\n",
              "      <td>B005MQ1XVW</td>\n",
              "      <td>A3OO0PCA2WG3L6</td>\n",
              "      <td>pepper</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1349568000</td>\n",
              "      <td>zero stars</td>\n",
              "      <td>not enough info to purchase this product also ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49824</th>\n",
              "      <td>49825</td>\n",
              "      <td>B000EUIZ8K</td>\n",
              "      <td>AIQRUBULCEZNA</td>\n",
              "      <td>M. Hunerberg</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1252368000</td>\n",
              "      <td>zen</td>\n",
              "      <td>great for making iced green tea. same stuff th...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103092</th>\n",
              "      <td>103093</td>\n",
              "      <td>B001E5E2RM</td>\n",
              "      <td>A22AL0PEWQLHK0</td>\n",
              "      <td>Art Majors</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1344988800</td>\n",
              "      <td>\"Classic\" is much better!</td>\n",
              "      <td>I agree with some of the other reviewers. I ha...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129481</th>\n",
              "      <td>129482</td>\n",
              "      <td>B002PO73KY</td>\n",
              "      <td>A22AL0PEWQLHK0</td>\n",
              "      <td>Art Majors</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1344988800</td>\n",
              "      <td>\"Classic\" is much better!</td>\n",
              "      <td>I agree with some of the other reviewers (for ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>392529</th>\n",
              "      <td>392530</td>\n",
              "      <td>B001H9XOEW</td>\n",
              "      <td>A22R17YPXEULEZ</td>\n",
              "      <td>Liz Wong</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1334534400</td>\n",
              "      <td>NaN</td>\n",
              "      <td>I like the product and tried it before I pucha...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>237565</th>\n",
              "      <td>237566</td>\n",
              "      <td>B000ELGPAO</td>\n",
              "      <td>A15AMT9T9A1309</td>\n",
              "      <td>Film-Friend</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1328572800</td>\n",
              "      <td>NaN</td>\n",
              "      <td>This is a cool system only problem is that the...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33958</th>\n",
              "      <td>33959</td>\n",
              "      <td>B00412W76S</td>\n",
              "      <td>A3TJPSWY2HE4BS</td>\n",
              "      <td>S. Layton \"homeschool blogger\"</td>\n",
              "      <td>1</td>\n",
              "      <td>24</td>\n",
              "      <td>2</td>\n",
              "      <td>1173312000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>I only used two maybe three tea bags and got p...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>119073 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            Id  ...                                               Text\n",
              "48131    48132  ...  I drink this product after hot Yoga and immedi...\n",
              "47893    47894  ...  I love ZICO. I have Sjogrens and can get dehyd...\n",
              "555584  555585  ...  poor quality/old product.  Upon grating, nutme...\n",
              "469036  469037  ...  not enough info to purchase this product also ...\n",
              "49824    49825  ...  great for making iced green tea. same stuff th...\n",
              "...        ...  ...                                                ...\n",
              "103092  103093  ...  I agree with some of the other reviewers. I ha...\n",
              "129481  129482  ...  I agree with some of the other reviewers (for ...\n",
              "392529  392530  ...  I like the product and tried it before I pucha...\n",
              "237565  237566  ...  This is a cool system only problem is that the...\n",
              "33958    33959  ...  I only used two maybe three tea bags and got p...\n",
              "\n",
              "[119073 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ps92yCeGdcEH"
      },
      "source": [
        "* Duplicates among the Summary don't need to be dropped, as having the same Summary for different reviews can happen.\n",
        "* However, there are some remaining duplicates for the `Summary` and `Text` that have very slight letter differences. This might be solved using regex, again, beyond the scope of this course."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XrTNIVlPdcEH"
      },
      "source": [
        "3. Drop the columns: `Id`, `Time` and `ProfileName` (Keep the `UserId` as it has more unique values).\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ApaCmUmIdcEH"
      },
      "source": [
        "# First reset the indeces\n",
        "df_clean.reset_index(drop=True, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "myDN1kVldcEI"
      },
      "source": [
        "df_clean.drop(columns=['Id','Time','ProfileName'], inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "_DxXokSIdcEJ",
        "outputId": "5fcef430-d9b4-4771-ad10-cad6dfea8848"
      },
      "source": [
        "df_clean.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ProductId</th>\n",
              "      <th>UserId</th>\n",
              "      <th>HelpfulnessNumerator</th>\n",
              "      <th>HelpfulnessDenominator</th>\n",
              "      <th>Score</th>\n",
              "      <th>Summary</th>\n",
              "      <th>Text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>B001PQTYN2</td>\n",
              "      <td>A3OTHWG8LLCLMU</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>Great</td>\n",
              "      <td>If you like a great , hot, sauce then buy this...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>B00171APVA</td>\n",
              "      <td>A21BT40VZCCYT4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>Healthy Dog Food</td>\n",
              "      <td>This is a very healthy dog food. Good for thei...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>B004FD13RW</td>\n",
              "      <td>A1BPLP0BKERV</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>It is awesome.</td>\n",
              "      <td>My partner is very happy with the tea, and is ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>B009WSNWC4</td>\n",
              "      <td>AMP7K1O84DH1T</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>DELICIOUS</td>\n",
              "      <td>Purchased this product at a local store in NY ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>B00523NRVO</td>\n",
              "      <td>A2JDXKFZ0PFHKU</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>The perfect pop!</td>\n",
              "      <td>These lollipops are are well done, look exactl...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    ProductId  ...                                               Text\n",
              "0  B001PQTYN2  ...  If you like a great , hot, sauce then buy this...\n",
              "1  B00171APVA  ...  This is a very healthy dog food. Good for thei...\n",
              "2  B004FD13RW  ...  My partner is very happy with the tea, and is ...\n",
              "3  B009WSNWC4  ...  Purchased this product at a local store in NY ...\n",
              "4  B00523NRVO  ...  These lollipops are are well done, look exactl...\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PQQxf9v9dcEK"
      },
      "source": [
        "4. The column `ProfileName` was already dropped."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "9KuLXRv5dcEK",
        "outputId": "f312dac2-9fb1-453e-94c0-08e387548d69"
