Neha Gupta .ipynb

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  "metadata": {
    "colab": {
      "name": "Neha Gupta",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LqnlCXKGJ3_O"
      },
      "source": [
        "This notebook contains basic to hard exercies for each library that you encounter in your data analytics journey. The emphasis here is to get familiar with the inbuilt functions of these libraries and get used to reading the documentation. Places with \n",
        "```python \n",
        "# ...\n",
        "``` \n",
        "require you to write code to complete the objective."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S_L5CesyVgMG"
      },
      "source": [
        "# Importing libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E3qxT1l3Jmv_"
      },
      "source": [
        "## Numpy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pqyJ2FVqKHPX"
      },
      "source": [
        "1. Create a null vector of size 10\n",
        "\n",
        "Expected output: `[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]`\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OuwkcLo0JmNQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9e1e4e56-b4d5-481f-ef68-ecb50dac5a79"
      },
      "source": [
        "Z = np.zeros(10)\n",
        "print(Z)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l1WtEdtuKhrI"
      },
      "source": [
        "2. Create a vector with values ranging from 10 to 39\n",
        "\n",
        "Expected output: `[10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39]`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5DeUt45IKiCN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e74a6341-488a-4bc7-fc10-2326594e0628"
      },
      "source": [
        "Z = np.arange(10, 40)\n",
        "print(Z)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33\n",
            " 34 35 36 37 38 39]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "krEsOrGHK3oy"
      },
      "source": [
        "3. Reverse the above vector (Hint: use slicing)\n",
        "\n",
        "Expected output: `[39 38 37 36 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10]`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LxQDHVUWK3u-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "13aa8da9-441b-4de8-f20b-b354bdad5f15"
      },
      "source": [
        "Z = Z[::-1]\n",
        "print(Z)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[39 38 37 36 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16\n",
            " 15 14 13 12 11 10]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zwXDmB_uLWyv"
      },
      "source": [
        "4. Create a 3x3 matrix with values ranging from 0 to 8\n",
        "\n",
        "Expected output:\n",
        "```\n",
        "[[0 1 2]\n",
        " [3 4 5]\n",
        " [6 7 8]]\n",
        " ```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MBtv_l0JLW6d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1d55b7a4-797b-4326-a8a9-23fd8a8fcfdd"
      },
      "source": [
        "Z = np.arange(0, 9).reshape(3,3)\n",
        "print(Z)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0 1 2]\n",
            " [3 4 5]\n",
            " [6 7 8]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aGs7AwbxLgbZ"
      },
      "source": [
        "5. Create a 10x10 array with random values and find the minimum and maximum values. Also find the mean value"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LC7v8dlwLxQr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "83f7df1c-d6a0-4b74-8567-b71e266562f9"
      },
      "source": [
        "Z = np.random.random((10, 10))\n",
        "Zmin = Z.min()\n",
        "Zmax = Z.max()\n",
        "Zmean = Z.mean()\n",
        "print(Zmin, Zmax, Zmean)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.01992743654661766 0.9952805755712775 0.5251684021842442\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_v_yoK7ALx39"
      },
      "source": [
        "6. Normalize a 5x5 random matrix ( Hint: (x -mean)/std )"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WJZaFlmmLvvH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fb214a67-fcbf-4c9a-eaab-41724a9c55b7"
      },
      "source": [
        "Z = np.random.random((5, 5))\n",
        "Z = (Z - Z.mean())/Z.std()\n",
        "print(Z)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 1.26082028 -0.62894603  0.51894206 -0.43748951  1.93579478]\n",
            " [-0.89611313 -1.45804612  1.53166006  0.09484552 -1.22932242]\n",
            " [ 0.94271328  0.57373832  0.27182556 -0.76236755 -0.52409269]\n",
            " [ 1.38595277 -0.13238808  1.70359349 -0.8995329  -0.83000659]\n",
            " [ 0.19275492 -1.20050351 -0.03981152  0.05507183 -1.42909282]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WPpIZvO2Mfj_"
      },
      "source": [
        "7. Multiply a 5x3 matrix by a 3x2 matrix (all elements of both matrices are 1)\n",
        "\n",
        "Expected output:\n",
        "```\n",
        "[[3. 3.]\n",
        " [3. 3.]\n",
        " [3. 3.]\n",
        " [3. 3.]\n",
        " [3. 3.]]\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OvyZjMRWMftv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4cdc5f73-f01a-40f9-99cf-598be5d6f1f9"
      },
      "source": [
        "Z = np.dot(np.ones((5,3)), np.ones((3,2)))\n",
        "print(Z)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[3. 3.]\n",
            " [3. 3.]\n",
            " [3. 3.]\n",
            " [3. 3.]\n",
            " [3. 3.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rMfmqe_4NcNf"
      },
      "source": [
        "8. How to find the most frequent value in an array? (Generate a random array and demonstrate)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FYRrJcDQNcUf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "abe0fabb-36c8-4c59-d6ed-509fda6b7cab"
      },
      "source": [
        "Z = np.random.randint(0,10,50)\n",
        "Zmode = np.bincount(Z).argmax()\n",
        "print(Zmode)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TU9Rkz9qOEc3"
      },
      "source": [
        "9. Consider a large vector Z, compute Z to the power of 3 using 2 different methods"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vm6lEJMeOEiX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "db8682b9-7ad8-48a4-f0be-c12acd410c4b"
      },
      "source": [
        "Z = np.random.rand(int(5e7))\n",
        "\n",
        "Z1 = np.power(Z, 3)\n",
        "Z2 = Z**3\n",
        "print(Z1)\n",
        "print(Z2)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.22372175 0.76785342 0.6204508  ... 0.61511979 0.09005622 0.17587557]\n",
            "[0.22372175 0.76785342 0.6204508  ... 0.61511979 0.09005622 0.17587557]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_qqq2l0dOlAp"
      },
      "source": [
        "10. Given a 1D array, negate all elements which are between 3 and 8, in place\n",
        "\n",
        "Hint: Use `<` and `>` to create indices\n",
        "\n",
        "Expected output: `[ 0  1  2  3 -4 -5 -6 -7  8  9 10]`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RuiGERlNOlHu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "91b3b0b8-2c70-4ec6-e89f-8601a8745873"
      },
      "source": [
        "Z = np.arange(11)\n",
        "idx = ((Z>3) & (Z<8))\n",
        "Z[idx] *= -1\n",
        "print(Z)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 0  1  2  3 -4 -5 -6 -7  8  9 10]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yA3SN8PZPEKM"
      },
      "source": [
        "## Pandas"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z8_XtdvvPQKs"
      },
      "source": [
        "1. Create a DataFrame df from this dictionary data which has the index labels\n",
        "\n",
        "```python\n",
        "data = {'animal': ['cat', 'cat', 'snake', 'dog', 'dog', 'cat', 'snake', 'cat', 'dog', 'dog'],\n",
        "        'age': [2.5, 3, 0.5, np.nan, 5, 2, 4.5, np.nan, 7, 3],\n",
        "        'visits': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
        "        'priority': ['yes', 'yes', 'no', 'yes', 'no', 'no', 'no', 'yes', 'no', 'no']}\n",
        "\n",
        "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']\n",
        "```\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xREF9gRGPLs2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "11bb7f28-968d-4646-af06-91c6d3855951"
