Lab_6_Ensemble_Learning.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 6: Ensemble Methods </h2>\n",
        "<h3 align=\"center\">Eng. Ahmed Wael</h3>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "puhAklCqMRaS"
      },
      "source": [
        "# Lab Objectives:\n",
        "\n",
        "- Understanding the idea of Ensemble Learning\n",
        "\n",
        "\n",
        "- Understanding the idea of Bootstraping\n",
        "\n",
        "\n",
        "- Understanding the idea of Bagging\n",
        "\n",
        "\n",
        "- What do we mean by Out-Of-Bag (OOB) Error\n",
        "\n",
        "\n",
        "- Implementing Random Forests from scratch to understand the last three points better\n",
        "\n",
        "\n",
        "- Discuss the different hyberparamaters of the RF\n",
        "\n",
        "\n",
        "- Understaing the idea of Boosting\n",
        "\n",
        "\n",
        "- Discuss AdaBoost, one of the most famous Boosting Algorithms"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2osRGlRpMRaS"
      },
      "source": [
        "# Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZHViQPQnMRaS"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "from math import log2, sqrt\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn import metrics\n",
        "from sklearn import tree\n",
        "from sklearn import preprocessing\n",
        "import matplotlib.pyplot as plt\n",
        "import six\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.tree import export_graphviz\n",
        "from six import StringIO  \n",
        "from IPython.display import Image  \n",
        "import pydotplus\n",
        "from tqdm.notebook import tqdm_notebook as tqdm\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "from decision_tree import MyDecisionTree ## Our implementation from lab 5\n",
        "\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.ensemble import AdaBoostRegressor\n",
        "from sklearn.metrics import r2_score\n",
        "import seaborn as sns\n",
        "from matplotlib import rcParams\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BX8LMID9MRaS"
      },
      "source": [
        "# Exploring the Dataset : \n",
        "We are going you use the same dataset from the last lab"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7kQFdBUkMRaS",
        "outputId": "36f38c84-6fff-4f46-9eff-ad5431845254"
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      "source": [
        "data = pd.read_csv(\"adult_data.csv\")\n",
        "\n",
        "categorical = ['workclass', 'education', 'marital_status', 'occupation', \n",
        "                   'relationship', 'c1', 'c2', 'c3']\n",
        "numerical = ['age', 'education_num','capital_gain', 'capital_loss',\n",
        "                'hours_per_week']\n",
        " \n",
        "X = pd.concat([data[categorical], data[numerical]], axis=1) ## Just sort them, no need for encoding\n",
        "y = data['eligible']\n",
        "X_train , X_test , y_train , y_test = train_test_split(X,y,test_size = 0.2 , random_state = 10)\n",
        "X_train , X_test , y_train , y_test = X_train.to_numpy() , X_test.to_numpy() , y_train.to_numpy() , y_test.to_numpy()\n",
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>workclass</th>\n",
              "      <th>state_sample</th>\n",
              "      <th>education</th>\n",
              "      <th>education_num</th>\n",
              "      <th>marital_status</th>\n",
              "      <th>occupation</th>\n",
              "      <th>relationship</th>\n",
              "      <th>c1</th>\n",
              "      <th>c2</th>\n",
              "      <th>capital_gain</th>\n",
              "      <th>capital_loss</th>\n",
              "      <th>hours_per_week</th>\n",
              "      <th>c3</th>\n",
              "      <th>eligible</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>39</td>\n",
              "      <td>State-gov</td>\n",
              "      <td>77516</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Adm-clerical</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>c14</td>\n",
              "      <td>c21</td>\n",
              "      <td>2174</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>c337</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50</td>\n",
              "      <td>Self-emp-not-inc</td>\n",
              "      <td>83311</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Husband</td>\n",
              "      <td>c14</td>\n",
              "      <td>c21</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>c337</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>38</td>\n",
              "      <td>Private</td>\n",
              "      <td>215646</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Handlers-cleaners</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>c14</td>\n",
              "      <td>c21</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>c337</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>53</td>\n",
              "      <td>Private</td>\n",
              "      <td>234721</td>\n",
              "      <td>11th</td>\n",
              "      <td>7</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Handlers-cleaners</td>\n",
              "      <td>Husband</td>\n",
              "      <td>c12</td>\n",
              "      <td>c21</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>c337</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>28</td>\n",
              "      <td>Private</td>\n",
              "      <td>338409</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Wife</td>\n",
              "      <td>c12</td>\n",
              "      <td>c20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>c34</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "   age          workclass  state_sample   education  education_num  \\\n",
              "0   39          State-gov         77516   Bachelors             13   \n",
              "1   50   Self-emp-not-inc         83311   Bachelors             13   \n",
              "2   38            Private        215646     HS-grad              9   \n",
              "3   53            Private        234721        11th              7   \n",
              "4   28            Private        338409   Bachelors             13   \n",
              "\n",
              "        marital_status          occupation    relationship   c1   c2  \\\n",
              "0        Never-married        Adm-clerical   Not-in-family  c14  c21   \n",
              "1   Married-civ-spouse     Exec-managerial         Husband  c14  c21   \n",
              "2             Divorced   Handlers-cleaners   Not-in-family  c14  c21   \n",
              "3   Married-civ-spouse   Handlers-cleaners         Husband  c12  c21   \n",
              "4   Married-civ-spouse      Prof-specialty            Wife  c12  c20   \n",
              "\n",
              "   capital_gain  capital_loss  hours_per_week    c3  eligible  \n",
              "0          2174             0              40  c337         0  \n",
              "1             0             0              13  c337         0  \n",
              "2             0             0              40  c337         0  \n",
              "3             0             0              40  c337         0  \n",
              "4             0             0              40   c34         0  "
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      "metadata": {
        "id": "Vo2lywrAyHAL"
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      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LF4OOYJIMRaT"
      },
      "source": [
        "# What is Ensemble Learning?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ctvmw0DMRaT"
      },
      "source": [
        "- **Combine many mediocre (weak) models in a one super model.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6AbabdWWMRaT"
      },
      "source": [
        "## Ensemble Learning [Example](https://www.analyticsvidhya.com/blog/2015/08/introduction-ensemble-learning/)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QRBbNDBWMRaT"