      },
      "source": [
        "df_clean[df_clean['HelpfulnessDenominator'] > 9].sort_values(by='HelpfulnessDenominator', ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ProductId</th>\n",
              "      <th>UserId</th>\n",
              "      <th>HelpfulnessNumerator</th>\n",
              "      <th>HelpfulnessDenominator</th>\n",
              "      <th>Score</th>\n",
              "      <th>Summary</th>\n",
              "      <th>Text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>324097</th>\n",
              "      <td>B00012182G</td>\n",
              "      <td>A1JUGIQDY6UYSM</td>\n",
              "      <td>844</td>\n",
              "      <td>923</td>\n",
              "      <td>3</td>\n",
              "      <td>Whole Rabbit - NOT!</td>\n",
              "      <td>I ordered one of these Fresh \"Whole\" Rabbits, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>387779</th>\n",
              "      <td>B000FI4O90</td>\n",
              "      <td>A1GQGYROVZVW49</td>\n",
              "      <td>866</td>\n",
              "      <td>878</td>\n",
              "      <td>5</td>\n",
              "      <td>Works as Advertised - Classy Product</td>\n",
              "      <td>see update at end of review&lt;br /&gt;&lt;br /&gt;*******...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>312564</th>\n",
              "      <td>B001PQTYN2</td>\n",
              "      <td>A1QB2Y8GSME58Y</td>\n",
              "      <td>808</td>\n",
              "      <td>815</td>\n",
              "      <td>5</td>\n",
              "      <td>sauce not for mortals</td>\n",
              "      <td>I purchased a burrito from a small shop a few ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>203416</th>\n",
              "      <td>B001F10XUU</td>\n",
              "      <td>A39V22BIBUMMB3</td>\n",
              "      <td>580</td>\n",
              "      <td>593</td>\n",
              "      <td>1</td>\n",
              "      <td>Lost in Translation: Truth</td>\n",
              "      <td>This product is called \"Hunmatsu-RyokuCha,\" in...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>362086</th>\n",
              "      <td>B000UUWECC</td>\n",
              "      <td>AU3GYRAKBUAEU</td>\n",
              "      <td>491</td>\n",
              "      <td>569</td>\n",
              "      <td>3</td>\n",
              "      <td>not bad stuff, but I have serious questions</td>\n",
              "      <td>Coconut water is the liquid inside an unopened...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>381654</th>\n",
              "      <td>B000LQTTVY</td>\n",
              "      <td>A39PE92UTLIR9X</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>Wonderful Freezer Storage</td>\n",
              "      <td>These Tupperware containers are wonderful.  It...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>381655</th>\n",
              "      <td>B000LKTEYC</td>\n",
              "      <td>AW1VO5WYU3PL6</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>Camel Halvah Bars</td>\n",
              "      <td>Excelent tasting, with little sugar.&lt;br /&gt;&lt;br ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>381656</th>\n",
              "      <td>B000JKPXU8</td>\n",
              "      <td>A3FBPC699DPYFP</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>Black Truffle Salt</td>\n",
              "      <td>After you use this product, you will never rea...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>381726</th>\n",
              "      <td>B000EEWZG4</td>\n",
              "      <td>A2Y5P8ISTELDO7</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>Good stuff</td>\n",
              "      <td>I used to buy the larger cans, but this is a b...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178948</th>\n",
              "      <td>B000J490VC</td>\n",
              "      <td>AKL3O57JQPM53</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>Fogcutter tea</td>\n",
              "      <td>My Doctor actually had me try some of this tea...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>17324 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         ProductId  ...                                               Text\n",
              "324097  B00012182G  ...  I ordered one of these Fresh \"Whole\" Rabbits, ...\n",
              "387779  B000FI4O90  ...  see update at end of review<br /><br />*******...\n",
              "312564  B001PQTYN2  ...  I purchased a burrito from a small shop a few ...\n",
              "203416  B001F10XUU  ...  This product is called \"Hunmatsu-RyokuCha,\" in...\n",
              "362086  B000UUWECC  ...  Coconut water is the liquid inside an unopened...\n",
              "...            ...  ...                                                ...\n",
              "381654  B000LQTTVY  ...  These Tupperware containers are wonderful.  It...\n",
              "381655  B000LKTEYC  ...  Excelent tasting, with little sugar.<br /><br ...\n",
              "381656  B000JKPXU8  ...  After you use this product, you will never rea...\n",
              "381726  B000EEWZG4  ...  I used to buy the larger cans, but this is a b...\n",
              "178948  B000J490VC  ...  My Doctor actually had me try some of this tea...\n",
              "\n",
              "[17324 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qmMXgXRAdcEL"
      },
      "source": [
        "5. These values represent 5% of the dataset. However they are approximately 12k rows for the Numerator and 17k rows for the Denominator. We cannot say that they are invalid and we cannot drop these rows."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MZXzRE_zdcEL"
      },
      "source": [
        "6. Add a new column `Helpfulness` which is Numerator/Denominator, and drop the `HelpfulnessNumerator` and `HelpfulnessDenominator`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MyTArS34dcEM"
      },
      "source": [
        "df_clean['Helpfulness'] = df_clean.apply(lambda row: row['HelpfulnessNumerator']/row['HelpfulnessDenominator']\n",
        "               if row['HelpfulnessDenominator'] != 0 else 0, axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "zA4gOUggdcEM",
        "outputId": "8e9ff6aa-2bef-4b92-b279-22461a23fb1d"
      },
      "source": [
        "df_clean.drop(columns=['HelpfulnessNumerator','HelpfulnessDenominator'], inplace=True)\n",
        "df_clean"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ProductId</th>\n",
              "      <th>UserId</th>\n",
              "      <th>Score</th>\n",
              "      <th>Summary</th>\n",
              "      <th>Text</th>\n",
              "      <th>Helpfulness</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>B001PQTYN2</td>\n",
              "      <td>A3OTHWG8LLCLMU</td>\n",
              "      <td>5</td>\n",
              "      <td>Great</td>\n",
              "      <td>If you like a great , hot, sauce then buy this...</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>B00171APVA</td>\n",
              "      <td>A21BT40VZCCYT4</td>\n",
              "      <td>5</td>\n",
              "      <td>Healthy Dog Food</td>\n",
              "      <td>This is a very healthy dog food. Good for thei...</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>B004FD13RW</td>\n",
              "      <td>A1BPLP0BKERV</td>\n",
              "      <td>5</td>\n",
              "      <td>It is awesome.</td>\n",
              "      <td>My partner is very happy with the tea, and is ...</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>B009WSNWC4</td>\n",
              "      <td>AMP7K1O84DH1T</td>\n",
              "      <td>5</td>\n",
              "      <td>DELICIOUS</td>\n",