      },
      "source": [
        "data = {'animal': ['cat', 'cat', 'snake', 'dog', 'dog', 'cat', 'snake', 'cat', 'dog', 'dog'],\n",
        "        'age': [2.5, 3, 0.5, np.nan, 5, 2, 4.5, np.nan, 7, 3],\n",
        "        'visits': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
        "        'priority': ['yes', 'yes', 'no', 'yes', 'no', 'no', 'no', 'yes', 'no', 'no']}\n",
        "\n",
        "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']\n",
        "\n",
        "df = pd.DataFrame(data, index = labels)\n",
        "df"
      ],
      "execution_count": 12,
      "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>animal</th>\n",
              "      <th>age</th>\n",
              "      <th>visits</th>\n",
              "      <th>priority</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>cat</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>cat</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>snake</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>dog</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>e</th>\n",
              "      <td>dog</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>f</th>\n",
              "      <td>cat</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g</th>\n",
              "      <td>snake</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>h</th>\n",
              "      <td>cat</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>i</th>\n",
              "      <td>dog</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>j</th>\n",
              "      <td>dog</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  animal  age  visits priority\n",
              "a    cat  2.5       1      yes\n",
              "b    cat  3.0       3      yes\n",
              "c  snake  0.5       2       no\n",
              "d    dog  NaN       3      yes\n",
              "e    dog  5.0       2       no\n",
              "f    cat  2.0       3       no\n",
              "g  snake  4.5       1       no\n",
              "h    cat  NaN       1      yes\n",
              "i    dog  7.0       2       no\n",
              "j    dog  3.0       1       no"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3s66ibyiPaHG"
      },
      "source": [
        "2. Display a summary of the basic information about this DataFrame using a single method (Hint: read about `describe` method)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZSWpZp68PaNW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "f552d06c-f435-4b26-83db-d9af0948d941"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 13,
      "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>age</th>\n",
              "      <th>visits</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>8.000000</td>\n",
              "      <td>10.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.437500</td>\n",
              "      <td>1.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.007797</td>\n",
              "      <td>0.875595</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.500000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.375000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.625000</td>\n",
              "      <td>2.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            age     visits\n",
              "count  8.000000  10.000000\n",
              "mean   3.437500   1.900000\n",
              "std    2.007797   0.875595\n",
              "min    0.500000   1.000000\n",
              "25%    2.375000   1.000000\n",
              "50%    3.000000   2.000000\n",
              "75%    4.625000   2.750000\n",
              "max    7.000000   3.000000"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_vF5KU14PiCV"
      },
      "source": [
        "3. Return the first 3 rows of the DataFrame `df` (Hint: use either `iloc` or `head`)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GN1NneyePiHQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "43feb6c1-758b-4cfb-d90c-72990caaf798"
      },
      "source": [
        "df.head(3)"
      ],
      "execution_count": 14,
      "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>animal</th>\n",
              "      <th>age</th>\n",
              "      <th>visits</th>\n",
              "      <th>priority</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>cat</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>cat</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>snake</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  animal  age  visits priority\n",
              "a    cat  2.5       1      yes\n",
              "b    cat  3.0       3      yes\n",
              "c  snake  0.5       2       no"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cLGPdfdtPpgl"
      },
      "source": [
        "4. Select just the 'animal' and 'age' columns from the DataFrame `df`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_q2esBxrPpns",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "68d15bbd-5a02-4fee-a7ce-3021bd23e430"
      },
      "source": [
        "cols = [ \"animal\" , \"age\"]\n",
        "df[cols]"
      ],
      "execution_count": 15,
      "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>animal</th>\n",
              "      <th>age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>cat</td>\n",
              "      <td>2.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>cat</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>snake</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>dog</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>e</th>\n",
              "      <td>dog</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>f</th>\n",
              "      <td>cat</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g</th>\n",
              "      <td>snake</td>\n",
              "      <td>4.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>h</th>\n",
              "      <td>cat</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>i</th>\n",
              "      <td>dog</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>j</th>\n",
              "      <td>dog</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  animal  age\n",
              "a    cat  2.5\n",
              "b    cat  3.0\n",
              "c  snake  0.5\n",
              "d    dog  NaN\n",
              "e    dog  5.0\n",
              "f    cat  2.0\n",
              "g  snake  4.5\n",
              "h    cat  NaN\n",
              "i    dog  7.0\n",
              "j    dog  3.0"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IkRygI9zP4rd"
      },
      "source": [
        "5. Select the data in rows [3, 4, 8] and in columns ['animal', 'age']."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u9Ufl808P4wH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "f616feca-95a5-493f-b347-eb07ce939cd8"
      },
      "source": [
        "row_idx = [3, 4, 8]\n",
        "cols = [0, 1]\n",
        "df.iloc[row_idx, cols]"
      ],
      "execution_count": 16,
      "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>animal</th>\n",
              "      <th>age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>dog</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>e</th>\n",
              "      <td>dog</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>i</th>\n",
              "      <td>dog</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  animal  age\n",
              "d    dog  NaN\n",
              "e    dog  5.0\n",
              "i    dog  7.0"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YXhVq4LXQ6AN"
      },
      "source": [
        "6. Select only the rows where the number of visits is greater than 3."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oqdD6FXXQ7sH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "outputId": "941ab590-0fd2-4a43-a081-b4faa4488fac"
      },
      "source": [
        "idx = df['visits'] > 3\n",
        "df[idx]"
      ],
      "execution_count": 17,
      "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>animal</th>\n",
              "      <th>age</th>\n",
              "      <th>visits</th>\n",
