Linear_Regression.ipynb

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        "<a href=\"https://colab.research.google.com/github/YopaNelly/AI-ML-Basics/blob/main/Linear_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "id": "38f11cd7-411f-4b8c-9de9-f2c3cd7f2036"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "#\n",
        "from sklearn.model_selection import train_test_split\n",
        "# from sklearn.feature_selection import\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import Normalizer, OneHotEncoder, OrdinalEncoder\n",
        "from sklearn.pipeline import Pipeline, make_pipeline\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.experimental import enable_iterative_imputer\n",
        "from sklearn.impute import SimpleImputer, IterativeImputer\n",
        "from sklearn.metrics import mutual_info_score, mean_squared_error, mean_absolute_error, r2_score\n",
        "from IPython.display import display"
      ]
    },
    {
      "cell_type": "code",
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      "id": "46c25a27-2960-49e3-977e-002f8171d9e3",
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        "outputId": "846ab76c-9ff6-41d1-d076-cac071680d1e"
      },
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   bedrooms  bathrooms  parking  size_sqm   age  condition  year_built  \\\n",
              "0       7.0        2.0      NaN     111.0   NaN  excellent      1985.0   \n",
              "1       4.0        3.0      0.0     271.0  40.0       good      1970.0   \n",
              "2       NaN        1.0      2.0     282.0  48.0       poor      2005.0   \n",
              "3       7.0        2.0      0.0      81.0  35.0       good      2018.0   \n",
              "4       3.0        NaN      0.0      68.0  28.0       fair      1975.0   \n",
              "\n",
              "   renovation_year   location  floor_number  distance_to_city_center  \\\n",
              "0           1981.0     Bastos           3.0                 4.714763   \n",
              "1           2011.0       Mvan           2.0                 5.268401   \n",
              "2              NaN        NaN           NaN                 7.680354   \n",
              "3              NaN     Bastos           2.0                 8.042010   \n",
              "4              NaN  Oyomabang           1.0                 9.636422   \n",
              "\n",
              "   proximity_to_schools         price  \n",
              "0              1.774572  30962.533609  \n",
              "1              3.664092   2733.858110  \n",
              "2              2.696442  77878.500593  \n",
              "3                   NaN  44617.709958  \n",
              "4                   NaN  45012.886096  "
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              "      <td>7.0</td>\n",
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              "      <td>111.0</td>\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 10000,\n  \"fields\": [\n    {\n      \"column\": \"bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0041794035653946,\n        \"min\": 1.0,\n        \"max\": 7.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          7.0,\n          4.0,\n          6.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bathrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.816228303043146,\n        \"min\": 1.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2.0,\n          3.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parking\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8130642288643586,\n        \"min\": 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          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# importing the dataset\n",
        "df = pd.read_csv('house_prices_yaounde.csv')\n",
        "# Viewing what the dataset looks like\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "2b819f60-4c88-4330-a7be-cb2ae05ec8f2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "2b819f60-4c88-4330-a7be-cb2ae05ec8f2",
        "outputId": "95a448f1-da8b-4b38-9d4e-c5c3b9551531"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           count          mean           std           min  \\\n",
              "bedrooms                  9000.0      4.016333      2.004179      1.000000   \n",
              "bathrooms                 9000.0      1.991778      0.816228      1.000000   \n",
              "parking                   9000.0      0.999889      0.813064      0.000000   \n",
              "size_sqm                  9000.0    162.476222     78.619007     30.000000   \n",
              "age                       9000.0     25.014000     14.239113      1.000000   \n",
              "year_built                9000.0   1986.446556     21.380124   1950.000000   \n",
              "renovation_year           2068.0   2000.099130     11.717582   1980.000000   \n",
              "floor_number              9000.0      2.488333      1.113055      1.000000   \n",
              "distance_to_city_center   9000.0      7.758767      4.189905      0.501885   \n",
              "proximity_to_schools      9000.0      2.548413      1.429546      0.100494   \n",
              "price                    10000.0  44110.840671  24116.528012 -66509.370542   \n",
              "\n",
              "                                  25%           50%           75%  \\\n",
              "bedrooms                     2.000000      4.000000      6.000000   \n",
              "bathrooms                    1.000000      2.000000      3.000000   \n",
              "parking                      0.000000      1.000000      2.000000   \n",
              "size_sqm                    93.750000    161.000000    232.000000   \n",
              "age                         12.000000     25.000000     38.000000   \n",
              "year_built                1968.000000   1987.000000   2005.000000   \n",
              "renovation_year           1990.000000   2000.000000   2010.000000   \n",
              "floor_number                 1.000000      2.000000      3.000000   \n",
              "distance_to_city_center      4.077821      7.826268     11.411340   \n",