              "      <td>Purchased this product at a local store in NY ...</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>B00523NRVO</td>\n",
              "      <td>A2JDXKFZ0PFHKU</td>\n",
              "      <td>5</td>\n",
              "      <td>The perfect pop!</td>\n",
              "      <td>These lollipops are are well done, look exactl...</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>393574</th>\n",
              "      <td>B00004CXX9</td>\n",
              "      <td>AJH6LUC1UT1ON</td>\n",
              "      <td>5</td>\n",
              "      <td>FANTASTIC!</td>\n",
              "      <td>Beetlejuice is an excellent and funny movie. K...</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>393575</th>\n",
              "      <td>B00004RYGX</td>\n",
              "      <td>A344SMIA5JECGM</td>\n",
              "      <td>5</td>\n",
              "      <td>A modern day fairy tale</td>\n",
              "      <td>A twist of rumplestiskin captured on film, sta...</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>393576</th>\n",
              "      <td>B00004CXX9</td>\n",
              "      <td>AIUWLEQ1ADEG5</td>\n",
              "      <td>5</td>\n",
              "      <td>Entertainingl Funny!</td>\n",
              "      <td>Beetlejuice is a well written movie ..... ever...</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>393577</th>\n",
              "      <td>0006641040</td>\n",
              "      <td>AJ46FKXOVC7NR</td>\n",
              "      <td>5</td>\n",
              "      <td>This whole series is great way to spend time w...</td>\n",
              "      <td>I can remember seeing the show when it aired o...</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>393578</th>\n",
              "      <td>0006641040</td>\n",
              "      <td>ACITT7DI6IDDL</td>\n",
              "      <td>5</td>\n",
              "      <td>EVERY book is educational</td>\n",
              "      <td>this witty little book makes my son laugh at l...</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>393579 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         ProductId  ... Helpfulness\n",
              "0       B001PQTYN2  ...         1.0\n",
              "1       B00171APVA  ...         0.0\n",
              "2       B004FD13RW  ...         0.0\n",
              "3       B009WSNWC4  ...         0.0\n",
              "4       B00523NRVO  ...         0.0\n",
              "...            ...  ...         ...\n",
              "393574  B00004CXX9  ...         0.0\n",
              "393575  B00004RYGX  ...         0.5\n",
              "393576  B00004CXX9  ...         0.0\n",
              "393577  0006641040  ...         1.0\n",
              "393578  0006641040  ...         0.0\n",
              "\n",
              "[393579 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A_lWZ-32dcEM",
        "outputId": "205c65b2-ea3f-4c51-b866-01a590c8ec1b"
      },
      "source": [
        "# Drop the remaining null values in the Summary \n",
        "df_clean.dropna(inplace=True)\n",
        "df_clean.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ProductId      0\n",
              "UserId         0\n",
              "Score          0\n",
              "Summary        0\n",
              "Text           0\n",
              "Helpfulness    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "id": "UVwOYJROdcEQ",
        "outputId": "f95a2636-403b-47e8-c07d-4810963e3eb8"
      },
      "source": [
        "#Visulaize the attributes\n",
        "df_clean.hist(bins=50, figsize=(15,8))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K_t_7lkkdcEQ",
        "outputId": "d87f051d-65e8-4f33-df90-b8d3d77d99c7"
      },
      "source": [
        "# Check the correlation \n",
        "corr_matrix = df_clean.corr()['Score']\n",
        "corr_matrix.sort_values(ascending=False)[1:]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Helpfulness    0.037726\n",
              "Name: Score, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "c_lIx9kVdcER",
        "outputId": "6fea76f2-860e-4c6c-edfe-66660c1fdacd"
      },
      "source": [
        "# Count plot of the target\n",
        "ax = sns.countplot(x='Score',data=df_clean);\n",
        "ax.set_title('Countplot of the Target');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SFqzFApogaqy",
        "outputId": "9a38373c-a2d1-4be4-ffb7-110256c53861"
      },
      "source": [
        "df_clean.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 393576 entries, 0 to 393578\n",
            "Data columns (total 6 columns):\n",
            " #   Column       Non-Null Count   Dtype  \n",
            "---  ------       --------------   -----  \n",
            " 0   ProductId    393576 non-null  object \n",
            " 1   UserId       393576 non-null  object \n",
            " 2   Score        393576 non-null  int64  \n",
            " 3   Summary      393576 non-null  object \n",
            " 4   Text         393576 non-null  object \n",
            " 5   Helpfulness  393576 non-null  float64\n",
            "dtypes: float64(1), int64(1), object(4)\n",
            "memory usage: 31.0+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uwh_fMT-dcER"
      },
      "source": [
        "## Sentiment analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9yZfAov3dcES"
      },
      "source": [
        "* First we will get rid of `Score`= 3 reviews (neutral), and separate the remaining reviews into binary class (1 = positive, 0 = negative)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NVTCaT0BdcES"
      },
      "source": [
        "df_clean = df_clean[df_clean['Score'] != 3]\n",
        "X = df_clean['Text']\n",
        "\n",
        "X_num = df_clean[df_clean.columns.difference(['ProductId', 'ProfileId', 'UserId', 'Score'])]\n",
        "\n",
        "y_dict = {1:0, 2:0, 4:1, 5:1}\n",
        "y = df_clean['Score'].map(y_dict)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X1iVbV6qdcES"
      },
      "source": [
        "#c = CountVectorizer(stop_words = 'english')\n",
        "#tf = TfidfVectorizer(ngram_range=(1,2),stop_words = 'english')\n",
        "\n",
        "english_stemmer = Stemmer.Stemmer('en')\n",
        "\n",
        "class StemmedTfidfVectorizer(TfidfVectorizer):\n",
        "     def build_analyzer(self):\n",
        "         analyzer = super(TfidfVectorizer, self).build_analyzer()\n",
        "         return lambda doc: english_stemmer.stemWords(analyzer(doc))\n",
        "     \n",
        "tf = StemmedTfidfVectorizer(min_df=1, stop_words='english', analyzer='word', ngram_range=(1,2))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p1jaajRtdcET"
      },
      "source": [
        "def text_fit(X, y, c, clf_model):\n",
        "    \n",
        "    X_c = c.fit_transform(X)\n",
        "    print('# features: {}'.format(X_c.shape[1]))\n",
        "    print(X_c.shape)\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X_c, y, random_state=0)\n",
        "    print('# train records: {}'.format(X_train.shape[0]))\n",
        "    print('# test records: {}'.format(X_test.shape[0]))\n",
        "    \n",
        "    clf = clf_model.fit(X_train, y_train)\n",
        "    acc = clf.score(X_test, y_test)\n",
        "    print ('Model Accuracy: {}'.format(acc))\n",
        "  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B1gofbAPdcET"
      },
      "source": [
        "## SVM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iSbeWWiJdcEU"
      },
      "source": [
        "#text_fit(X, y, c, SVC())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uIhQnhVjedp-",
        "outputId": "97dd5268-a783-43f5-8186-4f515c65d78c"
      },
      "source": [
        "text_fit(X, y, tf, SVC())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "# features: 3369045\n",
            "(363827, 3369045)\n",
