cohorts/2022/03-classification/homework_3.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1RP5ocnmvaNJ"
      },
      "source": [
        "**INITIALIZATION:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "metadata": {
        "id": "9UkFwRCfs0oo"
      },
      "outputs": [],
      "source": [
        "#@ IMPORTING LIBRARIES AND DEPENDENCIES:\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.linear_model import Ridge\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import mutual_info_score\n",
        "from sklearn.feature_extraction import DictVectorizer\n",
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XAWt3FaGvi2V"
      },
      "source": [
        "**IMPORTING DATASET:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "id": "qZ3ySstmvWhk"
      },
      "outputs": [],
      "source": [
        "#@ IMPORTING DATASET:\n",
        "PATH = \"./housing.csv\"\n",
        "select_cols = [\"longitude\", \"latitude\", \"housing_median_age\", \"total_rooms\", \"total_bedrooms\", \"population\", \"households\", \n",
        "               \"median_income\", \"median_house_value\", \"ocean_proximity\"]\n",
        "data = pd.read_csv(PATH, usecols=select_cols)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "colab": {
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        "id": "jQ_W08Z5xcdr",
        "outputId": "b9e0a6a2-5d4a-4d5a-e964-76b4037d74ae"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(20640, 10)\n"
          ]
        }
      ],
      "source": [
        "#@ INSPECTING DATAFRAME:\n",
        "print(data.shape)"
      ]
    },
    {
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      "execution_count": 76,
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>median_house_value</th>\n",
              "      <th>ocean_proximity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-122.23</td>\n",
              "      <td>37.88</td>\n",
              "      <td>41.0</td>\n",
              "      <td>880.0</td>\n",
              "      <td>129.0</td>\n",
              "      <td>322.0</td>\n",
              "      <td>126.0</td>\n",
              "      <td>8.3252</td>\n",
              "      <td>452600.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-122.22</td>\n",
              "      <td>37.86</td>\n",
              "      <td>21.0</td>\n",
              "      <td>7099.0</td>\n",
              "      <td>1106.0</td>\n",
              "      <td>2401.0</td>\n",
              "      <td>1138.0</td>\n",
              "      <td>8.3014</td>\n",
              "      <td>358500.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-122.24</td>\n",
              "      <td>37.85</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1467.0</td>\n",
              "      <td>190.0</td>\n",
              "      <td>496.0</td>\n",
              "      <td>177.0</td>\n",
              "      <td>7.2574</td>\n",
              "      <td>352100.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-122.25</td>\n",
              "      <td>37.85</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1274.0</td>\n",
              "      <td>235.0</td>\n",
              "      <td>558.0</td>\n",
              "      <td>219.0</td>\n",
              "      <td>5.6431</td>\n",
              "      <td>341300.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-122.25</td>\n",
              "      <td>37.85</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1627.0</td>\n",
              "      <td>280.0</td>\n",
              "      <td>565.0</td>\n",
              "      <td>259.0</td>\n",
              "      <td>3.8462</td>\n",
              "      <td>342200.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>-122.25</td>\n",
              "      <td>37.85</td>\n",
              "      <td>52.0</td>\n",
              "      <td>919.0</td>\n",
              "      <td>213.0</td>\n",
              "      <td>413.0</td>\n",
              "      <td>193.0</td>\n",
              "      <td>4.0368</td>\n",
              "      <td>269700.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>-122.25</td>\n",
              "      <td>37.84</td>\n",
              "      <td>52.0</td>\n",
              "      <td>2535.0</td>\n",
              "      <td>489.0</td>\n",
              "      <td>1094.0</td>\n",
              "      <td>514.0</td>\n",
              "      <td>3.6591</td>\n",
              "      <td>299200.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>-122.25</td>\n",
              "      <td>37.84</td>\n",
              "      <td>52.0</td>\n",
              "      <td>3104.0</td>\n",
              "      <td>687.0</td>\n",
              "      <td>1157.0</td>\n",
              "      <td>647.0</td>\n",
              "      <td>3.1200</td>\n",
              "      <td>241400.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>-122.26</td>\n",
              "      <td>37.84</td>\n",
              "      <td>42.0</td>\n",
              "      <td>2555.0</td>\n",
              "      <td>665.0</td>\n",
              "      <td>1206.0</td>\n",
              "      <td>595.0</td>\n",
              "      <td>2.0804</td>\n",
              "      <td>226700.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>-122.25</td>\n",
              "      <td>37.84</td>\n",