      },
      "source": [
        "- I want to invest in some company, but I am not sure about its performance though. \n",
        "\n",
        "\n",
        "- So, I look for advice on whether the stock price will increase more than 6% per annum or not? \n",
        "\n",
        "\n",
        "- I decide to approach various experts having **diverse** domain experience:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SHoVOUh_MRaT"
      },
      "source": [
        "1. **Employee of the Company:** \n",
        "    - *Good At :*\n",
        "    \n",
        "        - Knows the internal functionality of the company\n",
        "        \n",
        "        - Has the insider information about the functionality of the firm.\n",
        "    \n",
        "    - *Bad At :*\n",
        "        - Lacks a broader perspective on how are competitors innovating.\n",
        "   \n",
        "        - Lacks on how is the technology evolving and what will be the impact of this evolution on the Company's product. \n",
        "   \n",
        "    - *Accuracy:*\n",
        "        - In the past, he has been right 70% times.\n",
        "\n",
        "\n",
        "2. **Financial Advisor of the Company:** \n",
        "    - *Good At :*\n",
        "    \n",
        "        - Has a broader perspective on how companies strategy will fair of in this competitive environment.\n",
        "            \n",
        "    - *Bad At :*\n",
        "        - Lacks a view on how the company’s internal policies are fairing off\n",
        "        \n",
        "    - *Accuracy:*\n",
        "        - In the past, he has been right 75% times.\n",
        "\n",
        "\n",
        "3. **Stock Market Trader:**\n",
        "    - *Good At :*\n",
        "    \n",
        "        - Observed the company’s stock price over past 3 years.\n",
        "        - Knows the seasonality trends and how the overall market is performing.\n",
        "        - Developed a strong intuition on how stocks might vary over time.\n",
        "    - *Bad At :*\n",
        "        - Doesn't now the internal of the company.\n",
        "        \n",
        "    - *Accuracy:*\n",
        "        - In the past, he has been right 70% times.\n",
        "\n",
        "\n",
        "4. **Employee of a competitor:**\n",
        "    - *Good At :*\n",
        "    \n",
        "        - Knows the internal functionality of the competitor firms\n",
        "        - Aware of certain changes which are yet to be brought.\n",
        "    - *Bad At :*\n",
        "        - Lacks a sight of company in focus and the external factors which can relate the growth of competitor with the company of subject.\n",
        "        \n",
        "    - *Accuracy:*\n",
        "        - In the past, he has been right 60% times.\n",
        "\n",
        "\n",
        "5. **Market Research team in same segment:**\n",
        "    - *Good At :*\n",
        "    \n",
        "        - analyzes the customer preference of the company's product over others and how is this changing with time.\n",
        "        - Aware of certain changes which are yet to be brought.\n",
        "    - *Bad At :*\n",
        "        - Unaware of the changes the company will bring because of alignment to its own goals, because he deals with customer side.\n",
        "        \n",
        "    - *Accuracy:*\n",
        "        - In the past, he has been right 75% times.\n",
        "\n",
        "\n",
        "\n",
        "6. **Social Media Expert:**\n",
        "    - *Good At :*\n",
        "    \n",
        "        - Can help us understand how the company have positioned its products in the market\n",
        "        - Can help us understand how are the sentiment of customers changing over time towards company.\n",
        "    - *Bad At :*\n",
        "        - Unaware of any kind of details beyond digital marketing.\n",
        "        \n",
        "    - *Accuracy:*\n",
        "        - In the past, he has been right 65% times."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "stvf3Qq2MRaT"
      },
      "source": [
        "## What is the accuracy of the 6 experts combined?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P-zXQm5fMRaT"
      },
      "source": [
        "$$1 - \\prod Error(expert) $$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vyM90f4bMRaT"
      },
      "source": [
        "$$1 - (30\\% \\cdot 25\\% \\cdot 30\\% \\cdot 40\\% \\cdot 25\\% \\cdot 35\\%) = 1 - 0.07875 = 99.92125\\% $$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A_yqm1e0MRaT"
      },
      "source": [
        "## What is the problem in this approach?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-W0WU-yYMRaT"
      },
      "source": [
        "- We assume that all the predictions are **completely** independent on each other. Which doesn't make sense and not realistic at all."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pozg3dIFMRaT"
      },
      "source": [
        "## How to solve this problem?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wLgzeMJZMRaT"
      },
      "source": [
        "- **Bagging:** **Parallel** training of **different** models using **overlapping** training sets. Also called *Bootstrap AGgregation* \n",
        "\n",
        "\n",
        "- **Boosting:** **Sequential** training by iteratively **re-weighting** training examples such that each model focuses on a different subset of **hard examples**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5e-fwJ_RMRaT"
      },
      "source": [
        "# Bootstrap Sampling:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fatew7wlMRaT"
      },
      "source": [
        "## What does it address?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vL4dAXBfMRaT"
      },
      "source": [
        "- Bootstrap address a very famous challenge in statistics.\n",
        "\n",
        "\n",
        "- We only have sample of a population, but we want to collect information about the WHOLE population.\n",
        "\n",
        "\n",
        "- How to make sure that this sample represent the whole population? It the mean of the sample is a good-enough approximation for the mean of the population?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SjC4JuI7MRaT"
      },
      "source": [
        "## What is it?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ba3-8o7MRaT"
      },
      "source": [
        "- Instead of estimating the statistic - mean for example - only once, we do this many times.\n",
        "\n",
        "\n",
        "- We do it using re-sampling **with replacement** of the original sample."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d3rq8IxjMRaT"
      },
      "source": [
        "<img src = \"https://miro.medium.com/max/875/1*iH5w0MBdiOlxDOCX6nmqqw.png\">"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1WD7Oz8bMRaT"
      },
      "source": [
        "<img src = \"https://miro.medium.com/max/1250/1*y42mIxqot08pZq8_ZoFiXw.png\">"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "quPYP0k5MRaT"
      },
      "source": [
        "# Bagging - *Bootstrap Aggregation* - :"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kbLDr42zMRaT"
      },
      "source": [
        "## What is it?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zwre4uQlMRaT"
      },
      "source": [
        "Its basic idea is to create different models using **different bootstrapped**\n",
        "training sets and **average** their prediction to produce the final output\n",
        "of the model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o41uloLtMRaT"
      },
      "source": [
        "## What is it good for?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K4PAt0DRMRaT"
      },
      "source": [
        "- Reducing the variance of the model.\n",
        "\n",
        "\n",
        "- Thus, reducing overfitting\n",
        "\n",
        "\n",
        "- Outliers are likely omitted in some of the training bootstrap samples. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ApGVWcNHMRaT"