              "proximity_to_schools         1.298501      2.561336      3.801121   \n",
              "price                    27300.457886  44193.569478  60510.309729   \n",
              "\n",
              "                                   max  \n",
              "bedrooms                      7.000000  \n",
              "bathrooms                     3.000000  \n",
              "parking                       2.000000  \n",
              "size_sqm                    299.000000  \n",
              "age                          49.000000  \n",
              "year_built                 2023.000000  \n",
              "renovation_year            2020.000000  \n",
              "floor_number                  4.000000  \n",
              "distance_to_city_center      14.998074  \n",
              "proximity_to_schools          4.998796  \n",
              "price                    133364.405572  "
            ],
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              "      <td>299.000000</td>\n",
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              "    <tr>\n",
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              "      <td>1.000000</td>\n",
              "      <td>12.000000</td>\n",
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              "      <td>38.000000</td>\n",
              "      <td>49.000000</td>\n",
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              "    <tr>\n",
              "      <th>year_built</th>\n",
              "      <td>9000.0</td>\n",
              "      <td>1986.446556</td>\n",
              "      <td>21.380124</td>\n",
              "      <td>1950.000000</td>\n",
              "      <td>1968.000000</td>\n",
              "      <td>1987.000000</td>\n",
              "      <td>2005.000000</td>\n",
              "      <td>2023.000000</td>\n",
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              "    <tr>\n",
              "      <th>renovation_year</th>\n",
              "      <td>2068.0</td>\n",
              "      <td>2000.099130</td>\n",
              "      <td>11.717582</td>\n",
              "      <td>1980.000000</td>\n",
              "      <td>1990.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2010.000000</td>\n",
              "      <td>2020.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>floor_number</th>\n",
              "      <td>9000.0</td>\n",
              "      <td>2.488333</td>\n",
              "      <td>1.113055</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>distance_to_city_center</th>\n",
              "      <td>9000.0</td>\n",
              "      <td>7.758767</td>\n",
              "      <td>4.189905</td>\n",
              "      <td>0.501885</td>\n",
              "      <td>4.077821</td>\n",
              "      <td>7.826268</td>\n",
              "      <td>11.411340</td>\n",
              "      <td>14.998074</td>\n",
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              "      <td>0.100494</td>\n",
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              "      <td>3.801121</td>\n",
              "      <td>4.998796</td>\n",
              "    </tr>\n",
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              "      <td>24116.528012</td>\n",
              "      <td>-66509.370542</td>\n",
              "      <td>27300.457886</td>\n",
              "      <td>44193.569478</td>\n",
              "      <td>60510.309729</td>\n",
              "      <td>133364.405572</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2141.346730023379,\n        \"min\": 2068.0,\n        \"max\": 10000.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          9000.0,\n          2068.0,\n          10000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13197.036555790137,\n        \"min\": 0.9998888888888889,\n        \"max\": 44110.84067086536,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          1986.4465555555555,\n          4.016333333333334,\n          2.548413128854608\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7267.331935131964,\n        \"min\": 0.8130642288643586,\n        \"max\": 24116.528012218623,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          21.38012421472507,\n          2.0041794035653946,\n          1.429545538882562\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20188.1094370584,\n        \"min\": -66509.37054160207,\n        \"max\": 1980.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.0,\n          0.501885163426549,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"25%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8146.625691843801,\n        \"min\": 0.0,\n        \"max\": 27300.457886337157,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          1.298500657881991,\n          1.0,\n          1968.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13221.989074132682,\n        \"min\": 1.0,\n        \"max\": 44193.569477922625,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          2.5613355951193926,\n          2.0,\n          1987.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"75%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18131.72164875118,\n        \"min\": 2.0,\n        \"max\": 60510.30972941671,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          3.8011206430648152,\n          3.0,\n          2005.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40085.26780734149,\n        \"min\": 2.0,\n        \"max\": 133364.405571969,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          2023.0,\n          7.0,\n          4.998795689695913\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# viewing dataset description for insights on the data\n",
        "df.describe().T"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "cad0a866-0bc8-483a-b6f8-cbbf7ef4279f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cad0a866-0bc8-483a-b6f8-cbbf7ef4279f",
        "outputId": "3d920e0f-9dbe-43e6-a399-fc97c5dc26c1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 13 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   bedrooms                 9000 non-null   float64\n",