              "      <th>priority</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [animal, age, visits, priority]\n",
              "Index: []"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y6OF0QMKRBSd"
      },
      "source": [
        "7. Calculate the mean age for each different animal in `df`. (Hint: use `groupby`)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Iwprc6aRLEj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "858b9a1c-821e-4698-efd1-7001d1844865"
      },
      "source": [
        "df.groupby(['animal']).mean()"
      ],
      "execution_count": 18,
      "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>age</th>\n",
              "      <th>visits</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>animal</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>cat</th>\n",
              "      <td>2.5</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dog</th>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>snake</th>\n",
              "      <td>2.5</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        age  visits\n",
              "animal             \n",
              "cat     2.5     2.0\n",
              "dog     5.0     2.0\n",
              "snake   2.5     1.5"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "522H21ZqRRRE"
      },
      "source": [
        "8. Count the number of each type of animal in df"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2o31JmCTRBXe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "03564afe-97f5-4d81-fe8f-56c46a009440"
      },
      "source": [
        "df.animal.value_counts()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "cat      4\n",
              "dog      4\n",
              "snake    2\n",
              "Name: animal, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A2p_1gV0RfGH"
      },
      "source": [
        "9. Given the random data `xs` and `ys`, make a scatter plot utilising the Pandas `plot` method"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nLE7KX4DRfKs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "30ddbde6-32a6-4ed3-e04a-4ae5c2f3f24e"
      },
      "source": [
        "xs = [1,5,2,8,1]\n",
        "ys = [4,2,1,9,6]\n",
        "\n",
        "df = pd.DataFrame({\"xs\": xs, \"ys\": ys})\n",
        "\n",
        "df.plot.scatter(x='xs', y='ys')"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f66eb1922d0>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uvH0t9yRSiVk"
      },
      "source": [
        "10. Plot a bar graph representing the mean age of each animal"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F4-Oo3dKSiek",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "outputId": "c38e3b4e-2491-444d-979a-dfc6b69a872a"
      },
      "source": [
        "data = {'animal': ['cat', 'cat', 'snake', 'dog', 'dog', 'cat', 'snake', 'cat', 'dog', 'dog'],\n",
        "        'age': [2.5, 3, 0.5, np.nan, 5, 2, 4.5, np.nan, 7, 3],\n",
        "        'visits': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
        "        'priority': ['yes', 'yes', 'no', 'yes', 'no', 'no', 'no', 'yes', 'no', 'no']}\n",
        "\n",
        "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']\n",
        "\n",
        "df = pd.DataFrame(data, index = labels)\n",
        "(df.groupby(['animal']).mean()).plot.bar(y = 'age')"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f66eb088950>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d5N4bol2T2Xd"
      },
      "source": [
        "## Make a simple Linear Regression Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8AoIjszRjJ8D"
      },
      "source": [
        "### Linear Regression from scratch"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1uQjO45HhNzB"
      },
      "source": [
        "We will start with the most familiar linear regression, a straight-line fit to data.\n",
        "A straight-line fit is a model of the form\n",
        "$$\n",
        "y = ax + b\n",
        "$$\n",
        "where $a$ is commonly known as the *slope*, and $b$ is commonly known as the *intercept*.\n",
        "\n",
        "Consider the following data, which is scattered about a line with a slope of 2 and an intercept of -5"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tTWWnzsbT2ek",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "fb4c43ec-6728-4575-c751-86d09b69c4a9"
      },
      "source": [
        "rng = np.random.RandomState(1)\n",
        "x = 10 * rng.rand(50)\n",
        "y = 2 * x - 5 + rng.randn(50)\n",
        "plt.scatter(x, y);"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WBS61IRcVzZn"
      },
      "source": [
        "We will now build a simple linear regression model to fit this set of points. Firstly, define the hypothesis function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eg_AnaNGXYtU"
      },
      "source": [
        "def h(x, a, b):\n",
        "  return a * x + b"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x_tGjm2waFG8"
      },
      "source": [
        "Next, we define the cost function which returns a single float value given the input X, parameters and ground truth Y. Remember that $$ J = \\frac{\\sum\\limits_{i=1}^m (h(x^i) - y^i)^2}{2m} $$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xBPJsuuxakpU"
      },
      "source": [
        "def cost_function(X, Y, a, b):\n",
        "  y_pred = a * X + b\n",
        "  m = X.shape[0]\n",
        "  J = np.sum((y_pred - Y) ** 2)\n",
        "  return J/(2 * m)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M1iwpSCZc4br"
      },
      "source": [
        "In order to perform gradient descent, you need to calculate the derivative of each parameter with respect to the cost function. Remember the formulae:\n",
        "\n",
        "$$\n",
        "\\frac{dJ}{da} = \\frac{\\sum\\limits_{i=1}^m (h(x^i) - y^i).x^i}{m}\n",
        "$$\n",
        " \n",
        "and\n",
        "\n",
        "$$\n",
        "\\frac{dJ}{db} = \\frac{\\sum\\limits_{i=1}^m (h(x^i) - y^i)}{m}\n",
        "$$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5qSraaMMbdyL"
      },
      "source": [
        "def gradients(X, Y, a, b):\n",
        "  y_pred = a * X + b\n",
        "  m = X.shape[0]\n",
        "  grad_a = (np.sum((y_pred - y) * X))/m\n",
        "  grad_b = (np.sum(y_pred - y))/m\n",
        "  return grad_a, grad_b"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "loht93wDfC5a"
      },
      "source": [
        "Finally, we define the gradient descent function which will carry out the optimisation. Remember the gradient descent step:\n",
        "\n",
        "$$\n",
        "a = a - \\alpha \\times \\frac{dJ}{da}\n",
        "$$\n",
        "\n",
        "and\n",
        "\n",
        "$$\n",
        "b = b - \\alpha \\times \\frac{dJ}{db}\n",
        "$$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SaHmNA9tbG0D"
      },
      "source": [
        "def gradient_descent(X, Y, a, b, learning_rate, iterations):\n",
        "    \n",
        "    for iteration in range(iterations):\n",
        " \n",
        "        loss = cost_function(X, Y, a, b)\n",
        "        grad_a, grad_b = gradients(X, Y, a, b)\n",
        "\n",
        "        # Define the gradient descent step\n",
        "        a = a - (learning_rate) * (grad_a)\n",
        "        b = b - (learning_rate) * (grad_b)\n",
        "\n",
        "        if iteration%10 == 0:\n",
        "          print(f\"Iteration: {iteration} | Loss: {loss}\")\n",
        "        \n",
        "    return a, b"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p7L29PDVfyty"
      },
      "source": [
        "Now we will run the gradient descent algorithm. First you will need to define the intial values of $a$ and $b$ as well as the hyperparameters (learning rate and number of iterations)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2byfJ7_FfxOK"
      },
      "source": [
        "a = 0\n",
        "b = 0\n",
        "learning_rate = 0.01\n",
        "num_iterations = 5000"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nwRyD8qqciSC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e7669a4f-1076-42cc-bdeb-7a5d998c571e"
      },
      "source": [
        "a, b = gradient_descent(x, y, a, b, learning_rate, num_iterations)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Iteration: 0 | Loss: 29.54030763813738\n",