              "      <td>52.0</td>\n",
              "      <td>3549.0</td>\n",
              "      <td>707.0</td>\n",
              "      <td>1551.0</td>\n",
              "      <td>714.0</td>\n",
              "      <td>3.6912</td>\n",
              "      <td>261100.0</td>\n",
              "      <td>NEAR BAY</td>\n",
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              "  </tbody>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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            ],
            "text/plain": [
              "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "0    -122.23     37.88                41.0        880.0           129.0   \n",
              "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
              "2    -122.24     37.85                52.0       1467.0           190.0   \n",
              "3    -122.25     37.85                52.0       1274.0           235.0   \n",
              "4    -122.25     37.85                52.0       1627.0           280.0   \n",
              "5    -122.25     37.85                52.0        919.0           213.0   \n",
              "6    -122.25     37.84                52.0       2535.0           489.0   \n",
              "7    -122.25     37.84                52.0       3104.0           687.0   \n",
              "8    -122.26     37.84                42.0       2555.0           665.0   \n",
              "9    -122.25     37.84                52.0       3549.0           707.0   \n",
              "\n",
              "   population  households  median_income  median_house_value ocean_proximity  \n",
              "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
              "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
              "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
              "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
              "4       565.0       259.0         3.8462            342200.0        NEAR BAY  \n",
              "5       413.0       193.0         4.0368            269700.0        NEAR BAY  \n",
              "6      1094.0       514.0         3.6591            299200.0        NEAR BAY  \n",
              "7      1157.0       647.0         3.1200            241400.0        NEAR BAY  \n",
              "8      1206.0       595.0         2.0804            226700.0        NEAR BAY  \n",
              "9      1551.0       714.0         3.6912            261100.0        NEAR BAY  "
            ]
          },
          "execution_count": 76,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#@ INSPECTING DATAFRAME:\n",
        "data.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "id": "69tSECi6HzlS"
      },
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      "source": [
        "#@ HANDLING MISSING VALUES:\n",
        "median = data[\"total_bedrooms\"].median()                                  # Calculating the median.\n",
        "data[\"total_bedrooms\"].fillna(median, inplace=True)                       # Imputing the median value."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g3Xwa_AJx-AF"
      },
      "source": [
        "**EXPLORATORY DATA ANALYSIS:**"
      ]
    },
    {
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      "execution_count": 78,
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       ocean_proximity\n",
              "count            20640\n",
              "unique               5\n",
              "top          <1H OCEAN\n",
              "freq              9136"
            ]
          },
          "execution_count": 78,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#@ DATAFRAME INFORMATION:\n",
        "data.describe(include=[\"O\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "85IK6VLXJADT",
        "outputId": "0fe17171-1cb8-4a8e-f9d3-6178eca529e5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    <1H OCEAN\n",
              "dtype: object"
            ]
          },
          "execution_count": 79,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#@ CALCULATING MODEL:\n",
        "data['ocean_proximity'].mode()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y00Un2noJoKo"
      },
      "source": [
        "- Answer for Question1: '<1H OCEAN'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "id": "1LNjviIoLSQL",
        "outputId": "656a8395-2a42-49a8-f0fb-3672516a0424"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-7e498939-9abb-4d72-be0b-2b681763a3f0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>median_house_value</th>\n",
              "      <th>ocean_proximity</th>\n",
              "      <th>rooms_per_household</th>\n",
              "      <th>bedrooms_per_room</th>\n",
              "      <th>population_per_household</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-122.23</td>\n",
              "      <td>37.88</td>\n",
              "      <td>41.0</td>\n",
              "      <td>880.0</td>\n",
              "      <td>129.0</td>\n",
              "      <td>322.0</td>\n",
              "      <td>126.0</td>\n",
              "      <td>8.3252</td>\n",
              "      <td>452600.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "      <td>6.984127</td>\n",
              "      <td>0.146591</td>\n",