      },
      "source": [
        "<img src = \"https://hudsonthames.org/wp-content/uploads/2019/09/bagging-1.png\" >"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ttvBq3jOMRaT"
      },
      "source": [
        "## Bagging Steps:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YyssgCXoMRaT"
      },
      "source": [
        "1. Generate B different bootstrapped training data sets. \n",
        "\n",
        "\n",
        "2. Train our ML method on the *b* bootstrapped training set\n",
        "\n",
        "\n",
        "3. $$\\hat{f}(x) = \\frac{1}{B}\\sum\\hat{f}^b(x)$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5NtytzUFMRaT"
      },
      "source": [
        "## How to Calculate the test error in Bagging?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vZk-HK8xMRaT"
      },
      "source": [
        "- By using the **Out-of-Bag Error (OOB) :**  The mean prediction error on each training sample *x*, using only the model that did not have *x* in their bootstrapped training set."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WcV0CY-fMRaT"
      },
      "source": [
        "- No need to use cross-validation or hold-out samples in order to get an unbiased error estimation."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qao_QDwAMRaT"
      },
      "source": [
        "- Because, in Bagging, the error estimation takes place internally"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JKWEqrMKMRaT"
      },
      "source": [
        "<img src = https://miro.medium.com/max/875/1*HojoYXBU_DTnMuCatCnQaQ.png>\n",
        "\n",
        "                Training                                              Testing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "skTobVllMRaT"
      },
      "source": [
        "**What is the OOB here?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QZUGcmmHMRaT"
      },
      "source": [
        "- We have 15 points in the test set, that are NOT in the training set, right?\n",
        "\n",
        "\n",
        "- The model classified 4 of these 15 incorrectly. \n",
        "\n",
        "\n",
        "- So, the accuracy is 11/15*100 = 73.33%\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9PFIaOv-MRaT"
      },
      "source": [
        "- Thus, we can say that the OOB estimate is nothing more than the mean estimate of the base algorithms on those ~37% of inputs that were left out of training."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5YsxkO_TMRaT"
      },
      "source": [
        "## Variable Importance Measures:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I9iEoTb4MRaT"
      },
      "source": [
        "- Bagging improves prediction accuracy at the expense on interpretability.\n",
        "\n",
        "\n",
        "- For example, if we use it with decision trees, it is no longer clear which features are the most important.\n",
        "\n",
        "\n",
        "- So, we calculate an overall summary of importance using Variable Importance Measures such as GINI index and RSS."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IxnslHI0MRaT"
      },
      "source": [
        "# Random Forests"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wEtbzhWjMRaT"
      },
      "source": [
        "A Random Forest is slightly modified Bagging algorithm.\n",
        "\n",
        "\n",
        "It is basically a collection of **decorrelated** decision trees, built as follows"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cCfygtWVMRaT"
      },
      "source": [
        "- For every tree: \n",
        "\n",
        "    1. We subsample the examples with replacement **Bootstraping**\n",
        "\n",
        "    2. For each subsample, we choose attributes **at random**. We randomly pick features in each split. WHY?\n",
        "    \n",
        "    3. We build the tree ONLY on these random features and with the current subsample.\n",
        "\n",
        "    \n",
        "- Classification for a random forest is then done by taking a majority vote of the classifications yielded by each tree in the forest after it classifies an example.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VuoCE182MRaT"
      },
      "source": [
        "## Random Forest Implementation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lEqxMV_BMRaT"
      },
      "source": [
        "class RandomForest(object):\n",
        "    def __init__(self, n_estimators=50, max_depth=None, max_features=0.7):\n",
        "        self.n_estimators = n_estimators ## how many estimators we are going to use?\n",
        "        self.max_depth = max_depth ## Max depth \n",
        "        self.max_features = max_features ## the percentage of the RANDOM features we are going to use\n",
        "        self.bootstraps_row_indices = [] ## Which examples are in the current bootstrap\n",
        "        self.feature_indices = [] ## Keeping track of features indices\n",
        "        self.out_of_bag = [] ## To calculate the out of bag error\n",
        "        \n",
        "        decision_trees = []\n",
        "        for i in range(n_estimators):\n",
        "            decision_trees.append(MyDecisionTree(max_depth=max_depth)) ## Call our DT class\n",
        "        \n",
        "        self.decision_trees = decision_trees ## Our random forest\n",
        "        \n",
        "        ## OR  self.decision_trees = [MyDecisionTree(max_depth=max_depth) for i in range(n_estimators)]\n",
        "        \n",
        "    def _bootstrapping(self, num_training, num_features): ## _ is similar to private in other programming language\n",
        "        \"\"\"\n",
        "        - INPUT : \n",
        "            num_training: how many training examples in this bootstrap\n",
        "            num_features: how many features in this bootstrap\n",
        "            \n",
        "        - OUTPUT :\n",
        "        - row_idx: the row indices corresponding to the row locations of the selected samples in the original dataset.\n",
        "        - col_idx: the column indices corresponding to the column locations of the selected features  \n",
        "                   in the original feature list.\n",
        "        \n",
        "        \n",
        "        \n",
        "        - Randomly select a sample dataset of size num_training with replacement from the original dataset. \n",
        "        - Randomly select certain number of features (num_features denotes the total number of features in X,\n",
        "          without replacement from the total number of features.\n",
        "        \"\"\" \n",
        "        \n",
        "        sample_size = list(range(num_training))\n",
        "        row_idx = np.random.choice(sample_size,num_training) # Create random row indices\n",
        "        col_idx = np.random.permutation(num_features)[:int(num_features*self.max_features)] ## Permutation ( with replacement)\n",
        "        return row_idx, col_idx\n",
        "            \n",
        "    def bootstrapping(self, num_training, num_features):\n",
        "        \"\"\"\n",
        "        Initializing the bootstap datasets for each tree\n",
        "        \"\"\"\n",
        "        \n",
        "        for i in range(self.n_estimators):\n",
        "            total = set(list(range(num_training))) ## we use a set to get the unique elements\n",
        "            row_idx, col_idx = self._bootstrapping(num_training, num_features)\n",
        "            out_of_bag_examples = total - set(row_idx) ## again we use a set. Subtract the row indices from the total\n",
        "            self.bootstraps_row_indices.append(row_idx)\n",
        "            self.feature_indices.append(col_idx)\n",
        "            self.out_of_bag.append(out_of_bag_examples) ## total is used for the OOB \n",
        "            \n",
        "            \n",
        "    def fit(self, X, y):\n",
        "        \"\"\"\n",
        "        Train decision trees using the bootstrapped datasets.\n",
        "        \"\"\"\n",
        "        \n",