            "Iteration: 10 | Loss: 4.219649936010633\n",
            "Iteration: 20 | Loss: 3.991532601943413\n",
            "Iteration: 30 | Loss: 3.787505684359301\n",
            "Iteration: 40 | Loss: 3.59510365695081\n",
            "Iteration: 50 | Loss: 3.4136592563545594\n",
            "Iteration: 60 | Loss: 3.2425484245486866\n",
            "Iteration: 70 | Loss: 3.0811826471244217\n",
            "Iteration: 80 | Loss: 2.9290069265757706\n",
            "Iteration: 90 | Loss: 2.785497873453465\n",
            "Iteration: 100 | Loss: 2.650161906232094\n",
            "Iteration: 110 | Loss: 2.5225335536978863\n",
            "Iteration: 120 | Loss: 2.402173854018407\n",
            "Iteration: 130 | Loss: 2.2886688449879755\n",
            "Iteration: 140 | Loss: 2.181628140256182\n",
            "Iteration: 150 | Loss: 2.0806835866426314\n",
            "Iteration: 160 | Loss: 1.9854879979199014\n",
            "Iteration: 170 | Loss: 1.8957139607097289\n",
            "Iteration: 180 | Loss: 1.811052708385449\n",
            "Iteration: 190 | Loss: 1.7312130591076142\n",
            "Iteration: 200 | Loss: 1.6559204143403048\n",
            "Iteration: 210 | Loss: 1.5849158144036395\n",
            "Iteration: 220 | Loss: 1.5179550478141939\n",
            "Iteration: 230 | Loss: 1.4548078113499971\n",
            "Iteration: 240 | Loss: 1.3952569179512708\n",
            "Iteration: 250 | Loss: 1.3390975497325643\n",
            "Iteration: 260 | Loss: 1.2861365535371378\n",
            "Iteration: 270 | Loss: 1.236191776610717\n",
            "Iteration: 280 | Loss: 1.1890914401097705\n",
            "Iteration: 290 | Loss: 1.1446735482895591\n",
            "Iteration: 300 | Loss: 1.1027853313399327\n",
            "Iteration: 310 | Loss: 1.0632827199525874\n",
            "Iteration: 320 | Loss: 1.0260298498126108\n",
            "Iteration: 330 | Loss: 0.9908985943100803\n",
            "Iteration: 340 | Loss: 0.9577681238645401\n",
            "Iteration: 350 | Loss: 0.9265244903466947\n",
            "Iteration: 360 | Loss: 0.8970602351680061\n",
            "Iteration: 370 | Loss: 0.8692740196902503\n",
            "Iteration: 380 | Loss: 0.843070276683886\n",
            "Iteration: 390 | Loss: 0.818358881636466\n",
            "Iteration: 400 | Loss: 0.7950548427805917\n",
            "Iteration: 410 | Loss: 0.7730780087753082\n",
            "Iteration: 420 | Loss: 0.7523527930355479\n",
            "Iteration: 430 | Loss: 0.7328079137614749\n",
            "Iteration: 440 | Loss: 0.7143761487736079\n",
            "Iteration: 450 | Loss: 0.6969941043105033\n",
            "Iteration: 460 | Loss: 0.6806019969938013\n",
            "Iteration: 470 | Loss: 0.6651434482107382\n",
            "Iteration: 480 | Loss: 0.650565290206925\n",
            "Iteration: 490 | Loss: 0.6368173832224742\n",
            "Iteration: 500 | Loss: 0.6238524430425376\n",
            "Iteration: 510 | Loss: 0.6116258783691326\n",
            "Iteration: 520 | Loss: 0.6000956374549256\n",
            "Iteration: 530 | Loss: 0.5892220634714795\n",
            "Iteration: 540 | Loss: 0.5789677581145305\n",
            "Iteration: 550 | Loss: 0.5692974529771724\n",
            "Iteration: 560 | Loss: 0.5601778882485643\n",
            "Iteration: 570 | Loss: 0.5515776983209488\n",
            "Iteration: 580 | Loss: 0.5434673039115502\n",
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            "Iteration: 4020 | Loss: 0.40916942874651596\n",
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            "Iteration: 4150 | Loss: 0.4091694286221222\n",
            "Iteration: 4160 | Loss: 0.4091694286159251\n",
            "Iteration: 4170 | Loss: 0.40916942861008093\n",
            "Iteration: 4180 | Loss: 0.40916942860456973\n",
            "Iteration: 4190 | Loss: 0.4091694285993722\n",
            "Iteration: 4200 | Loss: 0.40916942859447075\n",
            "Iteration: 4210 | Loss: 0.40916942858984817\n",
            "Iteration: 4220 | Loss: 0.4091694285854894\n",
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            "Iteration: 4240 | Loss: 0.40916942857750177\n",
            "Iteration: 4250 | Loss: 0.40916942857384586\n",
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            "Iteration: 4550 | Loss: 0.40916942852373306\n",
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            "Iteration: 4770 | Loss: 0.409169428516178\n",
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            "Iteration: 4870 | Loss: 0.40916942851490495\n",
            "Iteration: 4880 | Loss: 0.409169428514814\n",
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            "Iteration: 4930 | Loss: 0.40916942851443155\n",
            "Iteration: 4940 | Loss: 0.40916942851436744\n",
            "Iteration: 4950 | Loss: 0.409169428514307\n",
            "Iteration: 4960 | Loss: 0.40916942851425037\n",
            "Iteration: 4970 | Loss: 0.40916942851419685\n",
            "Iteration: 4980 | Loss: 0.4091694285141461\n",
            "Iteration: 4990 | Loss: 0.40916942851409854\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CLH0jk_QgRVC"
      },
      "source": [
        "Now let's visualise the line that we got"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PcT2m1lfgUTF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "bb0ba736-d201-476f-96a5-8b132b79a8d9"
      },
      "source": [
        "xfit = np.linspace(0, 10, 1000)\n",
        "yfit = xfit * a + b\n",
        "\n",
        "plt.scatter(x, y)\n",
        "plt.plot(xfit, yfit);"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ChsMB8OqgdaU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1b8a051c-f7f2-4326-fe9f-bad07c5fc63f"
      },
      "source": [
        "print(\"Model slope:    \", a)\n",
        "print(\"Model intercept:\", b)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model slope:     2.0272084725749027\n",
            "Model intercept: -4.998574855226507\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yQlLWs7_jNnS"
      },
      "source": [
        "### Using sklearn library"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ChmcY-Hjf6w"
      },
      "source": [
        "Implementing all the machine learning algorithms from scratch becomes quite cumbersome as the complexity increases. There are optimised libraries which has all this stuff pre-implemented and are quite easy to use.\n",
        "\n",
        "We can directly use Scikit-Learn's ``LinearRegression`` estimator to fit the above data and construct the best-fit line"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tUAMSfVHjSXM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "959b7f53-911e-454a-b6ac-fc8033de25f4"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "model = LinearRegression(fit_intercept=True)\n",
        "\n",
        "model.fit(x[:, np.newaxis], y)\n",
        "\n",
        "xfit = np.linspace(0, 10, 1000)\n",
        "yfit = model.predict(xfit[:, np.newaxis])\n",
        "\n",
        "plt.scatter(x, y)\n",
        "plt.plot(xfit, yfit);"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gpTfztdEj6OK"
      },
      "source": [
        "The slope and intercept of the data are contained in the model's fit parameters, which in Scikit-Learn are always marked by a trailing underscore. Here the relevant parameters are *coef_* and *intercept_*:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ynD2VJaj9uC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4f17fc45-6a15-4ebb-cbac-a8c487e73003"
      },
      "source": [
        "print(\"Model slope:    \", model.coef_[0])\n",
        "print(\"Model intercept:\", model.intercept_)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model slope:     2.0272088103606953\n",
            "Model intercept: -4.998577085553204\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3TGYxLDkkH5S"
      },
      "source": [
        "These results should be close to the parameters obtained from the linear regression we implemented above"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PxeRI7mUkmGR"
      },
      "source": [
        "## Solving a Data Analytics Problem"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v25RyO2ikp9Z"
      },
      "source": [
