8)House_Price_Prediction_with_Pycaret.ipynb

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\n"
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      "metadata": {
        "id": "CgZQ3H07r4J_"
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      "source": [
        "# Install required modules\n",
        "!pip install pycaret"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Import necessary modules\n",
        "import pandas as pd\n",
        "from pycaret.regression import *"
      ],
      "metadata": {
        "id": "ekZz1_SLthep"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Load data\n",
        "Caf_house_df = pd.read_csv('california_housing_train.csv')\n",
        "Caf_house_df.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
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        "id": "S_RiN4y5tzH5",
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      "execution_count": 2,
      "outputs": [
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              "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "0    -114.31     34.19                15.0       5612.0          1283.0   \n",
              "1    -114.47     34.40                19.0       7650.0          1901.0   \n",
              "2    -114.56     33.69                17.0        720.0           174.0   \n",
              "3    -114.57     33.64                14.0       1501.0           337.0   \n",
              "4    -114.57     33.57                20.0       1454.0           326.0   \n",
              "5    -114.58     33.63                29.0       1387.0           236.0   \n",
              "6    -114.58     33.61                25.0       2907.0           680.0   \n",
              "7    -114.59     34.83                41.0        812.0           168.0   \n",
              "8    -114.59     33.61                34.0       4789.0          1175.0   \n",
              "9    -114.60     34.83                46.0       1497.0           309.0   \n",
              "\n",
              "   population  households  median_income  median_house_value  \n",
              "0      1015.0       472.0         1.4936             66900.0  \n",
              "1      1129.0       463.0         1.8200             80100.0  \n",
              "2       333.0       117.0         1.6509             85700.0  \n",
              "3       515.0       226.0         3.1917             73400.0  \n",
              "4       624.0       262.0         1.9250             65500.0  \n",
              "5       671.0       239.0         3.3438             74000.0  \n",
              "6      1841.0       633.0         2.6768             82400.0  \n",
              "7       375.0       158.0         1.7083             48500.0  \n",
              "8      3134.0      1056.0         2.1782             58400.0  \n",
              "9       787.0       271.0         2.1908             48100.0  "
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>-114.31</td>\n",
              "      <td>34.19</td>\n",
              "      <td>15.0</td>\n",
              "      <td>5612.0</td>\n",
              "      <td>1283.0</td>\n",
              "      <td>1015.0</td>\n",
              "      <td>472.0</td>\n",
              "      <td>1.4936</td>\n",
              "      <td>66900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-114.47</td>\n",
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              "      <td>19.0</td>\n",
              "      <td>7650.0</td>\n",
              "      <td>1901.0</td>\n",
              "      <td>1129.0</td>\n",
              "      <td>463.0</td>\n",
              "      <td>1.8200</td>\n",
              "      <td>80100.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>14.0</td>\n",
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              "      <td>515.0</td>\n",
              "      <td>226.0</td>\n",
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              "      <td>73400.0</td>\n",
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              "      <td>1.9250</td>\n",
              "      <td>65500.0</td>\n",
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              "      <td>236.0</td>\n",
              "      <td>671.0</td>\n",
              "      <td>239.0</td>\n",
              "      <td>3.3438</td>\n",
              "      <td>74000.0</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>-114.58</td>\n",
              "      <td>33.61</td>\n",
              "      <td>25.0</td>\n",
              "      <td>2907.0</td>\n",
              "      <td>680.0</td>\n",
              "      <td>1841.0</td>\n",
              "      <td>633.0</td>\n",
              "      <td>2.6768</td>\n",
              "      <td>82400.0</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>-114.59</td>\n",
              "      <td>34.83</td>\n",
              "      <td>41.0</td>\n",
              "      <td>812.0</td>\n",
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              "      <td>1.7083</td>\n",
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              "      <td>58400.0</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>-114.60</td>\n",
              "      <td>34.83</td>\n",
              "      <td>46.0</td>\n",
              "      <td>1497.0</td>\n",
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              "      <td>271.0</td>\n",
              "      <td>2.1908</td>\n",
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          "metadata": {},
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    {
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              "                               Description               Value\n",
              "0                               session_id                8140\n",
              "1                                   Target  median_house_value\n",