        "        num_training, num_features = X.shape\n",
        "        self.bootstrapping(num_training,num_features) ## initialize the bootstrapping\n",
        "        for i in range((self.n_estimators)): ## loop over the trees \n",
        "            current_bootstraps_row_indices = self.bootstraps_row_indices[i]\n",
        "            current_feature_indices = self.feature_indices[i]\n",
        "            current_X = X_train[current_bootstraps_row_indices[:,np.newaxis], current_feature_indices] ## data for this tree\n",
        "            current_y = y[current_bootstraps_row_indices]\n",
        "            current_dt = self.decision_trees[i]\n",
        "            current_dt.fit(current_X,current_y, 0) ## 0 for the initial depth\n",
        "            print(\"Current Tree to fit : \" ,i+1) ## Which tree we are using\n",
        "            \n",
        "            \n",
        "    def OOB_score(self, X, y):\n",
        "        \"\"\"\n",
        "        Calculate the OOB score\n",
        "        \"\"\"\n",
        "        \n",
        "        accuracy = []\n",
        "        for i in range(len(X)): ## Loop over the full dataset\n",
        "            predictions = []\n",
        "            for t in range(self.n_estimators): ## loop over each decision tree\n",
        "                if i in self.out_of_bag[t]: ## the data that is NOT used in the current tree\n",
        "                    predictions.append(self.decision_trees[t].predict(X[i][self.feature_indices[t]])) ## Predict\n",
        "            if len(predictions) > 0:\n",
        "                accuracy.append(np.sum(predictions == y[i]) / float(len(predictions))) ## Majority voting\n",
        "        return np.mean(accuracy) ## Total accuracy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0TLv1jGJyHAb"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o8_dylM6MRaU"
      },
      "source": [
        "## Evaluate the Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m9ggwUCkMRaU",
        "scrolled": true,
        "outputId": "c5d6b5f8-acce-452a-b03a-86f5b9cff7e0"
      },
      "source": [
        "n_estimators = 30\n",
        "max_depth = 5\n",
        "max_features = 0.7\n",
        "\n",
        "random_forest = RandomForest(n_estimators, max_depth, max_features)\n",
        "\n",
        "random_forest.fit(X_train, y_train)\n",
        "accuracy=random_forest.OOB_score(X_test, y_test)\n",
        "\n",
        "print(\"accuracy: %.4f\" % accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'max_depth' is not defined",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[1;32m<ipython-input-5-6ecced181e8a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mrandom_forest\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mRandomForest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_estimators\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_depth\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_features\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mrandom_forest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\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      8\u001b[0m \u001b[0maccuracy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrandom_forest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOOB_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;32m<ipython-input-4-dfd71739b75e>\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m     66\u001b[0m             \u001b[0mcurrent_y\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcurrent_bootstraps_row_indices\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     67\u001b[0m             \u001b[0mcurrent_dt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecision_trees\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 68\u001b[1;33m             \u001b[0mcurrent_dt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcurrent_X\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcurrent_y\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m## 0 for the initial depth\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     69\u001b[0m             \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Current Tree to fit : \"\u001b[0m \u001b[1;33m,\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m## Which tree we are using\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     70\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;32mE:\\CIE\\Year 4 semester 1 (Fall 2020)\\Machine Learning\\decision_tree.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, depth)\u001b[0m\n\u001b[0;32m    317\u001b[0m         \u001b[1;31m# base cases\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    318\u001b[0m         \u001b[0munique_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 319\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0munique_labels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdepth\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mmax_depth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m## IF we reach the maximum depth or the unique labels = 1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    320\u001b[0m             \u001b[0munique_labels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcounts_unique_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_counts\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    321\u001b[0m             \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcounts_unique_labels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margmax\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;31mNameError\u001b[0m: name 'max_depth' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VzSf-dxYMRaU"
      },
      "source": [
        "## Compare out implementation with Sklearn Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eMs5EJ0yMRaU"
      },
      "source": [
        "X_train , X_test , y_train , y_test = train_test_split(X,y,test_size = 0.2 , random_state = 10)\n",
        "\n",
        "X_train = pd.get_dummies(X_train,drop_first=True)\n",
        "X_test = pd.get_dummies(X_test,drop_first=True)\n",
        "clf = RandomForestClassifier(max_depth=5, n_estimators = 10 , criterion = 'entropy', oob_score = True)\n",
        "clf.fit(X_train, y_train)\n",
        "y_pred = clf.predict(X_test)\n",
        "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qAYb66fQMRaU"
      },
      "source": [
        "## Random Forest Pros and Cons"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KuUR9IqZMRaU"
      },
      "source": [
        "\n",
        "| Pros | Cons\n",
        "| --- | --- \n",
        "|Robust to outliers| biased while dealing with categorical variables.\n",
        "|Works well with non-linear data.|Slow Training.\n",
        "|Lower risk of overfitting.|Not suitable for linear methods with a lot of sparse features\n",
        "|Runs efficiently on a large dataset.|Greedy algorithms don’t yield the global optimum tree structure.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QXbSbzhqMRaU"
      },
      "source": [
        "# Boosting "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TvzTpGsiMRaU"
      },
      "source": [
        "<img src = https://quantdare.com/wp-content/uploads/2016/04/bb3.png>\n",
        "<img src = https://miro.medium.com/max/875/1*wpVgt07J_TeH3jEdc3A50g.png>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "02pF491MMRaU"
      },
      "source": [
        "# Adaboost - *ADAptive Boost* :"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0BS6wWzfMRaU"
      },
      "source": [
        "- Combining multiple weak classifiers to build one strong classifier\n",
        "\n",
        "\n",
        "- with each one progressively learning from the others' wrongly classified objects\n",
        "\n",
        "\n",
        "- Rather than being a model in itself, AdaBoost can be applied on top of any classifier to learn from its shortcomings and propose a more accurate model.\n",
        "\n",
        "\n",
        "- Therefore, it is called *best out-of-the-box classifier*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7FcdI-zQMRaU"