        "For the purposes of this exercise, let's pretend we're working for a startup that just got funded to create a smartphone app that automatically identifies species of flowers from pictures taken on the smartphone. We're working with a moderately-sized team of data scientists and will be building part of the data analysis pipeline for this app.\n",
        "\n",
        "We've been tasked by our company's Head of Data Science to create a demo machine learning model that takes four measurements from the flowers (sepal length, sepal width, petal length, and petal width) and identifies the species based on those measurements alone. \n",
        "\n",
        "We've been given a [data set](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/raw/master/example-data-science-notebook/iris-data.csv) from our field researchers to develop the demo, which only includes measurements for three types of Iris flowers."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5BHgrtPomYqY"
      },
      "source": [
        "### Step 1: Checking the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "564HJ1VHmdn6"
      },
      "source": [
        "The first step is to look at the data we're working with. Even curated data sets from the government can have errors in them, and it's vital that we spot these errors before investing too much time in our analysis.\n",
        "\n",
        "Let's start by reading the data into a pandas DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NYy05hlUmdch",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "18c1626f-799a-41ca-8be3-e140e94e71d2"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "iris_data = pd.read_csv('https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/raw/master/example-data-science-notebook/iris-data.csv')\n",
        "iris_data.head()"
      ],
      "execution_count": 33,
      "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>sepal_length_cm</th>\n",
              "      <th>sepal_width_cm</th>\n",
              "      <th>petal_length_cm</th>\n",
              "      <th>petal_width_cm</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal_length_cm  sepal_width_cm  ...  petal_width_cm        class\n",
              "0              5.1             3.5  ...             0.2  Iris-setosa\n",
              "1              4.9             3.0  ...             0.2  Iris-setosa\n",
              "2              4.7             3.2  ...             0.2  Iris-setosa\n",
              "3              4.6             3.1  ...             0.2  Iris-setosa\n",
              "4              5.0             3.6  ...             0.2  Iris-setosa\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z0G6snvlmwYw"
      },
      "source": [
        "The first row in the data file defines the column headers, and the headers are descriptive enough for us to understand what each column represents. The headers even give us the units that the measurements were recorded in, just in case we needed to know at a later point in the project.\n",
        "\n",
        "Each row following the first row represents an entry for a flower: four measurements and one class, which tells us the species of the flower.\n",
        "\n",
        "One of the first things we should look for is missing data. Thankfully, the field researchers already told us that they put a 'NA' into the spreadsheet when they were missing a measurement.\n",
        "\n",
        "We can tell pandas to automatically identify missing values if it knows our missing value marker."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ldsB0qXtkoea"
      },
      "source": [
        "iris_data = pd.read_csv('https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/raw/master/example-data-science-notebook/iris-data.csv', \n",
        "                        na_values=['NA'])"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q6Fm66Zkomv8"
      },
      "source": [
        "Next, it's always a good idea to look at the distribution of our data — especially the outliers.\n",
        "\n",
        "Let's start by printing out some summary statistics about the data set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EUGEvwpBonhI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "e2a43a88-1760-4e63-c35a-5fde24840c82"
      },
      "source": [
        "iris_data.describe()"
      ],
      "execution_count": 35,
      "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>sepal_length_cm</th>\n",
              "      <th>sepal_width_cm</th>\n",
              "      <th>petal_length_cm</th>\n",
              "      <th>petal_width_cm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>145.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.644627</td>\n",
              "      <td>3.054667</td>\n",
              "      <td>3.758667</td>\n",
              "      <td>1.236552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.312781</td>\n",
              "      <td>0.433123</td>\n",
              "      <td>1.764420</td>\n",
              "      <td>0.755058</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.055000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.100000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>0.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.700000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>1.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.400000</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>1.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.900000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>6.900000</td>\n",
              "      <td>2.500000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm\n",
              "count       150.000000      150.000000       150.000000      145.000000\n",
              "mean          5.644627        3.054667         3.758667        1.236552\n",
              "std           1.312781        0.433123         1.764420        0.755058\n",
              "min           0.055000        2.000000         1.000000        0.100000\n",
              "25%           5.100000        2.800000         1.600000        0.400000\n",
              "50%           5.700000        3.000000         4.350000        1.300000\n",
              "75%           6.400000        3.300000         5.100000        1.800000\n",
              "max           7.900000        4.400000         6.900000        2.500000"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BEA8e8aRoss7"
      },
      "source": [
        "We can see several useful values from this table. For example, we see that five `petal_width_cm` entries are missing.\n",
        "\n",
        "However, tables like this are rarely useful unless we know that our data should fall in a particular range. It's usually better to visualize the data in some way. Visualization makes outliers and errors immediately stand out, whereas they might go unnoticed in a large table of numbers."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ab2I-2roo1uw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 884
        },
        "outputId": "2ad7b5fe-d11f-4915-d179-adb8e0c379eb"
      },
      "source": [
        "sns.pairplot(iris_data.dropna(), hue='class')"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:306: UserWarning: Dataset has 0 variance; skipping density estimate.\n",
            "  warnings.warn(msg, UserWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:306: UserWarning: Dataset has 0 variance; skipping density estimate.\n",
            "  warnings.warn(msg, UserWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:306: UserWarning: Dataset has 0 variance; skipping density estimate.\n",
            "  warnings.warn(msg, UserWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:306: UserWarning: Dataset has 0 variance; skipping density estimate.\n",
            "  warnings.warn(msg, UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f66eab910d0>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 823.25x720 with 20 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F2EWDQb3pFjw"
      },
      "source": [
        "From the scatterplot matrix, we can already see some issues with the data set:\n",
        "\n",
        "1. There are five classes when there should only be three, meaning there were some coding errors.\n",
        "\n",