              "2                            Original Data          (17000, 9)\n",
              "3                           Missing Values               False\n",
              "4                         Numeric Features                   8\n",
              "5                     Categorical Features                   0\n",
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              "7                High Cardinality Features               False\n",
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              "9                    Transformed Train Set          (11899, 8)\n",
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              "12                     Stratify Train-Test               False\n",
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              "15                                CPU Jobs                  -1\n",
              "16                                 Use GPU               False\n",
              "17                          Log Experiment               False\n",
              "18                         Experiment Name    reg-default-name\n",
              "19                                     USI                fcb3\n",
              "20                         Imputation Type              simple\n",
              "21          Iterative Imputation Iteration                None\n",
              "22                         Numeric Imputer                mean\n",
              "23      Iterative Imputation Numeric Model                None\n",
              "24                     Categorical Imputer            constant\n",
              "25  Iterative Imputation Categorical Model                None\n",
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              "27                               Normalize               False\n",
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              "33                          PCA Components                None\n",
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              "41             Multicollinearity Threshold                None\n",
              "42             Remove Perfect Collinearity                True\n",
              "43                              Clustering               False\n",
              "44                    Clustering Iteration                None\n",
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              "46                       Polynomial Degree                None\n",
              "47                    Trignometry Features               False\n",
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              "51                Feature Selection Method             classic\n",
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              "53                     Feature Interaction               False\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>USI</td>\n",
              "      <td>fcb3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Imputation Type</td>\n",
              "      <td>simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Iterative Imputation Iteration</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Numeric Imputer</td>\n",
              "      <td>mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Iterative Imputation Numeric Model</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Categorical Imputer</td>\n",
              "      <td>constant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Iterative Imputation Categorical Model</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Unknown Categoricals Handling</td>\n",
              "      <td>least_frequent</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Normalize</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Normalize Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Transformation</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Transformation Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>PCA</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>PCA Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>PCA Components</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Ignore Low Variance</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Combine Rare Levels</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Rare Level Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Numeric Binning</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Remove Outliers</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Outliers Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Remove Multicollinearity</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Multicollinearity Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Remove Perfect Collinearity</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>Clustering</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Clustering Iteration</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>Polynomial Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Polynomial Degree</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Trignometry Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Polynomial Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>Group Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>Feature Selection</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>Feature Selection Method</td>\n",
              "      <td>classic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>Features Selection Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>Feature Interaction</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Feature Ratio</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>Interaction Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>Transform Target</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>Transform Target Method</td>\n",
              "      <td>box-cox</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            "INFO:logs:create_model_container: 0\n",