            "# train records: 272870\n",
            "# test records: 90957\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0LpSB0C5kX-K"
      },
      "source": [
        "#X_train, X_test, y_train, y_test = train_test_split(X_num, y_num, random_state=0)\n",
        "\n",
        "#svm = svm.SVC().fit(X_train, y_train)\n",
        "#accuracy = svm.score(X_test, y_test)\n",
        "#print('SVM accuracy: {}'.format(accuracy))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lqxPzz8mkUpj"
      },
      "source": [
        "## Hyperparameter tuning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AT8TQTH3BGtl"
      },
      "source": [
        "tuned_parameters = {\n",
        "    'C':  (np.arange(0.1,1,0.1)),\n",
        "        'gamma':[0.01,0.02,0.03,0.04,0.05],\n",
        "    \n",
        "     \"kernel\":[ 'rbf', 'linear']\n",
        "\n",
        "}\n",
        "svm_model= SVC()\n",
        "\n",
        "model_svm = GridSearchCV(svm_model, tuned_parameters,cv=10,scoring='accuracy')\n",
        "\n",
        "\n",
        "text_fit(X, y, tf, model_svm)\n",
        "\n",
        "print(model_svm.best_score_)\n",
        "print(model_svm.best_score_)\n",
        "print(model_svm.best_params_)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l8VuLvhdBGt4"
      },
      "source": [
        "y_pred= model_svm.predict(X_test)\n",
        "print(metrics.accuracy_score(y_pred,y_test))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "orxCmVll3M3w"
      },
      "source": [
        "# Task 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "itJTqmd73A6s"
      },
      "source": [
        "df = pd.read_csv('spam.csv', encoding='latin-1')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qv-wgOND3A6s",
        "outputId": "ec4949fb-8d02-4632-b319-1e496e018659"
      },
      "source": [
        "df.head(20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>v1</th>\n",
              "      <th>v2</th>\n",
              "      <th>Unnamed: 2</th>\n",
              "      <th>Unnamed: 3</th>\n",
              "      <th>Unnamed: 4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spam</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>spam</td>\n",
              "      <td>FreeMsg Hey there darling it's been 3 week's n...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>ham</td>\n",
              "      <td>Even my brother is not like to speak with me. ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>ham</td>\n",
              "      <td>As per your request 'Melle Melle (Oru Minnamin...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>spam</td>\n",
              "      <td>WINNER!! As a valued network customer you have...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>spam</td>\n",
              "      <td>Had your mobile 11 months or more? U R entitle...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>ham</td>\n",
              "      <td>I'm gonna be home soon and i don't want to tal...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>spam</td>\n",
              "      <td>SIX chances to win CASH! From 100 to 20,000 po...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>spam</td>\n",
              "      <td>URGENT! You have won a 1 week FREE membership ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>ham</td>\n",
              "      <td>I've been searching for the right words to tha...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>ham</td>\n",
              "      <td>I HAVE A DATE ON SUNDAY WITH WILL!!</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>spam</td>\n",
              "      <td>XXXMobileMovieClub: To use your credit, click ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>ham</td>\n",
              "      <td>Oh k...i'm watching here:)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>ham</td>\n",
              "      <td>Eh u remember how 2 spell his name... Yes i di...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>ham</td>\n",
              "      <td>Fine if thatåÕs the way u feel. ThatåÕs the wa...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>spam</td>\n",
              "      <td>England v Macedonia - dont miss the goals/team...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      v1                                                 v2 Unnamed: 2  \\\n",
              "0    ham  Go until jurong point, crazy.. Available only ...        NaN   \n",
              "1    ham                      Ok lar... Joking wif u oni...        NaN   \n",
              "2   spam  Free entry in 2 a wkly comp to win FA Cup fina...        NaN   \n",
              "3    ham  U dun say so early hor... U c already then say...        NaN   \n",
              "4    ham  Nah I don't think he goes to usf, he lives aro...        NaN   \n",
              "5   spam  FreeMsg Hey there darling it's been 3 week's n...        NaN   \n",
              "6    ham  Even my brother is not like to speak with me. ...        NaN   \n",
              "7    ham  As per your request 'Melle Melle (Oru Minnamin...        NaN   \n",
              "8   spam  WINNER!! As a valued network customer you have...        NaN   \n",
              "9   spam  Had your mobile 11 months or more? U R entitle...        NaN   \n",
              "10   ham  I'm gonna be home soon and i don't want to tal...        NaN   \n",
              "11  spam  SIX chances to win CASH! From 100 to 20,000 po...        NaN   \n",
              "12  spam  URGENT! You have won a 1 week FREE membership ...        NaN   \n",
              "13   ham  I've been searching for the right words to tha...        NaN   \n",
              "14   ham                I HAVE A DATE ON SUNDAY WITH WILL!!        NaN   \n",
              "15  spam  XXXMobileMovieClub: To use your credit, click ...        NaN   \n",
              "16   ham                         Oh k...i'm watching here:)        NaN   \n",
              "17   ham  Eh u remember how 2 spell his name... Yes i di...        NaN   \n",
              "18   ham  Fine if thatåÕs the way u feel. ThatåÕs the wa...        NaN   \n",
              "19  spam  England v Macedonia - dont miss the goals/team...        NaN   \n",
              "\n",
              "   Unnamed: 3 Unnamed: 4  \n",
              "0         NaN        NaN  \n",
              "1         NaN        NaN  \n",
              "2         NaN        NaN  \n",
              "3         NaN        NaN  \n",
              "4         NaN        NaN  \n",
              "5         NaN        NaN  \n",
              "6         NaN        NaN  \n",
              "7         NaN        NaN  \n",
              "8         NaN        NaN  \n",
              "9         NaN        NaN  \n",
              "10        NaN        NaN  \n",
              "11        NaN        NaN  \n",
              "12        NaN        NaN  \n",
              "13        NaN        NaN  \n",
              "14        NaN        NaN  \n",
              "15        NaN        NaN  \n",
              "16        NaN        NaN  \n",
              "17        NaN        NaN  \n",
              "18        NaN        NaN  \n",
              "19        NaN        NaN  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qHA0Gjx33A6s",
        "outputId": "9e1063f5-800c-4e8b-98ba-e56dfd2150bb"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5572 entries, 0 to 5571\n",
            "Data columns (total 5 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   v1          5572 non-null   object\n",
            " 1   v2          5572 non-null   object\n",
            " 2   Unnamed: 2  50 non-null     object\n",
            " 3   Unnamed: 3  12 non-null     object\n",
            " 4   Unnamed: 4  6 non-null      object\n",
            "dtypes: object(5)\n",
            "memory usage: 217.8+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K__ulNvn3A6t",