              "      <td>2.555556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-122.22</td>\n",
              "      <td>37.86</td>\n",
              "      <td>21.0</td>\n",
              "      <td>7099.0</td>\n",
              "      <td>1106.0</td>\n",
              "      <td>2401.0</td>\n",
              "      <td>1138.0</td>\n",
              "      <td>8.3014</td>\n",
              "      <td>358500.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "      <td>6.238137</td>\n",
              "      <td>0.155797</td>\n",
              "      <td>2.109842</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-122.24</td>\n",
              "      <td>37.85</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1467.0</td>\n",
              "      <td>190.0</td>\n",
              "      <td>496.0</td>\n",
              "      <td>177.0</td>\n",
              "      <td>7.2574</td>\n",
              "      <td>352100.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "      <td>8.288136</td>\n",
              "      <td>0.129516</td>\n",
              "      <td>2.802260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-122.25</td>\n",
              "      <td>37.85</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1274.0</td>\n",
              "      <td>235.0</td>\n",
              "      <td>558.0</td>\n",
              "      <td>219.0</td>\n",
              "      <td>5.6431</td>\n",
              "      <td>341300.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "      <td>5.817352</td>\n",
              "      <td>0.184458</td>\n",
              "      <td>2.547945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-122.25</td>\n",
              "      <td>37.85</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1627.0</td>\n",
              "      <td>280.0</td>\n",
              "      <td>565.0</td>\n",
              "      <td>259.0</td>\n",
              "      <td>3.8462</td>\n",
              "      <td>342200.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "      <td>6.281853</td>\n",
              "      <td>0.172096</td>\n",
              "      <td>2.181467</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7e498939-9abb-4d72-be0b-2b681763a3f0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7e498939-9abb-4d72-be0b-2b681763a3f0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7e498939-9abb-4d72-be0b-2b681763a3f0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "0    -122.23     37.88                41.0        880.0           129.0   \n",
              "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
              "2    -122.24     37.85                52.0       1467.0           190.0   \n",
              "3    -122.25     37.85                52.0       1274.0           235.0   \n",
              "4    -122.25     37.85                52.0       1627.0           280.0   \n",
              "\n",
              "   population  households  median_income  median_house_value ocean_proximity  \\\n",
              "0       322.0       126.0         8.3252            452600.0        NEAR BAY   \n",
              "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY   \n",
              "2       496.0       177.0         7.2574            352100.0        NEAR BAY   \n",
              "3       558.0       219.0         5.6431            341300.0        NEAR BAY   \n",
              "4       565.0       259.0         3.8462            342200.0        NEAR BAY   \n",
              "\n",
              "   rooms_per_household  bedrooms_per_room  population_per_household  \n",
              "0             6.984127           0.146591                  2.555556  \n",
              "1             6.238137           0.155797                  2.109842  \n",
              "2             8.288136           0.129516                  2.802260  \n",
              "3             5.817352           0.184458                  2.547945  \n",
              "4             6.281853           0.172096                  2.181467  "
            ]
          },
          "execution_count": 80,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#@ PREPARING DATAFRAME:\n",
        "data[\"rooms_per_household\"] = data[\"total_rooms\"]/data[\"households\"]   # Rooms per Household.\n",
        "data[\"bedrooms_per_room\"] = data[\"total_bedrooms\"]/data[\"total_rooms\"] # Bedrooms per Room.\n",
        "data[\"population_per_household\"] = data[\"population\"]/data[\"households\"] # Populations per Household.\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "fSX3EYwHyW_V",
        "outputId": "d0268b72-88ab-4ce7-9f69-eb8a8b8b2df3"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-9683c93d-ce1b-4f40-b46b-74c8307052c5\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>rooms_per_household</th>\n",
              "      <th>bedrooms_per_room</th>\n",
              "      <th>population_per_household</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-119.569704</td>\n",
              "      <td>35.631861</td>\n",
              "      <td>28.639486</td>\n",
              "      <td>2635.763081</td>\n",
              "      <td>536.838857</td>\n",
              "      <td>1425.476744</td>\n",
              "      <td>499.539680</td>\n",
              "      <td>3.870671</td>\n",