            "INFO:logs:master_model_container: 0\n",
            "INFO:logs:display_container: 1\n",
            "INFO:logs:Pipeline(memory=None,\n",
            "         steps=[('dtypes',\n",
            "                 DataTypes_Auto_infer(categorical_features=[],\n",
            "                                      display_types=True, features_todrop=[],\n",
            "                                      id_columns=[], ml_usecase='regression',\n",
            "                                      numerical_features=[],\n",
            "                                      target='median_house_value',\n",
            "                                      time_features=[])),\n",
            "                ('imputer',\n",
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            "                                fill_value_categorical=None,\n",
            "                                fill_value_numerical=None,\n",
            "                                numer...\n",
            "                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),\n",
            "                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),\n",
            "                ('cluster_all', 'passthrough'),\n",
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            "                ('fix_perfect', Remove_100(target='median_house_value')),\n",
            "                ('clean_names', Clean_Colum_Names()),\n",
            "                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),\n",
            "                ('dfs', 'passthrough'), ('pca', 'passthrough')],\n",
            "         verbose=False)\n",
            "INFO:logs:setup() succesfully completed......................................\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best = compare_models()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 787,
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        "id": "qwUdjjRzuieX",
        "outputId": "e89c7dff-495d-457e-f0b5-08d5553b9326"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                    Model          MAE           MSE  \\\n",
              "lightgbm  Light Gradient Boosting Machine   32394.1972  2.349034e+09   \n",
              "rf                Random Forest Regressor   32907.6818  2.511345e+09   \n",
              "et                  Extra Trees Regressor   35920.9350  2.859327e+09   \n",
              "gbr           Gradient Boosting Regressor   38175.6441  3.039162e+09   \n",
              "lr                      Linear Regression   51148.3016  4.950486e+09   \n",
              "lasso                    Lasso Regression   51148.2266  4.950502e+09   \n",
              "ridge                    Ridge Regression   51148.0633  4.950500e+09   \n",
              "llar         Lasso Least Angle Regression   51167.6747  4.950387e+09   \n",
              "br                         Bayesian Ridge   51146.5855  4.950498e+09   \n",
              "dt                Decision Tree Regressor   45598.1058  5.142616e+09   \n",
              "en                            Elastic Net   54330.6438  5.427388e+09   \n",
              "huber                     Huber Regressor   55641.8266  6.018488e+09   \n",
              "omp           Orthogonal Matching Pursuit   62302.3394  7.013196e+09   \n",
              "ada                    AdaBoost Regressor   78877.5364  8.303166e+09   \n",
              "knn                 K Neighbors Regressor   77950.0633  1.025934e+10   \n",
              "dummy                     Dummy Regressor   91717.4633  1.353694e+10   \n",
              "lar                Least Angle Regression   90158.3099  2.923356e+10   \n",
              "par          Passive Aggressive Regressor  191457.5591  9.557136e+10   \n",
              "\n",
              "                 RMSE      R2   RMSLE    MAPE  TT (Sec)  \n",
              "lightgbm   48415.3656  0.8263  0.2385  0.1816     0.351  \n",
              "rf         50054.3880  0.8143  0.2385  0.1821     6.288  \n",
              "et         53402.6410  0.7886  0.2529  0.1996     2.373  \n",
              "gbr        55068.8260  0.7753  0.2722  0.2171     2.353  \n",
              "lr         70300.6656  0.6338  0.4228  0.3041     0.294  \n",
              "lasso      70300.7738  0.6338  0.4227  0.3041     0.054  \n",
              "ridge      70300.7594  0.6338  0.4219  0.3041     0.015  \n",
              "llar       70299.8538  0.6338  0.4221  0.3041     0.015  \n",
              "br         70300.7411  0.6338  0.4215  0.3040     0.016  \n",
              "dt         71613.8066  0.6197  0.3297  0.2431     0.099  \n",
              "en         73617.5172  0.5983  0.3820  0.3339     0.053  \n",
              "huber      77534.6915  0.5540  0.4120  0.3142     0.149  \n",
              "omp        83697.6016  0.4814  0.4254  0.3829     0.014  \n",
              "ada        91062.7844  0.3844  0.5139  0.5793     0.447  \n",
              "knn       101272.3164  0.2404  0.5270  0.5134     0.072  \n",
              "dummy     116317.5633 -0.0008  0.5933  0.6257     0.012  \n",
              "lar       153914.1986 -1.1167  0.7165  0.5922     0.016  \n",
              "par       255217.0428 -6.2186  1.0214  1.1237     0.056  "
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>MAE</th>\n",
              "      <th>MSE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>R2</th>\n",
              "      <th>RMSLE</th>\n",
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              "      <th>TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>lightgbm</th>\n",
              "      <td>Light Gradient Boosting Machine</td>\n",
              "      <td>32394.1972</td>\n",
              "      <td>2.349034e+09</td>\n",
              "      <td>48415.3656</td>\n",