        "outputId": "5a89e16f-8bf7-4ba2-dbfb-4909660f1663"
      },
      "source": [
        "df['Unnamed: 4'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GNT:-)\"                                                     2\n",
              " Never comfort me with a lie\\\" gud ni8 and sweet dreams\"    1\n",
              " just Keep-in-touch\\\" gdeve..\"                              1\n",
              " CALL 2MWEN IM BK FRMCLOUD 9! J X\\\"\"                        1\n",
              " one day these two will become FREINDS FOREVER!\"            1\n",
              "Name: Unnamed: 4, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m4EJlp523A6t"
      },
      "source": [
        "## Assessing \n",
        "\n",
        "1. Column names are not expressive.\n",
        "\n",
        "\n",
        "2. The last three columns are useless."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bi3iipJn3A6t"
      },
      "source": [
        "## Cleaning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UBRsP53f3A6t"
      },
      "source": [
        "df_clean = df.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p_1y2t5u3A6t"
      },
      "source": [
        "# 1-Rename the columns\n",
        "df_clean.rename(columns={\"v1\" : 'spam', \"v2\" : 'message'}, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r0wiHUWt3A6t"
      },
      "source": [
        "# 2-Drop the last three columns\n",
        "df_clean.drop(columns=[\"Unnamed: 2\", \"Unnamed: 3\", \"Unnamed: 4\"], inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EM096HMP3A6u",
        "outputId": "18f45ae0-6b72-46f5-c06f-f24a3c810ad8"
      },
      "source": [
        "df_clean.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>spam</th>\n",
              "      <th>message</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spam</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   spam                                            message\n",
              "0   ham  Go until jurong point, crazy.. Available only ...\n",
              "1   ham                      Ok lar... Joking wif u oni...\n",
              "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3   ham  U dun say so early hor... U c already then say...\n",
              "4   ham  Nah I don't think he goes to usf, he lives aro..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8I0LZaiN3A6u"
      },
      "source": [
        "spam = pd.get_dummies(df_clean['spam'], drop_first=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_khvqmVz3A6u"
      },
      "source": [
        "df_clean['spam'] = spam"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2yBRNb0N3A6u",
        "outputId": "da8aa6fa-2d67-42b2-fd68-b3fb765c2dfe"
      },
      "source": [
        "df_clean"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>spam</th>\n",
              "      <th>message</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5567</th>\n",
              "      <td>1</td>\n",
              "      <td>This is the 2nd time we have tried 2 contact u...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5568</th>\n",
              "      <td>0</td>\n",
              "      <td>Will Ì_ b going to esplanade fr home?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5569</th>\n",
              "      <td>0</td>\n",
              "      <td>Pity, * was in mood for that. So...any other s...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5570</th>\n",
              "      <td>0</td>\n",
              "      <td>The guy did some bitching but I acted like i'd...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5571</th>\n",
              "      <td>0</td>\n",
              "      <td>Rofl. Its true to its name</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5572 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      spam                                            message\n",
              "0        0  Go until jurong point, crazy.. Available only ...\n",
              "1        0                      Ok lar... Joking wif u oni...\n",
              "2        1  Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3        0  U dun say so early hor... U c already then say...\n",
              "4        0  Nah I don't think he goes to usf, he lives aro...\n",
              "...    ...                                                ...\n",
              "5567     1  This is the 2nd time we have tried 2 contact u...\n",
              "5568     0              Will Ì_ b going to esplanade fr home?\n",
              "5569     0  Pity, * was in mood for that. So...any other s...\n",
              "5570     0  The guy did some bitching but I acted like i'd...\n",
              "5571     0                         Rofl. Its true to its name\n",
              "\n",
              "[5572 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8uGMk0pp3A6v"
      },
      "source": [
        "## Naive Bayes model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7lNdMmKJ3A6v"
      },
      "source": [
        "X = df_clean['message']\n",
        "y = df_clean['spam']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zNRothje3A6v"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lNbs6jnq3A6v"
      },
      "source": [
        "#Create a Gaussian Classifier\n",
        "model = MultinomialNB()\n",
        "\n",
        "# Train the model using the training sets\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "#Predict Output\n",
        "predicted= model.predict(X_test)\n",
        "print(\"Predicted Value:\", predicted)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n18hrsNm-_4M"
      },
      "source": [
        "* Task 3 [4 Points] :  BONUS\n",
        "    - Follow this [tutorial](https://www.mygreatlearning.com/blog/linear-discriminant-analysis-or-lda/) on **LDA** and apply what you learnt on the [Pokemon dataset](https://www.kaggle.com/rounakbanik/pokemon)\n",
        "\n",
        "    - **Hint**: You can use [this kernel for reference](https://www.kaggle.com/residentmario/linear-discriminant-analysis-with-pokemon-stats)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qej3v4z43ky2"
      },
      "source": [
        "# Task 3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TZ8SoLlP3A6w",
        "outputId": "e98dba30-c0b8-4ff2-d0ee-a3a64b02683a"
      },
      "source": [
        "df = pd.read_csv('pokemon.csv')\n",
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 801 entries, 0 to 800\n",
            "Data columns (total 41 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   abilities          801 non-null    object \n",
            " 1   against_bug        801 non-null    float64\n",
            " 2   against_dark       801 non-null    float64\n",
            " 3   against_dragon     801 non-null    float64\n",
            " 4   against_electric   801 non-null    float64\n",
            " 5   against_fairy      801 non-null    float64\n",
            " 6   against_fight      801 non-null    float64\n",
            " 7   against_fire       801 non-null    float64\n",
            " 8   against_flying     801 non-null    float64\n",
            " 9   against_ghost      801 non-null    float64\n",
            " 10  against_grass      801 non-null    float64\n",
            " 11  against_ground     801 non-null    float64\n",
            " 12  against_ice        801 non-null    float64\n",
            " 13  against_normal     801 non-null    float64\n",
            " 14  against_poison     801 non-null    float64\n",
            " 15  against_psychic    801 non-null    float64\n",
            " 16  against_rock       801 non-null    float64\n",
            " 17  against_steel      801 non-null    float64\n",
            " 18  against_water      801 non-null    float64\n",