              "      <td>5.429000</td>\n",
              "      <td>0.213794</td>\n",
              "      <td>3.070655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.003532</td>\n",
              "      <td>2.135952</td>\n",
              "      <td>12.585558</td>\n",
              "      <td>2181.615252</td>\n",
              "      <td>419.391878</td>\n",
              "      <td>1132.462122</td>\n",
              "      <td>382.329753</td>\n",
              "      <td>1.899822</td>\n",
              "      <td>2.474173</td>\n",
              "      <td>0.065248</td>\n",
              "      <td>10.386050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-124.350000</td>\n",
              "      <td>32.540000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.499900</td>\n",
              "      <td>0.846154</td>\n",
              "      <td>0.037151</td>\n",
              "      <td>0.692308</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-121.800000</td>\n",
              "      <td>33.930000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>1447.750000</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>787.000000</td>\n",
              "      <td>280.000000</td>\n",
              "      <td>2.563400</td>\n",
              "      <td>4.440716</td>\n",
              "      <td>0.175225</td>\n",
              "      <td>2.429741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-118.490000</td>\n",
              "      <td>34.260000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>2127.000000</td>\n",
              "      <td>435.000000</td>\n",
              "      <td>1166.000000</td>\n",
              "      <td>409.000000</td>\n",
              "      <td>3.534800</td>\n",
              "      <td>5.229129</td>\n",
              "      <td>0.203159</td>\n",
              "      <td>2.818116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>-118.010000</td>\n",
              "      <td>37.710000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>3148.000000</td>\n",
              "      <td>643.250000</td>\n",
              "      <td>1725.000000</td>\n",
              "      <td>605.000000</td>\n",
              "      <td>4.743250</td>\n",
              "      <td>6.052381</td>\n",
              "      <td>0.240126</td>\n",
              "      <td>3.282261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>-114.310000</td>\n",
              "      <td>41.950000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>39320.000000</td>\n",
              "      <td>6445.000000</td>\n",
              "      <td>35682.000000</td>\n",
              "      <td>6082.000000</td>\n",
              "      <td>15.000100</td>\n",
              "      <td>141.909091</td>\n",
              "      <td>2.824675</td>\n",
              "      <td>1243.333333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9683c93d-ce1b-4f40-b46b-74c8307052c5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9683c93d-ce1b-4f40-b46b-74c8307052c5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "          longitude      latitude  housing_median_age   total_rooms  \\\n",
              "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
              "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
              "std        2.003532      2.135952           12.585558   2181.615252   \n",
              "min     -124.350000     32.540000            1.000000      2.000000   \n",
              "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
              "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
              "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
              "max     -114.310000     41.950000           52.000000  39320.000000   \n",
              "\n",
              "       total_bedrooms    population    households  median_income  \\\n",
              "count    20640.000000  20640.000000  20640.000000   20640.000000   \n",
              "mean       536.838857   1425.476744    499.539680       3.870671   \n",
              "std        419.391878   1132.462122    382.329753       1.899822   \n",
              "min          1.000000      3.000000      1.000000       0.499900   \n",
              "25%        297.000000    787.000000    280.000000       2.563400   \n",
              "50%        435.000000   1166.000000    409.000000       3.534800   \n",
              "75%        643.250000   1725.000000    605.000000       4.743250   \n",
              "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
              "\n",
              "       rooms_per_household  bedrooms_per_room  population_per_household  \n",
              "count         20640.000000       20640.000000              20640.000000  \n",
              "mean              5.429000           0.213794                  3.070655  \n",
              "std               2.474173           0.065248                 10.386050  \n",
              "min               0.846154           0.037151                  0.692308  \n",
              "25%               4.440716           0.175225                  2.429741  \n",
              "50%               5.229129           0.203159                  2.818116  \n",
              "75%               6.052381           0.240126                  3.282261  \n",