            " 1   bathrooms                9000 non-null   float64\n",
            " 2   parking                  9000 non-null   float64\n",
            " 3   size_sqm                 9000 non-null   float64\n",
            " 4   age                      9000 non-null   float64\n",
            " 5   condition                9000 non-null   object \n",
            " 6   year_built               9000 non-null   float64\n",
            " 7   renovation_year          2068 non-null   float64\n",
            " 8   location                 9000 non-null   object \n",
            " 9   floor_number             9000 non-null   float64\n",
            " 10  distance_to_city_center  9000 non-null   float64\n",
            " 11  proximity_to_schools     9000 non-null   float64\n",
            " 12  price                    10000 non-null  float64\n",
            "dtypes: float64(11), object(2)\n",
            "memory usage: 1015.8+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "cd2dfd77-9258-4b5b-bc0e-259ff2108c45",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cd2dfd77-9258-4b5b-bc0e-259ff2108c45",
        "outputId": "4865bc07-c1d8-4918-d16f-ad4318cf1d79"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# check for duplicates\n",
        "df.duplicated().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "a0ec5f88-0101-4ec0-b5c0-9de22dfdf836",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        },
        "id": "a0ec5f88-0101-4ec0-b5c0-9de22dfdf836",
        "outputId": "eea43973-1709-40c5-a515-4a553546a8af"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Quantifying missing data')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# visualize missing values per feature\n",
        "df.isnull().mean().plot.bar(figsize=(12,6))\n",
        "plt.ylabel('Percentage of missing values')\n",
        "plt.xlabel('Variables')\n",
        "plt.title('Quantifying missing data')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "c398888a-5ee3-4a90-8894-8b5178ebf914",
      "metadata": {
        "id": "c398888a-5ee3-4a90-8894-8b5178ebf914"
      },
      "outputs": [],
      "source": [
        "# dropping feature with high null values\n",
        "df.drop(\"renovation_year\", inplace=True, axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "c68ce1b6-d17b-4628-95a0-048a7b4b4451",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "c68ce1b6-d17b-4628-95a0-048a7b4b4451",
        "outputId": "2a525c1e-abb7-4ff1-e40e-607c3866364f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='price', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2200x1600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# distribution checking\n",
        "plt.figure(figsize=(22, 16))\n",
        "sns.histplot(df.price)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "6cdb214f-b542-4ce6-b212-39c5056cf08c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6cdb214f-b542-4ce6-b212-39c5056cf08c",
        "outputId": "11c2580a-8bb9-4cdf-b6de-a9326deac641"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='price', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2200x1600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# dealing with negative price values\n",
        "df['price'] = df['price'].abs()\n",
        "\n",
        "# distribution checking\n",
        "plt.figure(figsize=(22, 16))\n",
        "sns.histplot(df.price)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "375781a0-28f1-4b34-86ea-52682a483863",
      "metadata": {
        "id": "375781a0-28f1-4b34-86ea-52682a483863"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "b69fde42-b659-47c0-a000-20e456bf4fe9",
      "metadata": {
        "id": "b69fde42-b659-47c0-a000-20e456bf4fe9"
      },
      "outputs": [],
      "source": [
        "X = df.drop(\"price\", axis=1)\n",
        "y = df['price']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "a3aaab2a-f015-415a-9272-82109a066c4c",
      "metadata": {
        "id": "a3aaab2a-f015-415a-9272-82109a066c4c"
      },
      "outputs": [],
      "source": [
        "cat_features = [col for col in X.columns if X[col].dtypes == \"object\" and X[col].nunique() < 10]\n",
        "numeric_features = [col for col in X.columns if X[col].dtypes in [\"int64\", \"float64\"]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "ee2ca02c-f403-4f19-9b8c-d0cb9dbe308b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ee2ca02c-f403-4f19-9b8c-d0cb9dbe308b",
        "outputId": "f05e29f1-4cbe-4446-d7ca-87bfd3e3f9b0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/impute/_iterative.py:800: ConvergenceWarning: [IterativeImputer] Early stopping criterion not reached.\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "# dealing with missing values\n",
        "X[numeric_features] = IterativeImputer(estimator=DecisionTreeRegressor(random_state=1), max_iter=5).fit_transform(X[numeric_features])\n",
        "X[cat_features] = SimpleImputer(strategy=\"most_frequent\").fit_transform(X[cat_features])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "16f8d705-bcb6-4656-89da-6c793bf9c846",
      "metadata": {
        "id": "16f8d705-bcb6-4656-89da-6c793bf9c846"
      },
      "outputs": [],
      "source": [
        "from IPython.display import display\n",
        "\n",
        "def rates(X, y, columns):\n",
        "    data = pd.concat([X, y], axis=1)\n",
        "    global_mean = round(data[\"price\"].mean(), 3)\n",
        "    for col in columns:\n",
        "        df_group = data.groupby(by=col).price.agg([\"mean\"])\n",
        "        df_group[\"diff\"] = df_group[\"mean\"] - global_mean\n",
        "        df_group[\"rate\"] = df_group[\"mean\"] / global_mean\n",
        "        display(df_group)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "202c5366-96b1-4755-a176-44f99e41344e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 552
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        "outputId": "da01e3c6-40da-4ab6-93dd-f1ef4a85e95b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                   mean        diff      rate\n",
              "condition                                    \n",
              "excellent  44534.847418 -124.046582  0.997222\n",