      },
      "source": [
        "## What do we mean by a Weak Classifier?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j6DiFAyyMRaU"
      },
      "source": [
        "**A one that performs better than random guessing, but still performs poorly at designating classes to objects.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yjI2fHDBMRaU"
      },
      "source": [
        "- For example, a weak classifier may predict that everyone above the age of 40 could not run a marathon but people falling below that age could.\n",
        "\n",
        "\n",
        "- You might get above 60% accuracy, but you would still be misclassifying a lot of data points!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PgpVMETvMRaU"
      },
      "source": [
        "## Decision Stumps"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ksrznJYUMRaU"
      },
      "source": [
        "- Trees with just **one** node and two leaves.\n",
        "\n",
        "\n",
        "- A stump can only use one variable to make a decision\n",
        "\n",
        "\n",
        "- AdaBoost uses a forest of them instead of trees"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b0weYAWIMRaU"
      },
      "source": [
        "## How AdaBoost works?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FVE6d6IhMRaU"
      },
      "source": [
        "1. A weak classifier (e.g. a decision stump) is made on top of the training data based on the **weighted samples**.\n",
        "\n",
        "- where each sample weight indicates how important it is to be correctly classified.\n",
        "\n",
        "\n",
        "- Initially, for the first stump, we give all the samples equal weights.\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rp2WqCuuMRaU"
      },
      "source": [
        "\n",
        "\n",
        "2. We create a decision stump for each variable and see how well each stump classifies samples to their target classes.\n",
        "\n",
        "\n",
        "- For example, in the diagram below we check for Age, Eating Junk Food, and Exercise. We'd look at how many samples are correctly or incorrectly classified as Fit or Unfit for each individual stump."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G4PgoF2xMRaU"
      },
      "source": [
        "<img src=https://lh3.googleusercontent.com/kpQjxgGIxnSnMm495bDs0OZf4rE08E58PV1wwK9q10b_pL5AtKkRcY0OY5Hc_NFY0aW6iRQYAQDKuueEwnOfcEz9_IYyO-Ej-HwAqoFS_rQ779mP5HTHPKCy4x-lBmr33dd-Nw>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CebHVYr_MRaU"
      },
      "source": [
        "3. More weight is assigned to the incorrectly classified samples so that they're classified correctly in the next decision stump.\n",
        "\n",
        "\n",
        "- Weight is also assigned to each classifier based on the accuracy of the classifier, which means high accuracy = high weight!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yo21Rnk-MRaU"
      },
      "source": [
        "4. Reiterate from Step 2 until all the data points have been correctly classified, or the maximum iteration level has been reached."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FWhMmhMLMRaU"
      },
      "source": [
        "<img src = https://blog.paperspace.com/content/images/2019/12/WhatsApp-Image-2019-12-30-at-11.55.02-AM.jpeg>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n9i3hfNqMRaU"
      },
      "source": [
        "## Adaboost Equation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1-Pwy3Q9MRaU"
      },
      "source": [
        "$$\\alpha_t = \\frac{1}{2}\\ln\\frac{1-TotalError}{TotalError}$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fxZ8VR3BMRaU"
      },
      "source": [
        "- Alpha is how much influence this stump will have in the final classification. \n",
        "\n",
        "\n",
        "- Total Error is nothing but the total number of misclassifications for that training set divided by the training set size"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dD9VmGenMRaU"
      },
      "source": [
        "<img src = https://i.ibb.co/J5ck9X9/adaboost-alphacurve.png>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BwLGEV-DMRaU"
      },
      "source": [
        "- When alpha is zero, the error rate is equal to the random chance - 0.5 - \n",
        "\n",
        "\n",
        "- When alpha is a positive number, the error rate decreases, which means we have a perfect decision stump"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d8WVvX6uMRaU"
      },
      "source": [
        "$$w_i = w_{i-1} \\cdot e^{\\pm\\alpha}$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TIImieWjMRaU"
      },
      "source": [
        "- Alpha is positive when the predicted and the actual output agree (the sample was classified correctly). In this case we **decrease** the sample weight from what it was before, since we're already performing well.\n",
        "\n",
        "\n",
        "- Alpha is negative when the predicted output does not agree with the actual class (i.e. the sample is misclassified). In this case we need to **increase** the sample weight so that the same misclassification does not repeat in the next stump. This is how the stumps are dependent on their predecessors."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tU_nmUMtMRaV"
      },
      "source": [
        "## AdaBoost Pseduocode"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hx7onSiEMRaV"
      },
      "source": [
        "- Initially set uniform example weights.\n",
        "\n",
        "\n",
        "\n",
        "- for Each base learner do:\n",
        "    1. Train base learner with a weighted sample.\n",
        "    2. Test base learner on all data.\n",
        "    3. Set learner weight with a weighted error.\n",
        "    4. Set example weights based on ensemble predictions.\n",
        "- end for"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6E4z2F6tMRaV"
      },
      "source": [
        "## Bagging vs Boosting"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ge2iUNyXMRaV"
      },
      "source": [
        "- Bagging is easy to parallelize and hence training is faster\n",
        "\n",
        "\n",
        "\n",
        "- Boosting is more efficient for fixed number of iterations (classifiers)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zn132FCSMRaV"
      },
      "source": [
        "# Sources"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-7ec2pHwMRaV"
      },
      "source": [
        "1. https://www.datacamp.com/community/tutorials/ensemble-learning-python\n",
        "\n",
        "\n",
        "2. https://blog.paperspace.com/tag/series-ensemble-methods/\n",
        "\n",
        "\n",
        "3. https://medium.com/open-machine-learning-course/open-machine-learning-course-topic-5-ensembles-of-algorithms-and-random-forest-8e05246cbba7\n",
        "\n",
        "\n",
        "4. https://towardsdatascience.com/an-introduction-to-the-bootstrap-method-58bcb51b4d60\n",
        "\n",
        "\n",
        "5. https://datasciencechalktalk.com/2019/11/12/bootstrap-sampling-an-implementation-with-python/\n",
        "\n",
        "\n",
        "6. https://blog.paperspace.com/adaboost-optimizer/\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tYKNFAtMMRaV"
      },
      "source": [
        "# Tasks"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IW58w8_BMRaV"
      },
      "source": [
        "## 3 points: Work with the [Vehicle regression dataset](https://www.kaggle.com/nehalbirla/vehicle-dataset-from-cardekho)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vHr79Rh6MRaV"
      },
      "source": [
        "### Load the dataset, perform all necessary data exploration and preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h6o3UjPiyHAj",
        "outputId": "7f11f6a7-f919-4daa-ef81-4a105e668aba"
      },
      "source": [
        "df = pd.read_csv('CAR DETAILS FROM CAR DEKHO.csv')\n",
        "df.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>name</th>\n",
              "      <th>year</th>\n",
              "      <th>selling_price</th>\n",
              "      <th>km_driven</th>\n",