              "max             141.909091           2.824675               1243.333333  "
            ]
          },
          "execution_count": 81,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#@ INSPECTING DATASET:\n",
        "data_numeric = data.copy()\n",
        "data_numeric = data.drop([\"ocean_proximity\", \"median_house_value\"], axis=1)\n",
        "data_numeric.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ffYEFIjxvGHm"
      },
      "source": [
        "**COORELATION CALCULATION:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "ciTJmhFavFtq",
        "outputId": "2c264f6d-5ecc-46af-e226-5b9810e3e967"
      },
      "outputs": [
        {
          "data": {
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              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
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              "    <tr>\n",
              "      <th>longitude</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.924664</td>\n",
              "      <td>-0.108197</td>\n",
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              "      <th>latitude</th>\n",
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              "    <tr>\n",
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              "      <td>0.135622</td>\n",
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              "    <tr>\n",
              "      <th>total_rooms</th>\n",
              "      <td>0.044568</td>\n",
              "      <td>-0.036100</td>\n",
              "      <td>-0.361262</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>-0.024581</td>\n",
              "    </tr>\n",
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              "      <th>total_bedrooms</th>\n",
              "      <td>0.069120</td>\n",
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              "      <td>-0.319026</td>\n",
              "      <td>0.927058</td>\n",
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              "      <td>0.071649</td>\n",
              "      <td>-0.028325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>population</th>\n",
              "      <td>0.099773</td>\n",
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              "      <td>0.857126</td>\n",
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              "      <td>-0.119034</td>\n",
              "      <td>0.198050</td>\n",
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              "          const element = document.querySelector('#df-4f5dbfa1-c362-44ba-94ca-636e6e0d1e88');\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ],
            "text/plain": [
              "                          longitude  latitude  housing_median_age  \\\n",
              "longitude                  1.000000 -0.924664           -0.108197   \n",
              "latitude                  -0.924664  1.000000            0.011173   \n",
              "housing_median_age        -0.108197  0.011173            1.000000   \n",
              "total_rooms                0.044568 -0.036100           -0.361262   \n",
              "total_bedrooms             0.069120 -0.066484           -0.319026   \n",
              "population                 0.099773 -0.108785           -0.296244   \n",
              "households                 0.055310 -0.071035           -0.302916   \n",
              "median_income             -0.015176 -0.079809           -0.119034   \n",
              "rooms_per_household       -0.027540  0.106389           -0.153277   \n",
              "bedrooms_per_room          0.081205 -0.098619            0.135622   \n",
              "population_per_household   0.002476  0.002366            0.013191   \n",
              "\n",
              "                          total_rooms  total_bedrooms  population  households  \\\n",
              "longitude                    0.044568        0.069120    0.099773    0.055310   \n",
              "latitude                    -0.036100       -0.066484   -0.108785   -0.071035   \n",
              "housing_median_age          -0.361262       -0.319026   -0.296244   -0.302916   \n",
              "total_rooms                  1.000000        0.927058    0.857126    0.918484   \n",
              "total_bedrooms               0.927058        1.000000    0.873535    0.974366   \n",
              "population                   0.857126        0.873535    1.000000    0.907222   \n",
              "households                   0.918484        0.974366    0.907222    1.000000   \n",
              "median_income                0.198050       -0.007617    0.004834    0.013033   \n",
              "rooms_per_household          0.133798        0.001765   -0.072213   -0.080598   \n",
              "bedrooms_per_room           -0.187381        0.071649    0.010035    0.034498   \n",
              "population_per_household    -0.024581       -0.028325    0.069863   -0.027309   \n",
              "\n",
              "                          median_income  rooms_per_household  \\\n",
              "longitude                     -0.015176            -0.027540   \n",
              "latitude                      -0.079809             0.106389   \n",
              "housing_median_age            -0.119034            -0.153277   \n",