              "fair       44767.348584  108.454584  1.002429\n",
              "good       44631.352110  -27.541890  0.999383\n",
              "poor       44687.703935   28.809935  1.000645"
            ],
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            "text/plain": [
              "                    mean         diff      rate\n",
              "location                                       \n",
              "Bastos      43771.541223  -887.352777  0.980130\n",
              "Biyem-Assi  44793.131576   134.237576  1.003006\n",
              "Emana       44500.869306  -158.024694  0.996462\n",
              "Jovance     44925.860850   266.966850  1.005978\n",
              "Mvan        45838.089436  1179.195436  1.026404\n",
              "Nlongkak    44157.453099  -501.440901  0.988772\n",
              "Obili       44533.899418  -124.994582  0.997201\n",
              "Odza        44196.669596  -462.224404  0.989650\n",
              "Oyomabang   45316.254069   657.360069  1.014720"
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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",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"rates(X, y, cat_features)\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Odza\",\n          \"Biyem-Assi\",\n          \"Nlongkak\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 632.8313919418241,\n        \"min\": 43771.54122283427,\n        \"max\": 45838.08943552936,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          44196.669595521766,\n          44793.13157645753,\n          44157.453098602324\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"diff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 632.8313919418241,\n        \"min\": -887.3527771657318,\n        \"max\": 1179.1954355293565,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          -462.22440447823465,\n          134.2375764575263,\n          -501.4409013976765\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.01417033283318269,\n        \"min\": 0.9801304354477356,\n        \"max\": 1.0264044925861657,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.9896498913636725,\n          1.0030058419372752,\n          0.9887717572809197\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "rates(X, y, cat_features)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "6482c6e1-016f-4a63-b8b0-4f55d0e9e1f1",
      "metadata": {
        "id": "6482c6e1-016f-4a63-b8b0-4f55d0e9e1f1"
      },
      "outputs": [],
      "source": [
        "def calculate_mi_score(X):\n",
        "    return mutual_info_score(X, y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "9c68c408-f26c-422d-a498-b0c85471dcce",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 796
        },
        "id": "9c68c408-f26c-422d-a498-b0c85471dcce",
        "outputId": "691ccf5f-5777-401c-b06a-0de7e80227e3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received multiclass values for label, and continuous values for target\n",
            "  warnings.warn(msg, UserWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received multiclass values for label, and continuous values for target\n",
            "  warnings.warn(msg, UserWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received multiclass values for label, and continuous values for target\n",
            "  warnings.warn(msg, UserWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received multiclass values for label, and continuous values for target\n",
            "  warnings.warn(msg, UserWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received multiclass values for label, and continuous values for target\n",
            "  warnings.warn(msg, UserWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received multiclass values for label, and continuous values for target\n",
            "  warnings.warn(msg, UserWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received multiclass values for label, and continuous values for target\n",
            "  warnings.warn(msg, UserWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received multiclass values for label, and continuous values for target\n",
            "  warnings.warn(msg, UserWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received multiclass values for label, and continuous values for target\n",
            "  warnings.warn(msg, UserWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target\n",
            "  warnings.warn(msg, UserWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:66: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and continuous values for target\n",
            "  warnings.warn(msg, UserWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         Mutual Information\n",
              "proximity_to_schools               9.067269\n",
              "distance_to_city_center            9.067096\n",
              "size_sqm                           5.581864\n",
              "year_built                         4.300507\n",
              "age                                3.888755\n",
              "location                           2.161077\n",
              "bedrooms                           1.945588\n",
              "floor_number                       1.386054\n",
              "condition                          1.371691\n",
              "parking                            1.098605\n",
              "bathrooms                          1.098557"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Mutual Information</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>proximity_to_schools</th>\n",
              "      <td>9.067269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>distance_to_city_center</th>\n",
              "      <td>9.067096</td>\n",
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              "      <th>size_sqm</th>\n",
              "      <td>5.581864</td>\n",
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              "      <th>year_built</th>\n",
              "      <td>4.300507</td>\n",
              "    </tr>\n",
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              "      <th>age</th>\n",