              "      <th>fuel</th>\n",
              "      <th>seller_type</th>\n",
              "      <th>transmission</th>\n",
              "      <th>owner</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Maruti 800 AC</td>\n",
              "      <td>2007</td>\n",
              "      <td>60000</td>\n",
              "      <td>70000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Individual</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First Owner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Maruti Wagon R LXI Minor</td>\n",
              "      <td>2007</td>\n",
              "      <td>135000</td>\n",
              "      <td>50000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Individual</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First Owner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Hyundai Verna 1.6 SX</td>\n",
              "      <td>2012</td>\n",
              "      <td>600000</td>\n",
              "      <td>100000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Individual</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First Owner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Datsun RediGO T Option</td>\n",
              "      <td>2017</td>\n",
              "      <td>250000</td>\n",
              "      <td>46000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Individual</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First Owner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Honda Amaze VX i-DTEC</td>\n",
              "      <td>2014</td>\n",
              "      <td>450000</td>\n",
              "      <td>141000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Individual</td>\n",
              "      <td>Manual</td>\n",
              "      <td>Second Owner</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       name  year  selling_price  km_driven    fuel  \\\n",
              "0             Maruti 800 AC  2007          60000      70000  Petrol   \n",
              "1  Maruti Wagon R LXI Minor  2007         135000      50000  Petrol   \n",
              "2      Hyundai Verna 1.6 SX  2012         600000     100000  Diesel   \n",
              "3    Datsun RediGO T Option  2017         250000      46000  Petrol   \n",
              "4     Honda Amaze VX i-DTEC  2014         450000     141000  Diesel   \n",
              "\n",
              "  seller_type transmission         owner  \n",
              "0  Individual       Manual   First Owner  \n",
              "1  Individual       Manual   First Owner  \n",
              "2  Individual       Manual   First Owner  \n",
              "3  Individual       Manual   First Owner  \n",
              "4  Individual       Manual  Second Owner  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3LWeM5bHyHAj",
        "outputId": "ef3c0421-d778-4ffb-8151-1a7131ba227a"
      },
      "source": [
        "df.info()\n",
        "\n",
        "#copying the dataset to a new one that will be cleaned\n",
        "df_clean = df.copy()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4340 entries, 0 to 4339\n",
            "Data columns (total 8 columns):\n",
            " #   Column         Non-Null Count  Dtype \n",
            "---  ------         --------------  ----- \n",
            " 0   name           4340 non-null   object\n",
            " 1   year           4340 non-null   int64 \n",
            " 2   selling_price  4340 non-null   int64 \n",
            " 3   km_driven      4340 non-null   int64 \n",
            " 4   fuel           4340 non-null   object\n",
            " 5   seller_type    4340 non-null   object\n",
            " 6   transmission   4340 non-null   object\n",
            " 7   owner          4340 non-null   object\n",
            "dtypes: int64(3), object(5)\n",
            "memory usage: 271.4+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NMnMxIeSyHAk"
      },
      "source": [
        "### Assessing\n",
        "1- There appears to be no missing values in the dataset.\n",
        "\n",
        "\n",
        "2- It is more convenient to store `fuel`, `seller_type`, `transmission` and `owner` columns as **\"category\"** datatype instead of object.\n",
        "\n",
        "reference: https://pandas.pydata.org/pandas-docs/stable/user_guide/categorical.html"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bCUvTvibyHAk"
      },
      "source": [
        "### Cleaning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sYQ89BikyHAk",
        "outputId": "464c3f9b-7161-486b-f0a8-ecaccb208ce9"
      },
      "source": [
        "df_clean['fuel'] = df_clean['fuel'].astype('category')\n",
        "df_clean['seller_type'] = df_clean['seller_type'].astype('category')\n",
        "df_clean['transmission'] = df_clean['transmission'].astype('category')\n",
        "df_clean['owner'] = df_clean['owner'].astype('category')\n",
        "\n",
        "df_clean.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4340 entries, 0 to 4339\n",
            "Data columns (total 8 columns):\n",
            " #   Column         Non-Null Count  Dtype   \n",
            "---  ------         --------------  -----   \n",
            " 0   name           4340 non-null   object  \n",
            " 1   year           4340 non-null   int64   \n",
            " 2   selling_price  4340 non-null   int64   \n",
            " 3   km_driven      4340 non-null   int64   \n",
            " 4   fuel           4340 non-null   category\n",
            " 5   seller_type    4340 non-null   category\n",
            " 6   transmission   4340 non-null   category\n",
            " 7   owner          4340 non-null   category\n",
            "dtypes: category(4), int64(3), object(1)\n",
            "memory usage: 153.3+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W-jYdKtXyHAl",
        "outputId": "cc4712d4-ddc0-4eb2-ca6e-d332aa29d6fb"
      },
      "source": [
        "df_clean.describe()"
      ],
      "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>year</th>\n",
              "      <th>selling_price</th>\n",
              "      <th>km_driven</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4340.000000</td>\n",
              "      <td>4.340000e+03</td>\n",
              "      <td>4340.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2013.090783</td>\n",
              "      <td>5.041273e+05</td>\n",
              "      <td>66215.777419</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.215344</td>\n",
              "      <td>5.785487e+05</td>\n",
              "      <td>46644.102194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1992.000000</td>\n",
              "      <td>2.000000e+04</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2011.000000</td>\n",
              "      <td>2.087498e+05</td>\n",
              "      <td>35000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2014.000000</td>\n",
              "      <td>3.500000e+05</td>\n",
              "      <td>60000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2016.000000</td>\n",
              "      <td>6.000000e+05</td>\n",
              "      <td>90000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2020.000000</td>\n",
              "      <td>8.900000e+06</td>\n",
              "      <td>806599.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              year  selling_price      km_driven\n",
              "count  4340.000000   4.340000e+03    4340.000000\n",
              "mean   2013.090783   5.041273e+05   66215.777419\n",
              "std       4.215344   5.785487e+05   46644.102194\n",
              "min    1992.000000   2.000000e+04       1.000000\n",
              "25%    2011.000000   2.087498e+05   35000.000000\n",
              "50%    2014.000000   3.500000e+05   60000.000000\n",
              "75%    2016.000000   6.000000e+05   90000.000000\n",
              "max    2020.000000   8.900000e+06  806599.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3FU9DgBNyHAl"
      },
      "source": [
        "The min of `km_driven` equals one, this might be an error."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a5lKLWMXyHAl",