        "2. There are some clear outliers in the measurements that may be erroneous: one `sepal_width_cm` entry for `Iris-setosa` falls well outside its normal range, and several `sepal_length_cm` entries for `Iris-versicolor` are near-zero for some reason.\n",
        "\n",
        "3. We had to drop those rows with missing values.\n",
        "\n",
        "In all of these cases, we need to figure out what to do with the erroneous data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0uIblWAypg4o"
      },
      "source": [
        "### Step 2: Cleaning the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U4bisVfkpkpI"
      },
      "source": [
        "Now that we've identified several errors in the data set, we need to fix them before we proceed with the analysis. Let's walk through the issues one-by-one."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z4_LBsg2pwuY"
      },
      "source": [
        "#### Fixing Class names"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rLVkk82spoVH"
      },
      "source": [
        "After talking with the field researchers, it sounds like one of them forgot to add `Iris-` before their `Iris-versicolor` entries. The other extraneous class, `Iris-setossa`, was simply a typo that they forgot to fix.\n",
        "\n",
        "Let's use the DataFrame to fix these errors."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jnyEvWh1pfiI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "45dceb3f-6251-47c8-c37f-49fb991ebb36"
      },
      "source": [
        "iris_data.loc[iris_data['class'] == 'versicolor', 'class'] = 'Iris-versicolor'\n",
        "iris_data.loc[iris_data['class'] == 'Iris-setossa', 'class'] = 'Iris-setosa'\n",
        "\n",
        "iris_data['class'].unique()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x_wkEA5Ipurx"
      },
      "source": [
        "#### Fixing outliers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MzoCyTFhp5c4"
      },
      "source": [
        "Fixing outliers can be tricky business. It's rarely clear whether the outlier was caused by measurement error, recording the data in improper units, or if the outlier is a real anomaly. For that reason, we should be judicious when working with outliers: if we decide to exclude any data, we need to make sure to document what data we excluded and provide solid reasoning for excluding that data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nMUcOed6p78_"
      },
      "source": [
        "In the case of the one anomalous entry for `Iris-setosa`, let's say our field researchers know that it's impossible for `Iris-setosa` to have a sepal width below 2.5 cm. Clearly this entry was made in error, and we're better off just scrapping the entry than spending hours finding out what happened."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "74VX_jTup11w",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "542f6044-abf2-4e02-96f1-e077c44aa7d4"
      },
      "source": [
        "# This line drops any 'Iris-setosa' rows with a separal width less than 2.5 cm\n",
        "iris_data = iris_data.loc[(iris_data['class'] != 'Iris-setosa') | (iris_data['sepal_width_cm'] >= 2.5)]\n",
        "iris_data.loc[iris_data['class'] == 'Iris-setosa', 'sepal_width_cm'].hist()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f66dc5c9350>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_xpPptbjqHjF"
      },
      "source": [
        "The next data issue to address is the several near-zero sepal lengths for the `Iris-versicolor` rows. Let's take a look at those rows."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hUAKocYVqBxL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "f78f21fe-fa02-41e0-9b89-abe93197df7d"
      },
      "source": [
        "iris_data.loc[(iris_data['class'] == 'Iris-versicolor') &\n",
        "              (iris_data['sepal_length_cm'] < 1.0)]"
      ],
      "execution_count": 39,
      "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>sepal_length_cm</th>\n",
              "      <th>sepal_width_cm</th>\n",
              "      <th>petal_length_cm</th>\n",
              "      <th>petal_width_cm</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>0.067</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.7</td>\n",
              "      <td>Iris-versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>0.060</td>\n",
              "      <td>2.9</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>Iris-versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>0.057</td>\n",
              "      <td>2.6</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Iris-versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>0.055</td>\n",
              "      <td>2.4</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1.1</td>\n",
              "      <td>Iris-versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>0.055</td>\n",
              "      <td>2.4</td>\n",
              "      <td>3.7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Iris-versicolor</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    sepal_length_cm  sepal_width_cm  ...  petal_width_cm            class\n",
              "77            0.067             3.0  ...             1.7  Iris-versicolor\n",
              "78            0.060             2.9  ...             1.5  Iris-versicolor\n",
              "79            0.057             2.6  ...             1.0  Iris-versicolor\n",
              "80            0.055             2.4  ...             1.1  Iris-versicolor\n",
              "81            0.055             2.4  ...             1.0  Iris-versicolor\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PEnQTOTNqN1y"
      },
      "source": [
        "All of these near-zero `sepal_length_cm` entries seem to be off by two orders of magnitude, as if they had been recorded in meters instead of centimeters.\n",
        "\n",
        "After some brief correspondence with the field researchers, we find that one of them forgot to convert those measurements to centimeters. Let's do that for them."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uOWTZXZXqS-Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "e558ae9e-e9d6-4602-efe0-b7f8ead0cbb2"
      },
      "source": [
        "iris_data.loc[(iris_data['class'] == 'Iris-versicolor') &\n",
        "              (iris_data['sepal_length_cm'] < 1.0),\n",
        "              'sepal_length_cm'] *= 100.0\n",
        "\n",
        "iris_data.loc[iris_data['class'] == 'Iris-versicolor', 'sepal_length_cm'].hist()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f66dc5d1890>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e1ACMz_TqXRq"
      },
      "source": [
        "#### Handling Missing Values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rr1SkpHVqenj"
      },
      "source": [
        "Let's take a look at the rows with missing values:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZR5jad4JqZxC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "88fc3dae-abc8-4ce3-c0d0-552e8a972bb1"
      },
      "source": [
        "iris_data.loc[(iris_data['sepal_length_cm'].isnull()) |\n",
        "              (iris_data['sepal_width_cm'].isnull()) |\n",
        "              (iris_data['petal_length_cm'].isnull()) |\n",
        "              (iris_data['petal_width_cm'].isnull())]"
      ],
      "execution_count": 41,
      "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>sepal_length_cm</th>\n",
              "      <th>sepal_width_cm</th>\n",
              "      <th>petal_length_cm</th>\n",
              "      <th>petal_width_cm</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>4.4</td>\n",
              "      <td>2.9</td>\n",
              "      <td>1.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.7</td>\n",
              "      <td>1.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>4.8</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    sepal_length_cm  sepal_width_cm  ...  petal_width_cm        class\n",
              "7               5.0             3.4  ...             NaN  Iris-setosa\n",
              "8               4.4             2.9  ...             NaN  Iris-setosa\n",
              "9               4.9             3.1  ...             NaN  Iris-setosa\n",
              "10              5.4             3.7  ...             NaN  Iris-setosa\n",