              "      <td>0.8263</td>\n",
              "      <td>0.2385</td>\n",
              "      <td>0.1816</td>\n",
              "      <td>0.351</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rf</th>\n",
              "      <td>Random Forest Regressor</td>\n",
              "      <td>32907.6818</td>\n",
              "      <td>2.511345e+09</td>\n",
              "      <td>50054.3880</td>\n",
              "      <td>0.8143</td>\n",
              "      <td>0.2385</td>\n",
              "      <td>0.1821</td>\n",
              "      <td>6.288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>et</th>\n",
              "      <td>Extra Trees Regressor</td>\n",
              "      <td>35920.9350</td>\n",
              "      <td>2.859327e+09</td>\n",
              "      <td>53402.6410</td>\n",
              "      <td>0.7886</td>\n",
              "      <td>0.2529</td>\n",
              "      <td>0.1996</td>\n",
              "      <td>2.373</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gbr</th>\n",
              "      <td>Gradient Boosting Regressor</td>\n",
              "      <td>38175.6441</td>\n",
              "      <td>3.039162e+09</td>\n",
              "      <td>55068.8260</td>\n",
              "      <td>0.7753</td>\n",
              "      <td>0.2722</td>\n",
              "      <td>0.2171</td>\n",
              "      <td>2.353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lr</th>\n",
              "      <td>Linear Regression</td>\n",
              "      <td>51148.3016</td>\n",
              "      <td>4.950486e+09</td>\n",
              "      <td>70300.6656</td>\n",
              "      <td>0.6338</td>\n",
              "      <td>0.4228</td>\n",
              "      <td>0.3041</td>\n",
              "      <td>0.294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lasso</th>\n",
              "      <td>Lasso Regression</td>\n",
              "      <td>51148.2266</td>\n",
              "      <td>4.950502e+09</td>\n",
              "      <td>70300.7738</td>\n",
              "      <td>0.6338</td>\n",
              "      <td>0.4227</td>\n",
              "      <td>0.3041</td>\n",
              "      <td>0.054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ridge</th>\n",
              "      <td>Ridge Regression</td>\n",
              "      <td>51148.0633</td>\n",
              "      <td>4.950500e+09</td>\n",
              "      <td>70300.7594</td>\n",
              "      <td>0.6338</td>\n",
              "      <td>0.4219</td>\n",
              "      <td>0.3041</td>\n",
              "      <td>0.015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>llar</th>\n",
              "      <td>Lasso Least Angle Regression</td>\n",
              "      <td>51167.6747</td>\n",
              "      <td>4.950387e+09</td>\n",
              "      <td>70299.8538</td>\n",
              "      <td>0.6338</td>\n",
              "      <td>0.4221</td>\n",
              "      <td>0.3041</td>\n",
              "      <td>0.015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>br</th>\n",
              "      <td>Bayesian Ridge</td>\n",
              "      <td>51146.5855</td>\n",
              "      <td>4.950498e+09</td>\n",
              "      <td>70300.7411</td>\n",
              "      <td>0.6338</td>\n",
              "      <td>0.4215</td>\n",
              "      <td>0.3040</td>\n",
              "      <td>0.016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dt</th>\n",
              "      <td>Decision Tree Regressor</td>\n",
              "      <td>45598.1058</td>\n",
              "      <td>5.142616e+09</td>\n",
              "      <td>71613.8066</td>\n",
              "      <td>0.6197</td>\n",
              "      <td>0.3297</td>\n",
              "      <td>0.2431</td>\n",
              "      <td>0.099</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>en</th>\n",
              "      <td>Elastic Net</td>\n",
              "      <td>54330.6438</td>\n",
              "      <td>5.427388e+09</td>\n",
              "      <td>73617.5172</td>\n",
              "      <td>0.5983</td>\n",
              "      <td>0.3820</td>\n",
              "      <td>0.3339</td>\n",
              "      <td>0.053</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>huber</th>\n",
              "      <td>Huber Regressor</td>\n",
              "      <td>55641.8266</td>\n",
              "      <td>6.018488e+09</td>\n",
              "      <td>77534.6915</td>\n",
              "      <td>0.5540</td>\n",
              "      <td>0.4120</td>\n",
              "      <td>0.3142</td>\n",
              "      <td>0.149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>omp</th>\n",
              "      <td>Orthogonal Matching Pursuit</td>\n",
              "      <td>62302.3394</td>\n",
              "      <td>7.013196e+09</td>\n",
              "      <td>83697.6016</td>\n",
              "      <td>0.4814</td>\n",
              "      <td>0.4254</td>\n",
              "      <td>0.3829</td>\n",
              "      <td>0.014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ada</th>\n",
              "      <td>AdaBoost Regressor</td>\n",
              "      <td>78877.5364</td>\n",
              "      <td>8.303166e+09</td>\n",
              "      <td>91062.7844</td>\n",
              "      <td>0.3844</td>\n",
              "      <td>0.5139</td>\n",
              "      <td>0.5793</td>\n",
              "      <td>0.447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>knn</th>\n",
              "      <td>K Neighbors Regressor</td>\n",
              "      <td>77950.0633</td>\n",
              "      <td>1.025934e+10</td>\n",
              "      <td>101272.3164</td>\n",
              "      <td>0.2404</td>\n",
              "      <td>0.5270</td>\n",
              "      <td>0.5134</td>\n",