            " 19  attack             801 non-null    int64  \n",
            " 20  base_egg_steps     801 non-null    int64  \n",
            " 21  base_happiness     801 non-null    int64  \n",
            " 22  base_total         801 non-null    int64  \n",
            " 23  capture_rate       801 non-null    object \n",
            " 24  classfication      801 non-null    object \n",
            " 25  defense            801 non-null    int64  \n",
            " 26  experience_growth  801 non-null    int64  \n",
            " 27  height_m           781 non-null    float64\n",
            " 28  hp                 801 non-null    int64  \n",
            " 29  japanese_name      801 non-null    object \n",
            " 30  name               801 non-null    object \n",
            " 31  percentage_male    703 non-null    float64\n",
            " 32  pokedex_number     801 non-null    int64  \n",
            " 33  sp_attack          801 non-null    int64  \n",
            " 34  sp_defense         801 non-null    int64  \n",
            " 35  speed              801 non-null    int64  \n",
            " 36  type1              801 non-null    object \n",
            " 37  type2              417 non-null    object \n",
            " 38  weight_kg          781 non-null    float64\n",
            " 39  generation         801 non-null    int64  \n",
            " 40  is_legendary       801 non-null    int64  \n",
            "dtypes: float64(21), int64(13), object(7)\n",
            "memory usage: 256.7+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PNwgeDHj3A6w",
        "outputId": "741843b6-d744-4fbe-ac71-1ddfaa4577ed"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['abilities', 'against_bug', 'against_dark', 'against_dragon',\n",
              "       'against_electric', 'against_fairy', 'against_fight', 'against_fire',\n",
              "       'against_flying', 'against_ghost', 'against_grass', 'against_ground',\n",
              "       'against_ice', 'against_normal', 'against_poison', 'against_psychic',\n",
              "       'against_rock', 'against_steel', 'against_water', 'attack',\n",
              "       'base_egg_steps', 'base_happiness', 'base_total', 'capture_rate',\n",
              "       'classfication', 'defense', 'experience_growth', 'height_m', 'hp',\n",
              "       'japanese_name', 'name', 'percentage_male', 'pokedex_number',\n",
              "       'sp_attack', 'sp_defense', 'speed', 'type1', 'type2', 'weight_kg',\n",
              "       'generation', 'is_legendary'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SWVfT3nB-_4N"
      },
      "source": [
        "df2= df[df['type2'].isnull()].loc[:, [ 'type1','abilities', 'against_bug', 'against_dark', 'against_dragon',\n",
        "       'against_electric', 'against_fairy', 'against_fight', 'against_fire',\n",
        "       'against_flying', 'against_ghost', 'against_grass', 'against_ground',\n",
        "       'against_ice', 'against_normal', 'against_poison', 'against_psychic',\n",
        "       'against_rock', 'against_steel', 'against_water', 'attack',\n",
        "       'base_egg_steps', 'base_happiness', 'base_total', 'capture_rate',\n",
        "       'classfication', 'defense', 'experience_growth', 'height_m', 'hp',\n",
        "       'japanese_name', 'name', 'percentage_male', 'pokedex_number',\n",
        "       'sp_attack', 'sp_defense', 'speed', 'weight_kg',\n",
        "       'generation', 'is_legendary']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-5JGp9HA3A6w",
        "outputId": "4d125bb7-158e-4799-ce24-fc22f9a07b93"
      },
      "source": [
        "df2.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['type1', 'abilities', 'against_bug', 'against_dark', 'against_dragon',\n",
              "       'against_electric', 'against_fairy', 'against_fight', 'against_fire',\n",
              "       'against_flying', 'against_ghost', 'against_grass', 'against_ground',\n",
              "       'against_ice', 'against_normal', 'against_poison', 'against_psychic',\n",
              "       'against_rock', 'against_steel', 'against_water', 'attack',\n",
              "       'base_egg_steps', 'base_happiness', 'base_total', 'capture_rate',\n",
              "       'classfication', 'defense', 'experience_growth', 'height_m', 'hp',\n",
              "       'japanese_name', 'name', 'percentage_male', 'pokedex_number',\n",
              "       'sp_attack', 'sp_defense', 'speed', 'weight_kg', 'generation',\n",
              "       'is_legendary'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f-WMFoqu3A6x"
      },
      "source": [
        "l = df['type1'].value_counts().index.tolist()\n",
        "i=0\n",
        "dict ={}\n",
        "for v in l:\n",
        "    dict[v] = i\n",
        "    i+=1\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "a4jp8dS532dC",
        "outputId": "4fdf340d-8466-451b-8679-2f1284fe2ac0"
      },
      "source": [
        "df2['type1'].replace(dict, inplace=True)\n",
        "df2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>type1</th>\n",
              "      <th>abilities</th>\n",
              "      <th>against_bug</th>\n",
              "      <th>against_dark</th>\n",
              "      <th>against_dragon</th>\n",
              "      <th>against_electric</th>\n",
              "      <th>against_fairy</th>\n",
              "      <th>against_fight</th>\n",
              "      <th>against_fire</th>\n",
              "      <th>against_flying</th>\n",
              "      <th>against_ghost</th>\n",
              "      <th>against_grass</th>\n",
              "      <th>against_ground</th>\n",
              "      <th>against_ice</th>\n",
              "      <th>against_normal</th>\n",
              "      <th>against_poison</th>\n",
              "      <th>against_psychic</th>\n",
              "      <th>against_rock</th>\n",
              "      <th>against_steel</th>\n",
              "      <th>against_water</th>\n",
              "      <th>attack</th>\n",
              "      <th>base_egg_steps</th>\n",
              "      <th>base_happiness</th>\n",
              "      <th>base_total</th>\n",
              "      <th>capture_rate</th>\n",
              "      <th>classfication</th>\n",
              "      <th>defense</th>\n",
              "      <th>experience_growth</th>\n",
              "      <th>height_m</th>\n",
              "      <th>hp</th>\n",
              "      <th>japanese_name</th>\n",
              "      <th>name</th>\n",
              "      <th>percentage_male</th>\n",
              "      <th>pokedex_number</th>\n",
              "      <th>sp_attack</th>\n",
              "      <th>sp_defense</th>\n",
              "      <th>speed</th>\n",
              "      <th>weight_kg</th>\n",
              "      <th>generation</th>\n",
              "      <th>is_legendary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>['Blaze', 'Solar Power']</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>52</td>\n",
              "      <td>5120</td>\n",
              "      <td>70</td>\n",
              "      <td>309</td>\n",
              "      <td>45</td>\n",
              "      <td>Lizard Pokémon</td>\n",
              "      <td>43</td>\n",
              "      <td>1059860</td>\n",
              "      <td>0.6</td>\n",
              "      <td>39</td>\n",
              "      <td>Hitokageヒトカゲ</td>\n",
              "      <td>Charmander</td>\n",
              "      <td>88.1</td>\n",
              "      <td>4</td>\n",
              "      <td>60</td>\n",
              "      <td>50</td>\n",
              "      <td>65</td>\n",
              "      <td>8.5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>['Blaze', 'Solar Power']</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>64</td>\n",