              "      <td>3.888755</td>\n",
              "    </tr>\n",
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              "      <th>location</th>\n",
              "      <td>2.161077</td>\n",
              "    </tr>\n",
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              "      <th>bedrooms</th>\n",
              "      <td>1.945588</td>\n",
              "    </tr>\n",
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              "      <th>floor_number</th>\n",
              "      <td>1.386054</td>\n",
              "    </tr>\n",
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              "      <th>condition</th>\n",
              "      <td>1.371691</td>\n",
              "    </tr>\n",
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              "      <td>1.098605</td>\n",
              "    </tr>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      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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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      buttonEl.style.display =\n",
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              "        const element = document.querySelector('#df-a6d80453-0eda-4ec1-bccd-a0ba1cb1bd82');\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",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-916fc0f3-db47-4d78-9390-1663d9848494 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_eac9afde-8bdc-47bd-9156-f87812a67079\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_mi')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_eac9afde-8bdc-47bd-9156-f87812a67079 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_mi');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_mi",
              "summary": "{\n  \"name\": \"df_mi\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"Mutual Information\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.0216027887511383,\n        \"min\": 1.0985566790583228,\n        \"max\": 9.067268747055545,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          2.161076896496476,\n          9.067268747055545,\n          1.0986054999924058\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "# calculating mutual information which helps measures the dependency between a feature and the target variable. In this case the categorical features.\n",
        "df_mi = pd.concat([X[cat_features],X[numeric_features]], axis=1).apply(calculate_mi_score)\n",
        "df_mi = df_mi.sort_values(ascending=False).to_frame(name=\"Mutual Information\")\n",
        "df_mi"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "edbf6839-1e0f-4bd8-a1cc-18d85ae07ebe",
      "metadata": {
        "id": "edbf6839-1e0f-4bd8-a1cc-18d85ae07ebe"
      },
      "source": [
        "# Results:\n",
        "high values of mutual information implies location and condition have a strong relationship with the price"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "5dbf3adf-4b7f-4109-86b5-10d818b286ae",
      "metadata": {
        "id": "5dbf3adf-4b7f-4109-86b5-10d818b286ae"
      },
      "outputs": [],
      "source": [
        "# checking and replacing the\n",
        "df['condition'].unique()\n",
        "mapping = {'excellent': 4, 'good': 3, 'poor': 2, 'fair': 1}\n",
        "df['condition'] = df['condition'].map(mapping)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "d6ff6ca6-52a5-4d63-bb10-5ff5f3f0502d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "d6ff6ca6-52a5-4d63-bb10-5ff5f3f0502d",
        "outputId": "538fb541-5616-4bc6-fce3-806a6e77b757"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2400x1600 with 9 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# identifying outliers\n",
        "ind = 0\n",
        "plt.figure(figsize=(24,16))\n",
        "for i in X[numeric_features]:\n",
        "    ind += 1\n",
        "    plt.subplot(6,6,ind)\n",
        "    plt.title(i)\n",
        "    sns.boxplot(X[i])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "29d9122d-859c-497d-b037-f84001af6942",
      "metadata": {
        "id": "29d9122d-859c-497d-b037-f84001af6942"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler =  StandardScaler()\n",
        "X = pd.DataFrame(scaler.fit_transform(X[numeric_features]), columns=numeric_features)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "a203a24d-ce7b-4352-ac0b-b506a59b2f24",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "a203a24d-ce7b-4352-ac0b-b506a59b2f24",
        "outputId": "af618c88-e3a6-4322-dd9d-c7c97098f03d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# output the correlation of the numerical features\n",
        "def correlation(X, y):\n",
        "    corr = X[numeric_features].corrwith(y).sort_values(ascending=False).to_frame(\"Correlation\")\n",
        "    return corr\n",
        "\n",
        "sns.heatmap(correlation(X, y), cmap=\"coolwarm\", annot=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "85f37318-3ede-47d9-8d9c-1d7719dd22b8",
      "metadata": {
        "id": "85f37318-3ede-47d9-8d9c-1d7719dd22b8"
      },
      "outputs": [],
      "source": [
        "# splitting the dataset\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b50b4a99-6868-4bfb-9df1-ae37afe2aeac",
      "metadata": {
        "id": "b50b4a99-6868-4bfb-9df1-ae37afe2aeac",
        "outputId": "cb00f57b-463e-43d4-bf0a-fb7bd84cc94f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[4.01793397e-04, 0.00000000e+00, 8.03586795e-04, ...,\n",
              "        0.00000000e+00, 4.33535076e-01, 9.00017210e-01],\n",
              "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
              "        0.00000000e+00, 8.43019721e-01, 5.37750726e-01],\n",
              "       [8.29343472e-04, 2.07335868e-04, 2.07335868e-04, ...,\n",
              "        4.14671736e-04, 2.37814241e-01, 9.70539198e-01],\n",
              "       ...,\n",
              "       [5.51217209e-04, 0.00000000e+00, 0.00000000e+00, ...,\n",
              "        0.00000000e+00, 6.85714208e-01, 7.27331108e-01],\n",
              "       [7.85618131e-03, 1.96404533e-03, 1.96404533e-03, ...,\n",