              "      <td>0.072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dummy</th>\n",
              "      <td>Dummy Regressor</td>\n",
              "      <td>91717.4633</td>\n",
              "      <td>1.353694e+10</td>\n",
              "      <td>116317.5633</td>\n",
              "      <td>-0.0008</td>\n",
              "      <td>0.5933</td>\n",
              "      <td>0.6257</td>\n",
              "      <td>0.012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lar</th>\n",
              "      <td>Least Angle Regression</td>\n",
              "      <td>90158.3099</td>\n",
              "      <td>2.923356e+10</td>\n",
              "      <td>153914.1986</td>\n",
              "      <td>-1.1167</td>\n",
              "      <td>0.7165</td>\n",
              "      <td>0.5922</td>\n",
              "      <td>0.016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>par</th>\n",
              "      <td>Passive Aggressive Regressor</td>\n",
              "      <td>191457.5591</td>\n",
              "      <td>9.557136e+10</td>\n",
              "      <td>255217.0428</td>\n",
              "      <td>-6.2186</td>\n",
              "      <td>1.0214</td>\n",
              "      <td>1.1237</td>\n",
              "      <td>0.056</td>\n",
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            "INFO:logs:create_model_container: 18\n",
            "INFO:logs:master_model_container: 18\n",
            "INFO:logs:display_container: 2\n",
            "INFO:logs:LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
            "              importance_type='split', learning_rate=0.1, max_depth=-1,\n",
            "              min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,\n",
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            "              random_state=8140, reg_alpha=0.0, reg_lambda=0.0, silent='warn',\n",
            "              subsample=1.0, subsample_for_bin=200000, subsample_freq=0)\n",
            "INFO:logs:compare_models() succesfully completed......................................\n"
          ]
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    {
      "cell_type": "code",
      "source": [
        "#Explore the best model parameter\n",
        "print(best)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c_kCHdDkunuX",
        "outputId": "6a37f23b-5f0a-45e7-a60b-fbe1f236d061"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
            "              importance_type='split', learning_rate=0.1, max_depth=-1,\n",
            "              min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,\n",
            "              n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,\n",
            "              random_state=8140, reg_alpha=0.0, reg_lambda=0.0, silent='warn',\n",
            "              subsample=1.0, subsample_for_bin=200000, subsample_freq=0)\n"
          ]
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      "cell_type": "code",
      "source": [
        "finalize_model(best)"
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        },
        "id": "3_oCCiguuxIv",
        "outputId": "c5d725a6-89cc-474e-e598-89c625d1e31b"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:logs:Initializing finalize_model()\n",
            "INFO:logs:finalize_model(estimator=LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
            "              importance_type='split', learning_rate=0.1, max_depth=-1,\n",
            "              min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,\n",
            "              n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,\n",
            "              random_state=8140, reg_alpha=0.0, reg_lambda=0.0, silent='warn',\n",
            "              subsample=1.0, subsample_for_bin=200000, subsample_freq=0), fit_kwargs=None, groups=None, model_only=True, display=None, experiment_custom_tags=None, return_train_score=False)\n",
            "INFO:logs:Finalizing LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
            "              importance_type='split', learning_rate=0.1, max_depth=-1,\n",
            "              min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,\n",
            "              n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,\n",
            "              random_state=8140, reg_alpha=0.0, reg_lambda=0.0, silent='warn',\n",
            "              subsample=1.0, subsample_for_bin=200000, subsample_freq=0)\n",
            "INFO:logs:Initializing create_model()\n",
            "INFO:logs:create_model(estimator=LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
            "              importance_type='split', learning_rate=0.1, max_depth=-1,\n",
            "              min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,\n",
            "              n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,\n",
            "              random_state=8140, reg_alpha=0.0, reg_lambda=0.0, silent='warn',\n",
            "              subsample=1.0, subsample_for_bin=200000, subsample_freq=0), fold=None, round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, verbose=False, system=False, metrics=None, experiment_custom_tags=None, add_to_model_list=False, probability_threshold=None, display=None, return_train_score=False, kwargs={})\n",
            "INFO:logs:Checking exceptions\n",
            "INFO:logs:Importing libraries\n",
            "INFO:logs:Copying training dataset\n",
            "INFO:logs:Defining folds\n",
            "INFO:logs:Declaring metric variables\n",
            "INFO:logs:Importing untrained model\n",
            "INFO:logs:Declaring custom model\n",
            "INFO:logs:Light Gradient Boosting Machine Imported succesfully\n",
            "INFO:logs:Starting cross validation\n",
            "INFO:logs:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1\n",