              "total_rooms                    0.198050             0.133798   \n",
              "total_bedrooms                -0.007617             0.001765   \n",
              "population                     0.004834            -0.072213   \n",
              "households                     0.013033            -0.080598   \n",
              "median_income                  1.000000             0.326895   \n",
              "rooms_per_household            0.326895             1.000000   \n",
              "bedrooms_per_room             -0.545298            -0.370308   \n",
              "population_per_household       0.018766            -0.004852   \n",
              "\n",
              "                          bedrooms_per_room  population_per_household  \n",
              "longitude                          0.081205                  0.002476  \n",
              "latitude                          -0.098619                  0.002366  \n",
              "housing_median_age                 0.135622                  0.013191  \n",
              "total_rooms                       -0.187381                 -0.024581  \n",
              "total_bedrooms                     0.071649                 -0.028325  \n",
              "population                         0.010035                  0.069863  \n",
              "households                         0.034498                 -0.027309  \n",
              "median_income                     -0.545298                  0.018766  \n",
              "rooms_per_household               -0.370308                 -0.004852  \n",
              "bedrooms_per_room                  1.000000                  0.002601  \n",
              "population_per_household           0.002601                  1.000000  "
            ]
          },
          "execution_count": 82,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#@ INSPECTING CORRELATION:\n",
        "data_numeric.corr()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        },
        "id": "4d_aB6MlvqT6",
        "outputId": "ef081b67-47f1-47a4-e54d-db8e642d811d"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#@ INSPECTING HEATMAP:\n",
        "plt.figure(figsize=(15,10))  \n",
        "sns.heatmap(data_numeric.corr(),annot=True,linewidths=.5, cmap=\"Blues\")\n",
        "plt.title('Heatmap showing correlations between numerical data')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AkAl7M2lyxkW",
        "outputId": "5c6bc6e1-3ca2-4d4d-ec0e-d8737d6500e4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "longitude                 longitude                   1.000000\n",
              "latitude                  latitude                    1.000000\n",
              "bedrooms_per_room         bedrooms_per_room           1.000000\n",
              "rooms_per_household       rooms_per_household         1.000000\n",
              "median_income             median_income               1.000000\n",
              "households                households                  1.000000\n",
              "total_bedrooms            total_bedrooms              1.000000\n",
              "total_rooms               total_rooms                 1.000000\n",
              "housing_median_age        housing_median_age          1.000000\n",
              "population                population                  1.000000\n",
              "population_per_household  population_per_household    1.000000\n",
              "total_bedrooms            households                  0.974366\n",
              "households                total_bedrooms              0.974366\n",
              "total_bedrooms            total_rooms                 0.927058\n",
              "total_rooms               total_bedrooms              0.927058\n",
              "dtype: float64"
            ]
          },
          "execution_count": 84,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#@ INSPECTING CORRELATION:\n",
        "data_numeric.corr().unstack().sort_values(ascending = False)[:15]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vt6WCEC-0OfJ"
      },
      "source": [
        "- Answer for Question2: 'total_bedrooms' and 'households'. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "id": "2GJHYKU50ODl"
      },
      "outputs": [],
      "source": [
        "#@ PROCESSING DATASET:\n",
        "data_class = data.copy()\n",
        "mean = data_class['median_house_value'].mean()\n",
        "\n",
        "data_class['above_average'] = np.where(data_class['median_house_value']>=mean,1,0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "id": "Sh0FsNIS06ym"
      },
      "outputs": [],
      "source": [
        "#@ PROCESSING DATASET:\n",
        "data_class = data_class.drop('median_house_value', axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "metadata": {
        "id": "6RYfeKmp1PfL"
      },
      "outputs": [],
      "source": [
        "#@ SPLITTING THE DATASET:\n",
        "df_train_full, df_test = train_test_split(data_class, test_size=0.2, random_state=42)\n",
        "df_train, df_val = train_test_split(df_train_full, test_size=0.25, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "id": "UgWT9jAa1Zjr"
      },
      "outputs": [],
      "source": [
        "#@ PROCESSING THE DATASET:\n",
        "df_train = df_train.reset_index(drop=True)\n",