              "        3.92809066e-03, 4.43874244e-01, 7.62049587e-01],\n",
              "       [8.47274598e-04, 0.00000000e+00, 0.00000000e+00, ...,\n",
              "        1.41212433e-04, 4.67413153e-01, 8.83848619e-01]])"
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "numeric_pipeline = Pipeline(steps=[('impute',SimpleImputer(strategy='mean')),('normalize', Normalizer())])\n",
        "print()\n",
        "categorical_pipeline = Pipeline(steps=[('impute', SimpleImputer(strategy='most_frequent')), ('ordinalEncoder', OrdinalEncoder()),('normalize', Normalizer())])\n",
        "categorical_pipeline.fit_transform(X_train, y_train)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f7c15ac8-73a6-42f0-9dc4-14252e4acfc8",
      "metadata": {
        "id": "f7c15ac8-73a6-42f0-9dc4-14252e4acfc8",
        "outputId": "dfc6914a-13ee-48af-84e5-cd57176b14b4"
      },
      "outputs": [
        {
          "ename": "ValueError",
          "evalue": "not enough values to unpack (expected 3, got 2)",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[59], line 5\u001b[0m\n\u001b[1;32m      2\u001b[0m column_transformer \u001b[38;5;241m=\u001b[39m ColumnTransformer(transformers\u001b[38;5;241m=\u001b[39m[(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnumeric\u001b[39m\u001b[38;5;124m'\u001b[39m, numeric_pipeline), (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcategorical\u001b[39m\u001b[38;5;124m'\u001b[39m, categorical_pipeline)], remainder\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdrop\u001b[39m\u001b[38;5;124m'\u001b[39m, n_jobs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# Transforming the data\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m column_transformer\u001b[38;5;241m.\u001b[39mfit_transform(X_train, y_train)\n",
            "File \u001b[0;32m~/anaconda3/lib/python3.12/site-packages/sklearn/utils/_set_output.py:295\u001b[0m, in \u001b[0;36m_wrap_method_output.<locals>.wrapped\u001b[0;34m(self, X, *args, **kwargs)\u001b[0m\n\u001b[1;32m    293\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(f)\n\u001b[1;32m    294\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 295\u001b[0m     data_to_wrap \u001b[38;5;241m=\u001b[39m f(\u001b[38;5;28mself\u001b[39m, X, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    296\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_to_wrap, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m    297\u001b[0m         \u001b[38;5;66;03m# only wrap the first output for cross decomposition\u001b[39;00m\n\u001b[1;32m    298\u001b[0m         return_tuple \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    299\u001b[0m             _wrap_data_with_container(method, data_to_wrap[\u001b[38;5;241m0\u001b[39m], X, \u001b[38;5;28mself\u001b[39m),\n\u001b[1;32m    300\u001b[0m             \u001b[38;5;241m*\u001b[39mdata_to_wrap[\u001b[38;5;241m1\u001b[39m:],\n\u001b[1;32m    301\u001b[0m         )\n",
            "File \u001b[0;32m~/anaconda3/lib/python3.12/site-packages/sklearn/base.py:1474\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1467\u001b[0m     estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[1;32m   1469\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m   1470\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m   1471\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m   1472\u001b[0m     )\n\u001b[1;32m   1473\u001b[0m ):\n\u001b[0;32m-> 1474\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fit_method(estimator, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
            "File \u001b[0;32m~/anaconda3/lib/python3.12/site-packages/sklearn/compose/_column_transformer.py:903\u001b[0m, in \u001b[0;36mColumnTransformer.fit_transform\u001b[0;34m(self, X, y, **params)\u001b[0m\n\u001b[1;32m    901\u001b[0m \u001b[38;5;66;03m# set n_features_in_ attribute\u001b[39;00m\n\u001b[1;32m    902\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_n_features(X, reset\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m--> 903\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_transformers()\n\u001b[1;32m    904\u001b[0m n_samples \u001b[38;5;241m=\u001b[39m _num_samples(X)\n\u001b[1;32m    906\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_column_callables(X)\n",
            "File \u001b[0;32m~/anaconda3/lib/python3.12/site-packages/sklearn/compose/_column_transformer.py:461\u001b[0m, in \u001b[0;36mColumnTransformer._validate_transformers\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    458\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransformers:\n\u001b[1;32m    459\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m--> 461\u001b[0m names, transformers, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mzip\u001b[39m(\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransformers)\n\u001b[1;32m    463\u001b[0m \u001b[38;5;66;03m# validate names\u001b[39;00m\n\u001b[1;32m    464\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_names(names)\n",
            "\u001b[0;31mValueError\u001b[0m: not enough values to unpack (expected 3, got 2)"
          ]
        }
      ],
      "source": [
        "# define a column transformer\n",
        "column_transformer = ColumnTransformer(transformers=[('numeric', numeric_pipeline, []), ('categorical', categorical_pipeline)], remainder='drop', n_jobs=1)\n",
        "\n",
        "# Transforming the data\n",
        "column_transformer.fit_transform(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "84a46efc-a71c-434e-9c1f-f95472fdaf5c",
      "metadata": {
        "id": "84a46efc-a71c-434e-9c1f-f95472fdaf5c"
      },
      "outputs": [],
      "source": [
        "# define the model\n",
        "model = LinearRegression()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "67e3a3f9-b7b3-4fb4-bd7c-def7ff1cf5e8",
      "metadata": {
        "id": "67e3a3f9-b7b3-4fb4-bd7c-def7ff1cf5e8",
        "outputId": "b752822a-f6ca-4b31-e29d-cd0951a7a673"
      },
      "outputs": [
        {
          "ename": "ValueError",
          "evalue": "A given column is not a column of the dataframe",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "File \u001b[0;32m~/anaconda3/lib/python3.12/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3805\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