              "11              4.8             3.4  ...             NaN  Iris-setosa\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eER2CdgcqgD4"
      },
      "source": [
        "It's not ideal that we had to drop those rows, especially considering they're all `Iris-setosa` entries. Since it seems like the missing data is systematic — all of the missing values are in the same column for the same Iris type — this error could potentially bias our analysis.\n",
        "\n",
        "One way to deal with missing data is mean imputation: If we know that the values for a measurement fall in a certain range, we can fill in empty values with the average of that measurement."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s3TWd262qqwq"
      },
      "source": [
        "Let's see if we can do that here:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jKdupXg0qfVK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "outputId": "2b1b30b9-ba80-4b47-e3d9-970c2982a7e4"
      },
      "source": [
        "iris_data.loc[iris_data['class'] == 'Iris-setosa', 'petal_width_cm'].hist()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f66dad55310>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD7CAYAAABzGc+QAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAOBElEQVR4nO3df4xl9VnH8fdTtv3DHQo0295sVtKphtYgVOqO2qSJmUmrWSEBmjakBAmk1KmmaJP2DzetiUTSiFowBvnDrRAwoR2NorsCtiLZKcGIcRe3zC6kUnBVNttd6dKVQfyBPv4xZ810mN1759xf+3Dfr+Rm7zn3nPt9nnwvnzmce85MZCaSpHreNO4CJEntGOCSVJQBLklFGeCSVJQBLklFGeCSVFTXAI+ICyNib0Q8HRGHIuLTzfpbIuJIRBxoHpcPv1xJ0inR7TrwiNgKbM3MJyPiXGA/cDVwDbCcmV8cfpmSpLU2ddsgM48CR5vnL0fEM8C2NoNt2bIlp6en2+zKK6+8wubNm1vtW5U9TwZ7ngz99Lx///4XM/Ptr3shM3t+ANPAPwNvBW4BDgNPAfcAF3Tbf/v27dnW3r17W+9blT1PBnueDP30DOzLdTK16ymUUyJiCvg68IXMfCAiOsCLQAK3snKa5ePr7DcPzAN0Op3tCwsLG/nB8/+Wl5eZmppqtW9V9jwZ7Hky9NPz3Nzc/syced0L66X62gfwZuBrwGdO8/o0cLDb+3gEvjH2PBnseTIM4wi8l6tQArgbeCYz71i1fuuqzT4MHGz1o0WS1ErXLzGBDwDXA0sRcaBZ9zng2oi4jJVTKIeBTw6lQknSunq5CuVxINZ56eHBlyNJ6pV3YkpSUQa4JBVlgEtSUQa4JBXVy1UoGpOlIye5cedDIx/38G1XjHxMSRvnEbgkFWWAS1JRBrgkFWWAS1JRBrgkFWWAS1JRBrgkFWWAS1JRBrgkFWWAS1JRBrgkFWWAS1JRBrgkFWWAS1JRBrgkFWWAS1JRBrgkFWWAS1JRBrgkFWWAS1JRBrgkFWWAS1JRBrgkFWWAS1JRBrgkFWWAS1JRBrgkFWWAS1JRBrgkFdU1wCPiwojYGxFPR8ShiPh0s/5tEfFIRDzb/HvB8MuVJJ3SyxH4a8BnM/Ni4P3ApyLiYmAn8GhmXgQ82ixLkkaka4Bn5tHMfLJ5/jLwDLANuAq4r9nsPuDqYRUpSXq9DZ0Dj4hp4H3A3wKdzDzavPRtoDPQyiRJZxSZ2duGEVPA14EvZOYDEfHdzDx/1esvZebrzoNHxDwwD9DpdLYvLCy0KnR5eZmpqalW+1Z1/MRJjr06+nEv3Xbe6AdtTOI82/Nk6Kfnubm5/Zk5s3b9pl52jog3A38C3J+ZDzSrj0XE1sw8GhFbgePr7ZuZu4BdADMzMzk7O9umfhYXF2m7b1V33r+b25d6mqKBOnzd7MjHPGUS59meJ8Mweu7lKpQA7gaeycw7Vr20B7iheX4DsHuglUmSzqiXw7sPANcDSxFxoFn3OeA24I8i4ibgn4BrhlOiJGk9XQM8Mx8H4jQvf3Cw5UiSeuWdmJJUlAEuSUUZ4JJUlAEuSUUZ4JJUlAEuSUUZ4JJUlAEuSUUZ4JJUlAEuSUUZ4JJUlAEuSUUZ4JJUlAEuSUUZ4JJUlAEuSUUZ4JJUlAEuSUUZ4JJUlAEuSUUZ4JJUlAEuSUUZ4JJUlAEuSUUZ4JJUlAEuSUUZ4JJUlAEuSUUZ4JJUlAEuSUUZ4JJUlAEuSUUZ4JJUlAEuSUV1DfCIuCcijkfEwVXrbomIIxFxoHlcPtwyJUlr9XIEfi+wY531v52ZlzWPhwdbliSpm64BnpmPASdGUIskaQP6OQd+c0Q81ZxiuWBgFUmSehKZ2X2jiGngwcy8pFnuAC8CCdwKbM3Mj59m33lgHqDT6WxfWFhoVejy8jJTU1Ot9q3q+ImTHHt19ONeuu280Q/amMR5tufJ0E/Pc3Nz+zNzZu36TW3eLDOPnXoeEV8CHjzDtruAXQAzMzM5OzvbZkgWFxdpu29Vd96/m9uXWk1RXw5fNzvyMU+ZxHm258kwjJ5bnUKJiK2rFj8MHDzdtpKk4eh6eBcRXwFmgS0R8QLwq8BsRFzGyimUw8Anh1ijJGkdXQM8M69dZ/XdQ6hFkrQB3okpSUUZ4JJUlAEuSUUZ4JJUlAEuSUUZ4JJUlAEuSUUZ4JJUlAEuSUUZ4JJUlAEuSUUZ4JJUlAEuSUUZ4JJUlAEuSUUZ4JJUlAEuSUUZ4JJUlAEuSUUZ4JJUlAEuSUUZ4JJUlAEuSUUZ4JJUlAEuSUUZ4JJUlAEuSUUZ4JJUlAEuSUUZ4JJUlAEuSUUZ4JJUlAEuSUUZ4JJUlAEuSUV1DfCIuCcijkfEwVXr3hYRj0TEs82/Fwy3TEnSWr0cgd8L7FizbifwaGZeBDzaLEuSRqhrgGfmY8CJNauvAu5rnt8HXD3guiRJXbQ9B97JzKPN828DnQHVI0nqUWRm940ipoEHM/OSZvm7mXn+qtdfysx1z4NHxDwwD9DpdLYvLCy0KnR5eZmpqalW+1Z1/MRJjr06+nEv3Xbe6AdtTOI82/Nk6Kfnubm5/Zk5s3b9ppa1HIuIrZl5NCK2AsdPt2Fm7gJ2AczMzOTs7GyrARcXF2m7b1V33r+b25faTlF7h6+bHfmYp0ziPNvzZBhGz21PoewBbmie3wDsHkw5kqRe9XIZ4VeAvwHeExEvRMRNwG3AT0XEs8CHmmVJ0gh1/f/zzLz2NC99cMC1SJI2wDsxJakoA1ySijLAJakoA1ySijLAJakoA1ySijLAJakoA1ySijLAJakoA1ySijLAJakoA1ySijLAJakoA1ySijLAJakoA1ySijLAJakoA1ySijLAJakoA1ySiur6R40lDdfSkZPcuPOhkY97+LYrRj6mBssjcEkqygCXpKIMcEkqygCXpKIMcEkqygCXpKIMcEkqygCXpKK8kUevMz2Gm0pOuXfH5rGNLVXjEbgkFWWAS1JRBrgkFWWAS1JRfX2JGRGHgZeB/wFey8yZQRQlSepuEFehzGXmiwN4H0nSBngKRZKK6jfAE/jLiNgfEfODKEiS1JvIzPY7R2zLzCMR8Q7gEeAXM/OxNdvMA/MAnU5n+8LCQquxjp84ybFXW5fal0u3nTeWccfZ87i867xzmJqaGncZIzWueR7X5xpgeXl54ua5n57n5ub2r/cdY18B/j1vFHELsJyZXzzdNjMzM7lv375W73/n/bu5fWk8N46O609PjbPncbl3x2ZmZ2fHXcZIjWuex/kn1RYXFydunvvpOSLWDfDWp1AiYnNEnHvqOfDTwMG27ydJ2ph+fux3gD+NiFPv8+XM/OpAqpIkddU6wDPzeeBHBliLJGkDvIxQkooywCWpKANckooywCWpKANckooywCWpKANckooywCWpKANckooywCWpKANckooywCWpKANckooywCWpKANckooywCWpKANckooywCWpKANckooywCWpKANckooywCWpKANckooywCWpKANckooywCWpKANckooywCWpKANckoraNO4CJE2epSMnuXHnQ+MuY6Tu3bF54O/pEbgkFWWAS1JRBrgkFWWAS1JRfQV4ROyIiG9GxLciYuegipIkddc6wCPiHOAu4GeAi4FrI+LiQRUmSTqzfo7Afxz4VmY+n5n/BSwAVw2mLElSN/0E+DbgX1Ytv9CskySNQGRmux0jPgrsyMxPNMvXAz+RmTev2W4emG8W3wN8s2WtW4AXW+5blT1PBnueDP30/M7MfPvalf3ciXkEuHDV8vc3675HZu4CdvUxDgARsS8zZ/p9n0rseTLY82QYRs/9nEL5O+CiiHhXRLwF+BiwZzBlSZK6aX0EnpmvRcTNwNeAc4B7MvPQwCqTJJ1RX7/MKjMfBh4eUC3d9H0apiB7ngz2PBkG3nPrLzElSePlrfSSVNRZF+Ddbs+PiJ+MiCcj4rXmUsbyeuj5MxHxdEQ8FRGPRsQ7x1HnIPXQ889HxFJEHIiIx98Id/n2+qsnIuIjEZERUfoqjR7m+MaI+Ndmjg9ExCfGUecg9TLHEXFN89/zoYj4cl8DZuZZ82Dly9DngB8A3gJ8A7h4zTbTwHuBPwA+Ou6aR9TzHPB9zfNfAP5w3HWPoOe3rnp+JfDVcdc97J6b7c4FHgOeAGbGXfeQ5/hG4HfHXeuIe74I+Hvggmb5Hf2MebYdgXe9PT8zD2fmU8D/jqPAIeil572Z+e/N4hOsXHNfWS89/9uqxc1A9S9rev3VE7cCvwH8xyiLG4JJ/FUbvfT8c8BdmfkSQGYe72fAsy3AJ/H2/I32fBPwF0OtaPh66jkiPhURzwG/CfzSiGoblq49R8SPAhdm5hvhb431+rn+SHNq8I8j4sJ1Xq+kl57fDbw7Iv46Ip6IiB39DHi2BbjOICJ+FpgBfmvctYxCZt6VmT8I/DLwK+OuZ5gi4k3AHcBnx13LCP05MJ2Z7wUeAe4bcz2jsImV0yizwLXAlyLi/LZvdrYFeE+357/B9NRzRHwI+DxwZWb+54hqG5aNzvMCcPVQKxq+bj2fC1wCLEbEYeD9wJ7CX2R2nePM/M6qz/LvA9tHVNuw9PK5fgHYk5n/nZn/CPwDK4HeytkW4JN4e37XniPifcDvsRLefZ0zO0v00vPqD/UVwLMjrG8YzthzZp7MzC2ZOZ2Z06x813FlZu4bT7l962WOt65avBJ4ZoT1DUMv+fVnrBx9ExFbWDml8nzrEcf9ze063+RezspPpeeAzzfrfo2VDzPAj7HyU+wV4DvAoXHXPIKe/wo4BhxoHnvGXfMIev4d4FDT717gh8dd87B7XrPtIoWvQulxjn+9meNvNHP8Q+OueQQ9Byunyp4GloCP9TOed2JKUlFn2ykUSVKPDHBJKsoAl6SiDHBJKsoAl6SiDHBJKsoAl6SiDHBJKur/AEVYCdJznOA4AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O4pGDsgBqvKF"
      },
      "source": [
        "Most of the petal widths for `Iris-setosa` fall within the 0.2-0.3 range, so let's fill in these entries with the average measured petal width."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MJNI1m7aqwMJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "4272cf59-2c87-4931-aba8-2d2728491576"
      },
      "source": [
        "average_petal_width = iris_data.loc[iris_data['class'] == 'Iris-setosa', 'petal_width_cm'].mean()\n",
        "\n",
        "iris_data.loc[(iris_data['class'] == 'Iris-setosa') &\n",
        "              (iris_data['petal_width_cm'].isnull()),\n",
        "              'petal_width_cm'] = average_petal_width\n",
        "\n",
        "iris_data.loc[(iris_data['class'] == 'Iris-setosa') &\n",
        "              (iris_data['petal_width_cm'] == average_petal_width)]"
      ],
      "execution_count": 43,
      "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>sepal_length_cm</th>\n",
              "      <th>sepal_width_cm</th>\n",
              "      <th>petal_length_cm</th>\n",
              "      <th>petal_width_cm</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.25</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>4.4</td>\n",
              "      <td>2.9</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.25</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.25</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.7</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.25</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>4.8</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.25</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    sepal_length_cm  sepal_width_cm  ...  petal_width_cm        class\n",
              "7               5.0             3.4  ...            0.25  Iris-setosa\n",
              "8               4.4             2.9  ...            0.25  Iris-setosa\n",
              "9               4.9             3.1  ...            0.25  Iris-setosa\n",
              "10              5.4             3.7  ...            0.25  Iris-setosa\n",