        "df_val = df_val.reset_index(drop=True)\n",
        "df_test = df_test.reset_index(drop=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {
        "id": "ZBGzHdVD1f_B"
      },
      "outputs": [],
      "source": [
        "#@ PROCESSING THE DATASET:\n",
        "y_train = df_train.above_average.values\n",
        "y_val = df_val.above_average.values\n",
        "y_test = df_test.above_average.values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QAieBTSC06zL"
      },
      "source": [
        "**MUTUAL INFORMATION:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "GrWi3uy40gLg",
        "outputId": "fe098f56-03d4-435f-eea7-a58588868fc0"
      },
      "outputs": [
        {
          "data": {
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              "      <th></th>\n",
              "      <th>MI</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ocean_proximity</th>\n",
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              "\n",
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            ],
            "text/plain": [
              "                       MI\n",
              "ocean_proximity  0.101384"
            ]
          },
          "execution_count": 90,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#@ PREPARING DATASET:\n",
        "cat = ['ocean_proximity']\n",
        "\n",
        "#@ DEFINING FUNCTION:\n",
        "def calculate_mi(series):\n",
        "    return mutual_info_score(series, df_train.above_average)\n",
        "\n",
        "#@ IMPLEMENTATION:\n",
        "df_mi = df_train[cat].apply(calculate_mi)\n",
        "df_mi = df_mi.sort_values(ascending=False).to_frame(name='MI')\n",
        "df_mi"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-oWxCUes2Oqn"
      },
      "source": [
        "- Answer for Question3: 'ocean_proximity=0.101384'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "id": "7a0HO3m7USvH"
      },
      "outputs": [],
      "source": [
        "#@ PREPARING THE DATASET:\n",
        "df_train = df_train.drop('above_average', axis=1)\n",
        "df_val = df_val.drop('above_average', axis=1)\n",
        "df_test = df_test.drop('above_average', axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MxFu2alh1R4Y"
      },
      "source": [
        "**CLASSIFICATION AND ACCURACY OF MODEL:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "id": "VjZYolWL1PCq"
      },
      "outputs": [],
      "source": [
        "#@ TRANSFORMING THE DATASET:\n",
        "num = [\"longitude\", \"latitude\", \"housing_median_age\", \"total_rooms\", \"total_bedrooms\", \"population\", \"households\", \"median_income\", \"rooms_per_household\", \n",
        "       \"bedrooms_per_room\", \"population_per_household\"]\n",
        "train_dict = df_train[cat + num].to_dict(orient='records')\n",
        "\n",
        "#@ VECTORIZING THE DATASET:\n",
        "dv = DictVectorizer(sparse=False)\n",
        "dv.fit(train_dict)\n",
        "X_train = dv.transform(train_dict)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "he_Rn01H1b8-",
        "outputId": "629e13b6-6890-4f64-c83b-bd16de56fd9c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.84\n"
          ]
        }
      ],
      "source": [
        "#@ TRAINING LOGISTIC REGRESSSION MODEL:\n",
        "model = LogisticRegression(solver=\"liblinear\", C=1.0, max_iter=1000, random_state=42)\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "#@ INITIALIZING THE MODEL PREDICTION:\n",
        "val_dict = df_val[cat + num].to_dict(orient='records')\n",
        "X_val = dv.transform(val_dict)\n",
        "y_pred = model.predict(X_val)\n",
        "\n",
        "#@ INSPECTING THE ACCURACY:\n",
        "accuracy = np.round(accuracy_score(y_val, y_pred),2)\n",
        "print(accuracy)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3ttq6Hcz6vJE"
      },
      "source": [
        "- Answer for Question4: ~= 0.84"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tNyfZPwr6uGb",
        "outputId": "77994134-6ea6-46fd-8fbc-53e7d5890b9c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['ocean_proximity',\n",
              " 'longitude',\n",
              " 'latitude',\n",
              " 'housing_median_age',\n",
              " 'total_rooms',\n",
              " 'total_bedrooms',\n",
              " 'population',\n",
              " 'households',\n",
              " 'median_income',\n",
              " 'rooms_per_household',\n",
              " 'bedrooms_per_room',\n",
              " 'population_per_household']"
            ]
          },
          "execution_count": 94,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#@ INITIALIZING FEATURES:\n",
        "features = cat + num\n",
        "features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G82TR8D7FxHz"
      },
      "source": [
        "**FEATURE ELIMINATION:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L11XM_pmGIgI",
        "outputId": "84ce3f55-072f-4492-cf83-ac5885b14e0e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ocean_proximity 0.019021317829457374 0.8209786821705426\n",
            "longitude 0.009089147286821642 0.8309108527131783\n",
            "latitude 0.00618217054263559 0.8338178294573644\n",
            "housing_median_age 0.009089147286821642 0.8309108527131783\n",