            "File \u001b[0;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "File \u001b[0;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'condition'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "File \u001b[0;32m~/anaconda3/lib/python3.12/site-packages/sklearn/utils/__init__.py:505\u001b[0m, in \u001b[0;36m_get_column_indices\u001b[0;34m(X, key)\u001b[0m\n\u001b[1;32m    504\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m col \u001b[38;5;129;01min\u001b[39;00m columns:\n\u001b[0;32m--> 505\u001b[0m     col_idx \u001b[38;5;241m=\u001b[39m all_columns\u001b[38;5;241m.\u001b[39mget_loc(col)\n\u001b[1;32m    506\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(col_idx, numbers\u001b[38;5;241m.\u001b[39mIntegral):\n",
            "File \u001b[0;32m~/anaconda3/lib/python3.12/site-packages/pandas/core/indexes/base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3811\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3812\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3814\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3815\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3816\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'condition'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[27], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m model_pipeline \u001b[38;5;241m=\u001b[39m Pipeline(steps\u001b[38;5;241m=\u001b[39m[(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpreprocessor\u001b[39m\u001b[38;5;124m'\u001b[39m, column_transformer), (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlinearRegression\u001b[39m\u001b[38;5;124m'\u001b[39m, model)])\n\u001b[0;32m----> 2\u001b[0m model_pipeline\u001b[38;5;241m.\u001b[39mfit(x_train, y_train)\n",
            "File \u001b[0;32m~/anaconda3/lib/python3.12/site-packages/sklearn/base.py:1474\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1467\u001b[0m     estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[1;32m   1469\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m   1470\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m   1471\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m   1472\u001b[0m     )\n\u001b[1;32m   1473\u001b[0m ):\n\u001b[0;32m-> 1474\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fit_method(estimator, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
            "File \u001b[0;32m~/anaconda3/lib/python3.12/site-packages/sklearn/pipeline.py:471\u001b[0m, in \u001b[0;36mPipeline.fit\u001b[0;34m(self, X, y, **params)\u001b[0m\n\u001b[1;32m    428\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Fit the model.\u001b[39;00m\n\u001b[1;32m    429\u001b[0m \n\u001b[1;32m    430\u001b[0m \u001b[38;5;124;03mFit all the transformers one after the other and sequentially transform the\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    468\u001b[0m \u001b[38;5;124;03m    Pipeline with fitted steps.\u001b[39;00m\n\u001b[1;32m    469\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    470\u001b[0m routed_params \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_method_params(method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfit\u001b[39m\u001b[38;5;124m\"\u001b[39m, props\u001b[38;5;241m=\u001b[39mparams)\n\u001b[0;32m--> 471\u001b[0m Xt \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit(X, y, routed_params)\n\u001b[1;32m    472\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _print_elapsed_time(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPipeline\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_log_message(\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m)):\n\u001b[1;32m    473\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_final_estimator \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpassthrough\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
            "File \u001b[0;32m~/anaconda3/lib/python3.12/site-packages/sklearn/pipeline.py:408\u001b[0m, in \u001b[0;36mPipeline._fit\u001b[0;34m(self, X, y, routed_params)\u001b[0m\n\u001b[1;32m    406\u001b[0m     cloned_transformer \u001b[38;5;241m=\u001b[39m clone(transformer)\n\u001b[1;32m    407\u001b[0m \u001b[38;5;66;03m# Fit or load from cache the current transformer\u001b[39;00m\n\u001b[0;32m--> 408\u001b[0m X, fitted_transformer \u001b[38;5;241m=\u001b[39m fit_transform_one_cached(\n\u001b[1;32m    409\u001b[0m     cloned_transformer,\n\u001b[1;32m    410\u001b[0m     X,\n\u001b[1;32m    411\u001b[0m     y,\n\u001b[1;32m    412\u001b[0m     \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    413\u001b[0m     message_clsname\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPipeline\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    414\u001b[0m     message\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_log_message(step_idx),\n\u001b[1;32m    415\u001b[0m     params\u001b[38;5;241m=\u001b[39mrouted_params[name],\n\u001b[1;32m    416\u001b[0m )\n\u001b[1;32m    417\u001b[0m \u001b[38;5;66;03m# Replace the transformer of the step with the fitted\u001b[39;00m\n\u001b[1;32m    418\u001b[0m \u001b[38;5;66;03m# transformer. This is necessary when loading the transformer\u001b[39;00m\n\u001b[1;32m    419\u001b[0m \u001b[38;5;66;03m# from the cache.\u001b[39;00m\n\u001b[1;32m    420\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps[step_idx] \u001b[38;5;241m=\u001b[39m (name, fitted_transformer)\n",
            "File \u001b[0;32m~/anaconda3/lib/python3.12/site-packages/joblib/memory.py:312\u001b[0m, in \u001b[0;36mNotMemorizedFunc.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    311\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 312\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