            "INFO:logs:Calculating mean and std\n",
            "INFO:logs:Creating metrics dataframe\n",
            "INFO:logs:Finalizing model\n",
            "INFO:logs:create_model_container: 18\n",
            "INFO:logs:master_model_container: 18\n",
            "INFO:logs:display_container: 3\n",
            "INFO:logs:LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
            "              importance_type='split', learning_rate=0.1, max_depth=-1,\n",
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            "              subsample=1.0, subsample_for_bin=200000, subsample_freq=0)\n",
            "INFO:logs:create_model() succesfully completed......................................\n",
            "INFO:logs:create_model_container: 18\n",
            "INFO:logs:master_model_container: 18\n",
            "INFO:logs:display_container: 2\n",
            "INFO:logs:LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
            "              importance_type='split', learning_rate=0.1, max_depth=-1,\n",
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            "              subsample=1.0, subsample_for_bin=200000, subsample_freq=0)\n",
            "INFO:logs:finalize_model() succesfully completed......................................\n"
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              "LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
              "              importance_type='split', learning_rate=0.1, max_depth=-1,\n",
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            "INFO:logs:Initializing evaluate_model()\n",
            "INFO:logs:evaluate_model(estimator=LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
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            "INFO:logs:Initializing predict_model()\n",
            "INFO:logs:predict_model(estimator=LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
            "              importance_type='split', learning_rate=0.1, max_depth=-1,\n",
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            "INFO:logs:Checking exceptions\n",
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              "                             Model         MAE           MSE       RMSE  \\\n",
              "0  Light Gradient Boosting Machine  32275.4268  2.237674e+09  47304.054   \n",
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              "      <th>Model</th>\n",
              "      <th>MAE</th>\n",
              "      <th>MSE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>R2</th>\n",
              "      <th>RMSLE</th>\n",
              "      <th>MAPE</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Light Gradient Boosting Machine</td>\n",
              "      <td>32275.4268</td>\n",
              "      <td>2.237674e+09</td>\n",
              "      <td>47304.054</td>\n",
              "      <td>0.8312</td>\n",
              "      <td>0.2353</td>\n",
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              "       longitude   latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "0    -118.279999  33.939999                43.0       1201.0           292.0   \n",
              "1    -118.360001  33.980000                45.0       1559.0           305.0   \n",
              "2    -122.300003  37.900002                38.0       2263.0           522.0   \n",
              "3    -119.230003  34.250000                28.0         26.0             3.0   \n",
              "4    -120.930000  37.560001                17.0       1812.0           361.0   \n",
              "...          ...        ...                 ...          ...             ...   \n",
              "5096 -122.089996  37.680000                43.0       1415.0           348.0   \n",
              "5097 -117.790001  33.709999                16.0       6339.0           862.0   \n",
              "5098 -118.410004  34.209999                35.0       2830.0           518.0   \n",
              "5099 -121.889999  37.230000                20.0       7754.0           976.0   \n",
              "5100 -118.089996  33.959999                36.0       3271.0           603.0   \n",
              "\n",
              "      population  households  median_income  median_house_value          Label  \n",
              "0          840.0       252.0         2.7917            105600.0  126356.992195  \n",
              "1          891.0       341.0         4.4038            259400.0  205176.320373  \n",
              "2         1027.0       509.0         3.5125            224200.0  237089.245597  \n",
              "3           29.0         9.0         8.0000            275000.0  366831.530531  \n",
              "4          672.0       334.0         1.5500            166100.0  109039.021546  \n",
              "...          ...         ...            ...                 ...            ...  \n",
              "5096       569.0       293.0         2.5156            190900.0  176786.957220  \n",
              "5097      3132.0       825.0         7.1069            313400.0  318930.046299  \n",
              "5098      1577.0       524.0         5.3500            210500.0  253102.579414  \n",
              "5099      3094.0       941.0         8.1900            361600.0  427701.236514  \n",
              "5100      2593.0       616.0         3.3621            169700.0  161210.502515  \n",
              "\n",
              "[5101 rows x 10 columns]"
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-118.360001</td>\n",
              "      <td>33.980000</td>\n",
              "      <td>45.0</td>\n",
              "      <td>1559.0</td>\n",
              "      <td>305.0</td>\n",
              "      <td>891.0</td>\n",
              "      <td>341.0</td>\n",
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              "      <td>2263.0</td>\n",
              "      <td>522.0</td>\n",