            "total_rooms 0.0015794573643410637 0.8384205426356589\n",
            "total_bedrooms 0.002306201550387521 0.8376937984496124\n",
            "population 0.013691860465116279 0.8263081395348837\n",
            "households 0.006666666666666599 0.8333333333333334\n",
            "median_income 0.05438953488372089 0.7856104651162791\n",
            "rooms_per_household 0.005697674418604581 0.8343023255813954\n",
            "bedrooms_per_room 0.004486434108527115 0.8355135658914729\n",
            "population_per_household 0.004486434108527115 0.8355135658914729\n"
          ]
        }
      ],
      "source": [
        "#@ INSPECTING THE DIFFERENCE IN ACCURACY:\n",
        "orig_score = accuracy\n",
        "\n",
        "for c in features:\n",
        "    subset = features.copy()\n",
        "    subset.remove(c)\n",
        "    \n",
        "    train_dict = df_train[subset].to_dict(orient='records')\n",
        "\n",
        "    dv = DictVectorizer(sparse=False)\n",
        "    dv.fit(train_dict)\n",
        "\n",
        "    X_train = dv.transform(train_dict)\n",
        "\n",
        "    model = LogisticRegression(solver='liblinear', C=1.0, max_iter=1000, random_state=42)\n",
        "    model.fit(X_train, y_train)\n",
        "\n",
        "    val_dict = df_val[subset].to_dict(orient='records')\n",
        "    X_val = dv.transform(val_dict)\n",
        "\n",
        "    y_pred = model.predict(X_val)\n",
        "\n",
        "    score = accuracy_score(y_val, y_pred)\n",
        "    print(c, orig_score - score, score)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mQOkdoy2HiJw"
      },
      "source": [
        "- Answer for Question5: smallest difference - `total rooms`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "id": "r6wFxYueWcco"
      },
      "outputs": [],
      "source": [
        "#@ NORMALIZING THE DATA:\n",
        "data['median_house_value']=np.log1p(data['median_house_value'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {
        "id": "gyftDcMvWrou"
      },
      "outputs": [],
      "source": [
        "#@ SPLITTING THE DATASET:\n",
        "df_train_full, df_test = train_test_split(data, test_size=0.2, random_state=42)\n",
        "df_train, df_val = train_test_split(df_train_full, test_size=0.25, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "id": "dPO-Pb85W1qN"
      },
      "outputs": [],
      "source": [
        "#@ PREPARING THE DATASET:\n",
        "df_train = df_train.reset_index(drop=True)\n",
        "df_val = df_val.reset_index(drop=True)\n",
        "df_test = df_test.reset_index(drop=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "id": "EyjgXwFGW3WO"
      },
      "outputs": [],
      "source": [
        "#@ PREPARING THE DATASET:\n",
        "y_train = df_train.median_house_value.values\n",
        "y_val = df_val.median_house_value.values\n",
        "y_test = df_test.median_house_value.values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "id": "4u2Jmme1W3Qq"
      },
      "outputs": [],
      "source": [
        "#@ DELETING DATASET:\n",
        "del df_train['median_house_value']\n",
        "del df_val['median_house_value']\n",
        "del df_test['median_house_value']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lHPwHninXNLY"
      },
      "source": [
        "**RIDGE REGRESSION:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {
        "id": "XbIYll6mW3HW"
      },
      "outputs": [],
      "source": [
        "#@ PREPARING THE DATASET:\n",
        "train_dict = df_train[cat + num].to_dict(orient='records')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "metadata": {
        "id": "gdW4VCIGXXjK"
      },
      "outputs": [],
      "source": [
        "#@ VECTORIZING THE DATASET:\n",
        "dv = DictVectorizer(sparse=False)\n",
        "dv.fit(train_dict)\n",
        "\n",
        "X_train = dv.transform(train_dict)\n",
        "\n",
        "val_dict = df_val[cat + num].to_dict(orient='records')\n",
        "X_val = dv.transform(val_dict)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 116,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rqMpeRa9XXYA",
        "outputId": "50348620-f291-4056-c921-0c38b31b84f4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0 0.5241\n",
            "0.01 0.5241\n",
            "0.1 0.5241\n",
            "1 0.5241\n",
            "10 0.5241\n"
          ]
        }
      ],
      "source": [
        "#@ RIDGE REGRESSION IMPLEMENTATION:\n",
        "for a in [0, 0.01, 0.1, 1, 10]:\n",
        "    model = Ridge(alpha=a, solver=\"sag\", random_state=42)\n",
        "    model.fit(X_train, y_train)\n",
        "    \n",
        "    y_pred = model.predict(X_val)\n",
        "    \n",
        "    score = np.sqrt(mean_squared_error(y_val, y_pred))\n",
        "    \n",
        "    print(a, round(score, 4))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7V9Bi9vZKeEU"
      },
      "source": [
        "- Answer for Question6: 0."
      ]
    }
  ],
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    "colab": {
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    "kernelspec": {
      "display_name": "Python 3.8.13 ('chatbot')",
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    "vscode": {
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