        "outputId": "89541037-6c98-4ff3-f152-4974c8d10d4a"
      },
      "source": [
        "#Checking the values of each column\n",
        "\n",
        "v_c = {}\n",
        "for col in df_clean.columns:\n",
        "    v_c [col] = [df_clean[col].value_counts()]\n",
        "v_c\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'name': [Maruti Swift Dzire VDI                  69\n",
              "  Maruti Alto 800 LXI                     59\n",
              "  Maruti Alto LXi                         47\n",
              "  Maruti Alto LX                          35\n",
              "  Hyundai EON Era Plus                    35\n",
              "                                          ..\n",
              "  Hyundai Grand i10 Asta Option AT         1\n",
              "  Volkswagen Jetta 2.0 TDI Comfortline     1\n",
              "  Hyundai Grand i10 Nios Sportz            1\n",
              "  Renault Duster Petrol RxL                1\n",
              "  Maruti Ertiga BSIV ZXI                   1\n",
              "  Name: name, Length: 1491, dtype: int64],\n",
              " 'year': [2017    466\n",
              "  2015    421\n",
              "  2012    415\n",
              "  2013    386\n",
              "  2014    367\n",
              "  2018    366\n",
              "  2016    357\n",
              "  2011    271\n",
              "  2010    234\n",
              "  2019    195\n",
              "  2009    193\n",
              "  2008    145\n",
              "  2007    134\n",
              "  2006    110\n",
              "  2005     85\n",
              "  2020     48\n",
              "  2004     42\n",
              "  2003     23\n",
              "  2002     21\n",
              "  2001     20\n",
              "  1998     12\n",
              "  2000     12\n",
              "  1999     10\n",
              "  1997      3\n",
              "  1996      2\n",
              "  1995      1\n",
              "  1992      1\n",
              "  Name: year, dtype: int64],\n",
              " 'selling_price': [300000     162\n",
              "  250000     125\n",
              "  350000     122\n",
              "  550000     107\n",
              "  600000     103\n",
              "            ... \n",
              "  1331000      1\n",
              "  479000       1\n",
              "  1290000      1\n",
              "  746000       1\n",
              "  213000       1\n",
              "  Name: selling_price, Length: 445, dtype: int64],\n",
              " 'km_driven': [70000     236\n",
              "  80000     228\n",
              "  50000     222\n",
              "  120000    220\n",
              "  60000     215\n",
              "           ... \n",
              "  80577       1\n",
              "  49834       1\n",
              "  19107       1\n",
              "  13270       1\n",
              "  22255       1\n",
              "  Name: km_driven, Length: 770, dtype: int64],\n",
              " 'fuel': [Diesel      2153\n",
              "  Petrol      2123\n",
              "  CNG           40\n",
              "  LPG           23\n",
              "  Electric       1\n",
              "  Name: fuel, dtype: int64],\n",
              " 'seller_type': [Individual          3244\n",
              "  Dealer               994\n",
              "  Trustmark Dealer     102\n",
              "  Name: seller_type, dtype: int64],\n",
              " 'transmission': [Manual       3892\n",
              "  Automatic     448\n",
              "  Name: transmission, dtype: int64],\n",
              " 'owner': [First Owner             2832\n",
              "  Second Owner            1106\n",
              "  Third Owner              304\n",
              "  Fourth & Above Owner      81\n",
              "  Test Drive Car            17\n",
              "  Name: owner, dtype: int64]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eKkO6aXuyHAm"
      },
      "source": [
        "We may change the values of `owner` column to be **0, 1, 2, 3, 4** indtead of **'Test Drive Car'**, **'First Owner'**, **'Second Owner'**, **'Third Owner'**, **'Fourth & Above Owner'**. Or we may perform one hot encoding."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iPpqosRgyHAm"
      },
      "source": [
        "### Visualizations "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xkkq51XqyHAm",
        "outputId": "8082512f-66ba-4458-ee65-cee7a8e63b7f"
      },
      "source": [
        "sns.countplot(x='selling_price',data=df_clean);\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 7200x7200 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "icXNSsPqyHAm"
      },
      "source": [
        "# figure size in inches\n",
        "rcParams['figure.figsize'] = 100,100"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6BIRHqUMyHAm",
        "outputId": "3bbe58df-3497-461c-80a5-e1005c7097ac"
      },
      "source": [
        "sns.catplot(x=\"year\", y=\"selling_price\", data=df_clean);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JPEtMP91yHAn",
        "outputId": "ac1f9ea2-ba04-4346-99c5-e167b2bc7ab3"
      },
      "source": [
        "sns.catplot(x=\"km_driven\", y=\"selling_price\", data=df_clean);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "beOFMc6ryHAn"
      },
      "source": [
        "#df_clean.plot(kind=\"scatter\", x='name', y='selling_price');\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h-VJf6YqyHAn"
      },
      "source": [
        "#c = list(df.columns)\n",
        "#for i in range(1,len(c)):\n",
        "#    df_clean.plot(kind=\"scatter\", x=c[i-1], y=c[i])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C_iySN4RyHAn",
        "outputId": "26cbed2e-d906-4c17-dd2a-44a061166ce7"
      },
      "source": [
        "df_clean.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 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WW71pObYyHAo"
      },
      "source": [
        "### Checking which features are mostly correlated to the target"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VeGnKJnbyHAo",
        "outputId": "c31cd967-46eb-4940-e43b-ca02211814d1"
      },
      "source": [
        "corr_matrix = df_clean.corr()\n",
        "\n",
        "corr_matrix[\"selling_price\"].sort_values(ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "selling_price    1.000000\n",
              "year             0.413922\n",
              "km_driven       -0.192289\n",
              "Name: selling_price, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3cqSL66IyHAo"
      },
      "source": [
        "### Perform one hot encoding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3eYOvRQsyHAp"
      },
      "source": [
        "categorical = ['fuel', 'seller_type', 'transmission', 'owner']   \n",
        "#name is not here since it has so many unique values, if we encode it we will have 1503 columns!\n",
        "#However, we could change the name values to be 'Maruti', 'Hyundai', 'Datsun', 'Honda' and so on,\n",
        "#without the details of the name, in this way, we will have less unique names in the 'name' column.\n",
        "\n",
        "numerical = ['year', 'km_driven']\n",
        "\n",
        "X = pd.concat([df_clean[categorical], df_clean[numerical]], axis=1) ## Just sort them\n",
        "\n",
        "y = df_clean['selling_price']\n",
        "\n",
        "#One hot encoding\n",
        "X = pd.get_dummies(X,drop_first=True)\n",
        "\n",
        "df_clean = pd.concat([X,y], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Li6wJCRgyHAp"
      },
      "source": [
        "### Checking the correlations again after encoding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sqrOmVt5yHAp",
        "outputId": "01ad4fe1-e87d-4c00-e010-a513933ed840"
      },
      "source": [
        "corr_matrix = df_clean.corr()\n",
        "\n",
        "corr_matrix[\"selling_price\"].sort_values(ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "selling_price                   1.000000\n",
              "year                            0.413922\n",