            "File \u001b[0;32m~/anaconda3/lib/python3.12/site-packages/sklearn/pipeline.py:1303\u001b[0m, in \u001b[0;36m_fit_transform_one\u001b[0;34m(transformer, X, y, weight, message_clsname, message, params)\u001b[0m\n\u001b[1;32m   1301\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _print_elapsed_time(message_clsname, message):\n\u001b[1;32m   1302\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(transformer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfit_transform\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m-> 1303\u001b[0m         res \u001b[38;5;241m=\u001b[39m transformer\u001b[38;5;241m.\u001b[39mfit_transform(X, y, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mparams\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfit_transform\u001b[39m\u001b[38;5;124m\"\u001b[39m, {}))\n\u001b[1;32m   1304\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1305\u001b[0m         res \u001b[38;5;241m=\u001b[39m transformer\u001b[38;5;241m.\u001b[39mfit(X, y, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mparams\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfit\u001b[39m\u001b[38;5;124m\"\u001b[39m, {}))\u001b[38;5;241m.\u001b[39mtransform(\n\u001b[1;32m   1306\u001b[0m             X, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mparams\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtransform\u001b[39m\u001b[38;5;124m\"\u001b[39m, {})\n\u001b[1;32m   1307\u001b[0m         )\n",
            "File \u001b[0;32m~/anaconda3/lib/python3.12/site-packages/sklearn/utils/_set_output.py:295\u001b[0m, in \u001b[0;36m_wrap_method_output.<locals>.wrapped\u001b[0;34m(self, X, *args, **kwargs)\u001b[0m\n\u001b[1;32m    293\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(f)\n\u001b[1;32m    294\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 295\u001b[0m     data_to_wrap \u001b[38;5;241m=\u001b[39m f(\u001b[38;5;28mself\u001b[39m, X, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    296\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_to_wrap, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m    297\u001b[0m         \u001b[38;5;66;03m# only wrap the first output for cross decomposition\u001b[39;00m\n\u001b[1;32m    298\u001b[0m         return_tuple \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    299\u001b[0m             _wrap_data_with_container(method, data_to_wrap[\u001b[38;5;241m0\u001b[39m], X, \u001b[38;5;28mself\u001b[39m),\n\u001b[1;32m    300\u001b[0m             \u001b[38;5;241m*\u001b[39mdata_to_wrap[\u001b[38;5;241m1\u001b[39m:],\n\u001b[1;32m    301\u001b[0m         )\n",
            "File \u001b[0;32m~/anaconda3/lib/python3.12/site-packages/sklearn/base.py:1474\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1467\u001b[0m     estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[1;32m   1469\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m   1470\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m   1471\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m   1472\u001b[0m     )\n\u001b[1;32m   1473\u001b[0m ):\n\u001b[0;32m-> 1474\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fit_method(estimator, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
            "File \u001b[0;32m~/anaconda3/lib/python3.12/site-packages/sklearn/compose/_column_transformer.py:906\u001b[0m, in \u001b[0;36mColumnTransformer.fit_transform\u001b[0;34m(self, X, y, **params)\u001b[0m\n\u001b[1;32m    903\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_transformers()\n\u001b[1;32m    904\u001b[0m n_samples \u001b[38;5;241m=\u001b[39m _num_samples(X)\n\u001b[0;32m--> 906\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_column_callables(X)\n\u001b[1;32m    907\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_remainder(X)\n\u001b[1;32m    909\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _routing_enabled():\n",
            "File \u001b[0;32m~/anaconda3/lib/python3.12/site-packages/sklearn/compose/_column_transformer.py:496\u001b[0m, in \u001b[0;36mColumnTransformer._validate_column_callables\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    494\u001b[0m         columns \u001b[38;5;241m=\u001b[39m columns(X)\n\u001b[1;32m    495\u001b[0m     all_columns\u001b[38;5;241m.\u001b[39mappend(columns)\n\u001b[0;32m--> 496\u001b[0m     transformer_to_input_indices[name] \u001b[38;5;241m=\u001b[39m _get_column_indices(X, columns)\n\u001b[1;32m    498\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_columns \u001b[38;5;241m=\u001b[39m all_columns\n\u001b[1;32m    499\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_transformer_to_input_indices \u001b[38;5;241m=\u001b[39m transformer_to_input_indices\n",
            "File \u001b[0;32m~/anaconda3/lib/python3.12/site-packages/sklearn/utils/__init__.py:513\u001b[0m, in \u001b[0;36m_get_column_indices\u001b[0;34m(X, key)\u001b[0m\n\u001b[1;32m    510\u001b[0m         column_indices\u001b[38;5;241m.\u001b[39mappend(col_idx)\n\u001b[1;32m    512\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m--> 513\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mA given column is not a column of the dataframe\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m    515\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m column_indices\n",
            "\u001b[0;31mValueError\u001b[0m: A given column is not a column of the dataframe"
          ]
        }
      ],
      "source": [
        "model_pipeline = Pipeline(steps=[('preprocessor', column_transformer), ('linearRegression', model)])\n",
        "model.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2ffa9ab1-0ccf-465e-bc23-db895ae91739",
      "metadata": {
        "id": "2ffa9ab1-0ccf-465e-bc23-db895ae91739",
        "outputId": "3dcd7e91-9460-497f-8f2f-cba63966767f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MAE: 16672.14190333877\n",
            "MSE: 419764085.67270535\n",
            "R-squared: 0.23624234448124704\n"
          ]
        }
      ],
      "source": [
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Predict and evaluate\n",
        "y_pred = model.predict(X_test)\n",
        "print('MAE:', mean_absolute_error(y_test, y_pred))\n",
        "print('MSE:', mean_squared_error(y_test, y_pred))\n",
        "print('R-squared:', r2_score(y_test, y_pred))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bc978314-f991-4995-9aac-35c73366c2b3",
      "metadata": {
        "id": "bc978314-f991-4995-9aac-35c73366c2b3"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.12.4"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}