              "11              4.8             3.4  ...            0.25  Iris-setosa\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j9bmsiZVq0Ko",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "outputId": "a581cb53-37fb-4ece-859b-24f23d33a0e9"
      },
      "source": [
        "# Checking again for any missing values\n",
        "iris_data.loc[(iris_data['sepal_length_cm'].isnull()) |\n",
        "              (iris_data['sepal_width_cm'].isnull()) |\n",
        "              (iris_data['petal_length_cm'].isnull()) |\n",
        "              (iris_data['petal_width_cm'].isnull())]"
      ],
      "execution_count": 44,
      "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>sepal_length_cm</th>\n",
              "      <th>sepal_width_cm</th>\n",
              "      <th>petal_length_cm</th>\n",
              "      <th>petal_width_cm</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [sepal_length_cm, sepal_width_cm, petal_length_cm, petal_width_cm, class]\n",
              "Index: []"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gFfvEcJlq9XP"
      },
      "source": [
        "After all this work, let's save this DataFrame into a new variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mvPYz9pAq3-X"
      },
      "source": [
        "iris_data_clean = iris_data"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tmkXnCLQrF1t"
      },
      "source": [
        "Now, let's take a look at the scatterplot matrix now that we've tidied the data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ALk9GqJKrGAY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 743
        },
        "outputId": "396ab715-32a1-4dd4-f1c2-2c1b9a20b9f8"
      },
      "source": [
        "sns.pairplot(iris_data_clean, hue='class')"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f66dac4b1d0>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 823.25x720 with 20 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bvZABBh2rWcf"
      },
      "source": [
        "### Step 3: Exploratory Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "87DV4RSIrdOZ"
      },
      "source": [
        "Now after spending entirely too much time tidying our data, we can start analyzing it. Exploratory analysis is the step where we start delving deeper into the data set beyond the outliers and errors, we analyze things like correlations between various features of the data, confounding factors that explain these correlations, etc."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vpmhKrHWrsUc"
      },
      "source": [
        "Let's return to that scatterplot matrix that we used earlier."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EKlZlAdBrZKv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 743
        },
        "outputId": "9226f057-022f-4c60-9df1-3e819bbd69c9"
      },
      "source": [
        "sns.pairplot(iris_data_clean, hue='class')"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f66da4cc110>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 823.25x720 with 20 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o2tUj2HYr22v"
      },
      "source": [
        "There's a strange distribution of the petal measurements because of the different species. This is actually great news for our classification task since it means that the petal measurements will make it easy to distinguish between `Iris-setosa` and the other `Iris` types. Distinguishing `Iris-versicolor` and `Iris-virginica` will prove more difficult given how much their measurements overlap."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4a7j9plHsNzi"
      },
      "source": [
        "There are also correlations between petal length and petal width, as well as sepal length and sepal width. The field biologists assure us that this is to be expected: Longer flower petals also tend to be wider, and the same applies for sepals.\n",
        "\n",
        "We can also make violin plots of the data to compare the measurement distributions of the classes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fZCMEQMMr2rB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "outputId": "864dc61d-ffa7-4a25-d11d-80d1602f48c6"
      },
      "source": [
        "plt.figure(figsize=(10, 10))\n",
        "\n",
        "for column_index, column in enumerate(iris_data_clean.columns):\n",
        "    if column == 'class':\n",
        "        continue\n",
        "    plt.subplot(2, 2, column_index + 1)\n",
        "    sns.violinplot(x='class', y=column, data=iris_data_clean)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r0IrLmWUsav_"
      },
      "source": [
        "### Step 4: Classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wMgebXMqslOQ"
      },
      "source": [
        "As tiresome as it can be, tidying and exploring our data is a vital component to any data analysis. If we had jumped straight to the modeling step, we would have created a faulty classification model. Remember, your model is as good as the data you feed it."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_qlLYzMGszj6"
      },
      "source": [
        "As the first step, let's split the data into a training set and testing set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Klvbr46sgd4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "959ffe2d-9d50-4334-be3d-42b30c7e36f9"
      },
      "source": [
        "# We're using all four measurements as inputs\n",
        "# Note that scikit-learn expects each entry to be a list of values, e.g.,\n",
        "# [ [val1, val2, val3],\n",
        "#   [val1, val2, val3],\n",
        "#   ... ]\n",
        "# such that our input data set is represented as a list of lists\n",
        "\n",
        "# We can extract the data in this format from pandas like this:\n",
        "all_inputs = iris_data_clean[['sepal_length_cm', 'sepal_width_cm',\n",
        "                             'petal_length_cm', 'petal_width_cm']].values\n",
        "\n",
        "# Similarly, we can extract the class labels\n",
        "all_labels = iris_data_clean['class'].values\n",
        "\n",
        "# Make sure that you don't mix up the order of the entries\n",
        "# all_inputs[5] inputs should correspond to the class in all_labels[5]\n",
        "\n",
        "# Here's what a subset of our inputs looks like:\n",
        "all_inputs[:5]"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5.1, 3.5, 1.4, 0.2],\n",
              "       [4.9, 3. , 1.4, 0.2],\n",
              "       [4.7, 3.2, 1.3, 0.2],\n",
              "       [4.6, 3.1, 1.5, 0.2],\n",
              "       [5. , 3.6, 1.4, 0.2]])"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Iph5pMDhs624"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "(training_inputs,\n",
        " testing_inputs,\n",
        " training_classes,\n",
        " testing_classes) = train_test_split(all_inputs, all_labels, test_size=0.25)"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tsPjLoyis_m6"
      },
      "source": [
        "With our data split, we can start fitting models to our data. Here's where you come in. Our company's Head of Data is all about eco-friendly and putting a stop to pollution. As a result, he loves tree-based classifiers. You have to build a tree-based classifier and train it on the training set. A template for the same has been provided below.\n",
        "\n",
        "Tweak various parameters until you achieve a good enough score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "anUfxwDst-Yf"
      },
      "source": [
        "#from sklearn.datasets import make_classification\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "# Create the classifier\n",
        "classifier = RandomForestClassifier(n_estimators = 50, criterion = 'entropy', random_state=0)"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vl8c8gits9qA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "910ca737-8a81-46b5-d0a1-e17a39632612"
      },
      "source": [
        "# Train the classifier on the training set\n",
        "classifier.fit(training_inputs, training_classes)\n",
        "\n",
        "# Validate the classifier on the testing set using classification accuracy\n",
        "classifier.score(testing_inputs, testing_classes)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9736842105263158"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    }
  ]
}