              "fuel_Diesel                     0.282947\n",
              "seller_type_Trustmark Dealer    0.110176\n",
              "owner_Test Drive Car            0.048799\n",
              "fuel_Electric                  -0.005095\n",
              "fuel_LPG                       -0.042434\n",
              "owner_Fourth & Above Owner     -0.078725\n",
              "owner_Third Owner              -0.111326\n",
              "owner_Second Owner             -0.161986\n",
              "km_driven                      -0.192289\n",
              "seller_type_Individual         -0.236798\n",
              "fuel_Petrol                    -0.269453\n",
              "transmission_Manual            -0.530205\n",
              "Name: selling_price, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oxi5tQUjyHAp"
      },
      "source": [
        "#### The `year` feature appears to have the highest positive correlation. While the `transmission_Manual` feature appears to have the highest negative correlation."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PqUn3195yHAp"
      },
      "source": [
        "### Split the data into train and test "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LUFGcdDXyHAq"
      },
      "source": [
        "#Train,test split\n",
        "X_train , X_test , y_train , y_test = train_test_split(X,y,test_size = 0.2 , random_state = 0)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tKYJ7BkVMRaV"
      },
      "source": [
        "## 3 Points: Use Sklearn Random Forest Regressor as the ML model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CxX0rPBeyHAq"
      },
      "source": [
        "#Random Forest Regression fitting and predicting\n",
        "regr = RandomForestRegressor(max_depth=2, random_state=0)\n",
        "regr.fit(X_train, y_train)\n",
        "y_predicted = regr.predict(X_test)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0fjw2zTLyHAq",
        "outputId": "06a9d385-371a-4be8-e72a-fc71eceb6e11"
      },
      "source": [
        "# Calculating R^2 metric to assess the model\n",
        "print(\"R^2 metric:\", regr.score(X_test, y_test) )\n",
        "\n",
        "# before cleaning, R^2 metric: 0.4943483415797354\n",
        "\n",
        "#r2_score(y_test, y_predicted)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R^2 metric: 0.4943483415797354\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dV0T3amYMRaV"
      },
      "source": [
        "### Perform proper hyperparameter tuning to choose the best base model, depth, max_features, and any other important hyper-parameters "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WiQXI0rDyHAr",
        "outputId": "05a81af6-eaab-4177-fc69-17175750c44f"
      },
      "source": [
        "#hyper-parameters to be tuned\n",
        "parameters = {'n_estimators':list(range(50,251,50)), 'max_depth':list(range(4,7)),\n",
        "             'max_features': ['auto', 'log2'] }\n",
        "\n",
        "#Using Grid Search to tune these hyper-parameters\n",
        "rfr = RandomForestRegressor()\n",
        "clf = GridSearchCV(rfr, parameters)\n",
        "clf.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(estimator=RandomForestRegressor(),\n",
              "             param_grid={'max_depth': [4, 5, 6],\n",
              "                         'max_features': ['auto', 'log2'],\n",
              "                         'n_estimators': [50, 100, 150, 200, 250]})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mVsOWOz-yHAr",
        "outputId": "e64e18b0-feb8-4a85-ca69-b2f4b7c96a0b"
      },
      "source": [
        "#The best hyper-parameters found using Grid Search\n",
        "print(clf.best_params_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'max_depth': 6, 'max_features': 'auto', 'n_estimators': 50}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K4wTPQrfyHAr",
        "outputId": "db49fb3b-5dc8-476e-a43f-9cde16be249e"
      },
      "source": [
        "#y_pred = clf.predict(X_test)\n",
        "\n",
        "# Calculating R^2 metric to assess the model\n",
        "print(\"R^2 metric:\", clf.score(X_test, y_test) )\n",
        "\n",
        "# before cleaning, R^2 metric: 0.756080891896676\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R^2 metric: 0.756080891896676\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O5fzEgw1MRaV"
      },
      "source": [
        "## 4 Points: Use Sklearn AdaBoost on the same dataset and compare the results with the RF"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tg91VhyAyHAr"
      },
      "source": [
        "#AdaBoost Regression fitting and predicting\n",
        "regr = AdaBoostRegressor(random_state=0, n_estimators=100)\n",
        "regr.fit(X_train, y_train)\n",
        "y_predicted = regr.predict(X_test)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ebuDU7vRyHAs",
        "outputId": "b3029082-ad9a-4ac7-cb13-3bb1753b6eb6"
      },
      "source": [
        "# Calculating R^2 metric to assess the model\n",
        "print(\"R^2 metric:\", regr.score(X_test, y_test) )\n",
        "\n",
        "#before cleaning, R^2 metric: 0.48125740908409176\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R^2 metric: 0.48125740908409176\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VvomN-T1yHAs"
      },
      "source": [
        "### Hyperparameter tuning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "code_folding": [
          1
        ],
        "id": "wWh9fYDTyHAs",
        "outputId": "514d4d7e-9995-4eb1-cb27-0260d5f90749"
      },
      "source": [
        "#hyper-parameters to be tuned\n",
        "parameters = {'n_estimators':list(range(50,251,50)), 'learning_rate':list(np.arange(1,2,0.5)),\n",
        "             'loss': ['linear', 'square'] }\n",
        "\n",
        "#Using Grid Search to tune these hyper-parameters\n",
        "adaBoost = AdaBoostRegressor()\n",
        "clf = GridSearchCV(adaBoost, parameters)\n",
        "clf.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(estimator=AdaBoostRegressor(),\n",
              "             param_grid={'learning_rate': [1.0, 1.5],\n",
              "                         'loss': ['linear', 'square'],\n",
              "                         'n_estimators': [50, 100, 150, 200, 250]})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "code_folding": [],
        "id": "ljgTin35yHAs",
        "outputId": "f46f303e-b606-4868-8dd7-c0ece13cc2e2"
      },
      "source": [
        "#The best hyper-parameters found using Grid Search\n",
        "print(clf.best_params_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'learning_rate': 1.0, 'loss': 'linear', 'n_estimators': 150}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SRD0SoOByHAt",
        "outputId": "c4dab332-297b-4d43-8558-6ee9bd53954f"
      },
      "source": [
        "# Calculating R^2 metric to assess the model\n",
        "print(\"R^2 metric:\", clf.score(X_test, y_test) )\n",
        "\n",
        "#before cleaning, R^2 metric: 0.5098663414330425\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R^2 metric: 0.5098663414330425\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s_1dJEaeyHAt"
      },
      "source": [
        "### Comparing the results with the RF¶\n",
        "The performance of Random Forest Regressor appears to be better than Ada Boost Regressor in this specific dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "enLcW4NtMRaV"
      },
      "source": [
        "## Bonus Task ( 3 Points ) : Use another Boosting Algorithm, such as XGBoost, on the same dataset. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FY4IZjJfyHAt"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jpc6_znJMRaV"
      },
      "source": [
        "### You need to explain in details how the algorithm work and how it is different from AdaBoost in order to receive any credit."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4g33SnWfyHAu"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CBLy5kDlMRaV"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e9geqzvNm97y"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}