              "      <td>5120</td>\n",
              "      <td>70</td>\n",
              "      <td>405</td>\n",
              "      <td>45</td>\n",
              "      <td>Flame Pokémon</td>\n",
              "      <td>58</td>\n",
              "      <td>1059860</td>\n",
              "      <td>1.1</td>\n",
              "      <td>58</td>\n",
              "      <td>Lizardoリザード</td>\n",
              "      <td>Charmeleon</td>\n",
              "      <td>88.1</td>\n",
              "      <td>5</td>\n",
              "      <td>80</td>\n",
              "      <td>65</td>\n",
              "      <td>80</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0</td>\n",
              "      <td>['Torrent', 'Rain Dish']</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>48</td>\n",
              "      <td>5120</td>\n",
              "      <td>70</td>\n",
              "      <td>314</td>\n",
              "      <td>45</td>\n",
              "      <td>Tiny Turtle Pokémon</td>\n",
              "      <td>65</td>\n",
              "      <td>1059860</td>\n",
              "      <td>0.5</td>\n",
              "      <td>44</td>\n",
              "      <td>Zenigameゼニガメ</td>\n",
              "      <td>Squirtle</td>\n",
              "      <td>88.1</td>\n",
              "      <td>7</td>\n",
              "      <td>50</td>\n",
              "      <td>64</td>\n",
              "      <td>43</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0</td>\n",
              "      <td>['Torrent', 'Rain Dish']</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>63</td>\n",
              "      <td>5120</td>\n",
              "      <td>70</td>\n",
              "      <td>405</td>\n",
              "      <td>45</td>\n",
              "      <td>Turtle Pokémon</td>\n",
              "      <td>80</td>\n",
              "      <td>1059860</td>\n",
              "      <td>1.0</td>\n",
              "      <td>59</td>\n",
              "      <td>Kameilカメール</td>\n",
              "      <td>Wartortle</td>\n",
              "      <td>88.1</td>\n",
              "      <td>8</td>\n",
              "      <td>65</td>\n",
              "      <td>80</td>\n",
              "      <td>58</td>\n",
              "      <td>22.5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0</td>\n",
              "      <td>['Torrent', 'Rain Dish']</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>103</td>\n",
              "      <td>5120</td>\n",
              "      <td>70</td>\n",
              "      <td>630</td>\n",
              "      <td>45</td>\n",
              "      <td>Shellfish Pokémon</td>\n",
              "      <td>120</td>\n",
              "      <td>1059860</td>\n",
              "      <td>1.6</td>\n",
              "      <td>79</td>\n",
              "      <td>Kamexカメックス</td>\n",
              "      <td>Blastoise</td>\n",
              "      <td>88.1</td>\n",
              "      <td>9</td>\n",
              "      <td>135</td>\n",
              "      <td>115</td>\n",
              "      <td>78</td>\n",
              "      <td>85.5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>781</th>\n",
              "      <td>13</td>\n",
              "      <td>['Bulletproof', 'Soundproof', 'Overcoat']</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>55</td>\n",
              "      <td>10240</td>\n",
              "      <td>70</td>\n",
              "      <td>300</td>\n",
              "      <td>45</td>\n",
              "      <td>Scaly Pokémon</td>\n",
              "      <td>65</td>\n",
              "      <td>1250000</td>\n",
              "      <td>0.6</td>\n",
              "      <td>45</td>\n",
              "      <td>Jyarakoジャラコ</td>\n",
              "      <td>Jangmo-o</td>\n",
              "      <td>50.0</td>\n",
              "      <td>782</td>\n",
              "      <td>45</td>\n",
              "      <td>45</td>\n",
              "      <td>45</td>\n",
              "      <td>29.7</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>788</th>\n",
              "      <td>4</td>\n",
              "      <td>['Unaware']</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>29</td>\n",
              "      <td>30720</td>\n",
              "      <td>0</td>\n",
              "      <td>200</td>\n",
              "      <td>45</td>\n",
              "      <td>Nebula Pokémon</td>\n",
              "      <td>31</td>\n",
              "      <td>1250000</td>\n",
              "      <td>0.2</td>\n",
              "      <td>43</td>\n",
              "      <td>Cosmogコスモッグ</td>\n",
              "      <td>Cosmog</td>\n",
              "      <td>NaN</td>\n",
              "      <td>789</td>\n",
              "      <td>29</td>\n",
              "      <td>31</td>\n",
              "      <td>37</td>\n",
              "      <td>0.1</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>789</th>\n",
              "      <td>4</td>\n",
              "      <td>['Sturdy']</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>29</td>\n",
              "      <td>30720</td>\n",
              "      <td>0</td>\n",
              "      <td>400</td>\n",
              "      <td>45</td>\n",
              "      <td>Protostar Pokémon</td>\n",
              "      <td>131</td>\n",
              "      <td>1250000</td>\n",
              "      <td>0.1</td>\n",
              "      <td>43</td>\n",
              "      <td>Cosmovumコスモウム</td>\n",
              "      <td>Cosmoem</td>\n",
              "      <td>NaN</td>\n",
              "      <td>790</td>\n",
              "      <td>29</td>\n",
              "      <td>131</td>\n",
              "      <td>37</td>\n",
              "      <td>999.9</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>795</th>\n",
              "      <td>7</td>\n",
              "      <td>['Beast Boost']</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>89</td>\n",
              "      <td>30720</td>\n",
              "      <td>0</td>\n",
              "      <td>570</td>\n",
              "      <td>30</td>\n",
              "      <td>Glowing Pokémon</td>\n",
              "      <td>71</td>\n",
              "      <td>1250000</td>\n",
              "      <td>3.8</td>\n",
              "      <td>83</td>\n",
              "      <td>Denjyumokuデンジュモク</td>\n",
              "      <td>Xurkitree</td>\n",
              "      <td>NaN</td>\n",
              "      <td>796</td>\n",
              "      <td>173</td>\n",
              "      <td>71</td>\n",
              "      <td>83</td>\n",
              "      <td>100.0</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>799</th>\n",
              "      <td>4</td>\n",
              "      <td>['Prism Armor']</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>107</td>\n",
              "      <td>30720</td>\n",
              "      <td>0</td>\n",
              "      <td>600</td>\n",
              "      <td>3</td>\n",
              "      <td>Prism Pokémon</td>\n",
              "      <td>101</td>\n",
              "      <td>1250000</td>\n",
              "      <td>2.4</td>\n",
              "      <td>97</td>\n",
              "      <td>Necrozmaネクロズマ</td>\n",
              "      <td>Necrozma</td>\n",
              "      <td>NaN</td>\n",
              "      <td>800</td>\n",
              "      <td>127</td>\n",
              "      <td>89</td>\n",
              "      <td>79</td>\n",
              "      <td>230.0</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>384 rows × 40 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     type1                                  abilities  ...  generation  is_legendary\n",
              "3        5                   ['Blaze', 'Solar Power']  ...           1             0\n",