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              "      <td>-120.930000</td>\n",
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              "      <td>1812.0</td>\n",
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              "      <th>5096</th>\n",
              "      <td>-122.089996</td>\n",
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              "      <td>43.0</td>\n",
              "      <td>1415.0</td>\n",
              "      <td>348.0</td>\n",
              "      <td>569.0</td>\n",
              "      <td>293.0</td>\n",
              "      <td>2.5156</td>\n",
              "      <td>190900.0</td>\n",
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              "      <td>976.0</td>\n",
              "      <td>3094.0</td>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "calif_test_df = pd.read_csv('california_housing_train.csv')\n",
        "\n",
        "predict_model(best, calif_test_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 681
        },
        "id": "DrLh0nptu0rR",
        "outputId": "ff00d5b0-5734-47a4-b54d-e5f294bc2db2"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:logs:Initializing predict_model()\n",
            "INFO:logs:predict_model(estimator=LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
            "              importance_type='split', learning_rate=0.1, max_depth=-1,\n",
            "              min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,\n",
            "              n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,\n",
            "              random_state=8140, reg_alpha=0.0, reg_lambda=0.0, silent='warn',\n",
            "              subsample=1.0, subsample_for_bin=200000, subsample_freq=0), probability_threshold=None, encoded_labels=True, drift_report=False, raw_score=False, round=4, verbose=True, ml_usecase=MLUsecase.REGRESSION, display=None, drift_kwargs=None)\n",
            "INFO:logs:Checking exceptions\n",
            "INFO:logs:Preloading libraries\n",
            "INFO:logs:Preparing display monitor\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                             Model         MAE           MSE        RMSE  \\\n",
              "0  Light Gradient Boosting Machine  28816.3294  1.769655e+09  42067.2706   \n",
              "\n",
              "       R2   RMSLE    MAPE  \n",
              "0  0.8684  0.2147  0.1638  "
            ],
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Light Gradient Boosting Machine</td>\n",
              "      <td>28816.3294</td>\n",
              "      <td>1.769655e+09</td>\n",
              "      <td>42067.2706</td>\n",
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          "data": {
            "text/plain": [
              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "0        -114.31     34.19                15.0       5612.0          1283.0   \n",
              "1        -114.47     34.40                19.0       7650.0          1901.0   \n",
              "2        -114.56     33.69                17.0        720.0           174.0   \n",
              "3        -114.57     33.64                14.0       1501.0           337.0   \n",
              "4        -114.57     33.57                20.0       1454.0           326.0   \n",
              "...          ...       ...                 ...          ...             ...   \n",
              "16995    -124.26     40.58                52.0       2217.0           394.0   \n",
              "16996    -124.27     40.69                36.0       2349.0           528.0   \n",
              "16997    -124.30     41.84                17.0       2677.0           531.0   \n",
              "16998    -124.30     41.80                19.0       2672.0           552.0   \n",
              "16999    -124.35     40.54                52.0       1820.0           300.0   \n",
              "\n",
              "       population  households  median_income  median_house_value  \\\n",
              "0          1015.0       472.0         1.4936             66900.0   \n",
              "1          1129.0       463.0         1.8200             80100.0   \n",
              "2           333.0       117.0         1.6509             85700.0   \n",
              "3           515.0       226.0         3.1917             73400.0   \n",
              "4           624.0       262.0         1.9250             65500.0   \n",
              "...           ...         ...            ...                 ...   \n",
              "16995       907.0       369.0         2.3571            111400.0   \n",
              "16996      1194.0       465.0         2.5179             79000.0   \n",
              "16997      1244.0       456.0         3.0313            103600.0   \n",
              "16998      1298.0       478.0         1.9797             85800.0   \n",
              "16999       806.0       270.0         3.0147             94600.0   \n",
              "\n",
              "               Label  \n",
              "0       52185.151700  \n",
              "1       57634.549553  \n",
              "2       65157.919439  \n",
              "3       98543.117524  \n",
              "4       71101.548103  \n",
              "...              ...  \n",
              "16995   88793.945083  \n",
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              "16997   96005.116615  \n",
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              "\n",
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              "<p>17000 rows × 10 columns</p>\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bf0b3b22-5a7b-4737-92fe-e0c8efd06827');\n",
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              "            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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    }
  ]
}