              "4        5                   ['Blaze', 'Solar Power']  ...           1             0\n",
              "6        0                   ['Torrent', 'Rain Dish']  ...           1             0\n",
              "7        0                   ['Torrent', 'Rain Dish']  ...           1             0\n",
              "8        0                   ['Torrent', 'Rain Dish']  ...           1             0\n",
              "..     ...                                        ...  ...         ...           ...\n",
              "781     13  ['Bulletproof', 'Soundproof', 'Overcoat']  ...           7             0\n",
              "788      4                                ['Unaware']  ...           7             1\n",
              "789      4                                 ['Sturdy']  ...           7             1\n",
              "795      7                            ['Beast Boost']  ...           7             1\n",
              "799      4                            ['Prism Armor']  ...           7             1\n",
              "\n",
              "[384 rows x 40 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bbgibDgd-_4O",
        "outputId": "117e2185-674e-47fa-b2c2-f4e5808c82b5"
      },
      "source": [
        "# Check the correlation \n",
        "corr_matrix = df2.corr()['type1']\n",
        "corr_matrix.sort_values(ascending=False)[1:]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "against_steel        0.534773\n",
              "against_fairy        0.321718\n",
              "against_ghost        0.258474\n",
              "against_water        0.243953\n",
              "against_ground       0.221639\n",
              "against_psychic      0.195306\n",
              "pokedex_number       0.145905\n",
              "sp_defense           0.132981\n",
              "attack               0.112523\n",
              "against_ice          0.112255\n",
              "against_fire         0.109909\n",
              "generation           0.108543\n",
              "defense              0.083654\n",
              "weight_kg            0.076177\n",
              "experience_growth    0.067719\n",
              "base_egg_steps       0.067570\n",
              "base_total           0.055445\n",
              "is_legendary         0.034319\n",
              "sp_attack            0.020402\n",
              "height_m             0.005764\n",
              "against_rock        -0.016860\n",
              "percentage_male     -0.039548\n",
              "hp                  -0.058667\n",
              "speed               -0.069707\n",
              "against_poison      -0.071793\n",
              "against_flying      -0.092113\n",
              "base_happiness      -0.092623\n",
              "against_fight       -0.181213\n",
              "against_dragon      -0.187586\n",
              "against_dark        -0.216956\n",
              "against_normal      -0.248282\n",
              "against_grass       -0.296114\n",
              "against_bug         -0.359072\n",
              "against_electric    -0.379578\n",
              "Name: type1, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JaJcX5R3-_4O",
        "outputId": "6c41445b-ec3a-4671-ecd2-b381e8a7c6cc"
      },
      "source": [
        "#Get the highest correlated features\n",
        "print(corr_matrix[corr_matrix <-0.2].index);\n",
        "print(corr_matrix[corr_matrix > 0.2].index);\n",
        "\n",
        "col =  []\n",
        "\n",
        "col = corr_matrix[ corr_matrix <-0.2].index.tolist() + corr_matrix[corr_matrix> 0.2].index.tolist()\n",
        "col"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['against_bug', 'against_dark', 'against_electric', 'against_grass',\n",
            "       'against_normal'],\n",
            "      dtype='object')\n",
            "Index(['type1', 'against_fairy', 'against_ghost', 'against_ground',\n",
            "       'against_steel', 'against_water'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['against_bug',\n",
              " 'against_dark',\n",
              " 'against_electric',\n",
              " 'against_grass',\n",
              " 'against_normal',\n",
              " 'type1',\n",
              " 'against_fairy',\n",
              " 'against_ghost',\n",
              " 'against_ground',\n",
              " 'against_steel',\n",
              " 'against_water']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n5y0ShgN5osJ"
      },
      "source": [
        "We will use these features only in our model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0zFfqUNS307g"
      },
      "source": [
        "df_clean = df2[col]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZEp_PbIN7fGe",
        "outputId": "3dc4e49f-4dc7-4e39-a355-2c37423e30ab"
      },
      "source": [
        "df_clean.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 384 entries, 3 to 799\n",
            "Data columns (total 11 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   against_bug       384 non-null    float64\n",
            " 1   against_dark      384 non-null    float64\n",
            " 2   against_electric  384 non-null    float64\n",
            " 3   against_grass     384 non-null    float64\n",
            " 4   against_normal    384 non-null    float64\n",
            " 5   type1             384 non-null    int64  \n",
            " 6   against_fairy     384 non-null    float64\n",
            " 7   against_ghost     384 non-null    float64\n",
            " 8   against_ground    384 non-null    float64\n",
            " 9   against_steel     384 non-null    float64\n",
            " 10  against_water     384 non-null    float64\n",
            "dtypes: float64(10), int64(1)\n",
            "memory usage: 36.0 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "id": "MrqUw1xP7hg5",
        "outputId": "69c15e5e-f0ed-4194-da2a-3ccd0d71d43c"
      },
      "source": [
        "df_clean.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>against_bug</th>\n",
              "      <th>against_dark</th>\n",
              "      <th>against_electric</th>\n",
              "      <th>against_grass</th>\n",
              "      <th>against_normal</th>\n",
              "      <th>type1</th>\n",
              "      <th>against_fairy</th>\n",
              "      <th>against_ghost</th>\n",
              "      <th>against_ground</th>\n",
              "      <th>against_steel</th>\n",
              "      <th>against_water</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   against_bug  against_dark  ...  against_steel  against_water\n",
              "3          0.5           1.0  ...            0.5            2.0\n",
              "4          0.5           1.0  ...            0.5            2.0\n",
              "6          1.0           1.0  ...            0.5            0.5\n",
              "7          1.0           1.0  ...            0.5            0.5\n",
              "8          1.0           1.0  ...            0.5            0.5\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UjCZgTT-8uiw"
      },
      "source": [
        "X = df_clean.drop(columns='type1')\n",
        "y = df_clean['type1']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m30Ntv0a75hr"
      },
      "source": [
        "lda = LinearDiscriminantAnalysis()\n",
        "lda.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L2fN8e9n9eFf"
      },
      "source": [
        "y_pred=lda.predict(X_test)\n",
        "print('Accuracy Score RBF:')\n",
        "print(metrics.accuracy_score(y_test,y_pred))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0YFEq582-Anv"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VX8R7BRdkTgO"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wrZMrvo7kWlh"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q945G_z8zekw"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}