house_prize.ipynb

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      "source": [
        "#house prize prediction\n",
        "\n",
        "#supervised learning is of two types\n",
        "#1. classification  : predicts a class eg yes/no\n",
        "#2. regression : predicts a value eg rate\n",
        "\n",
        "#steps\n",
        "#step 1 : data preprocessing\n",
        "#step 2 : data analysis\n",
        "#step 3 : train test split\n",
        "#step 4 : XGBoosRegressor\n",
        "#step 5 : Evaluation\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#importing the dependencies\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import sklearn.datasets\n",
        "from sklearn.model_selection import train_test_split\n",
        "from xgboost import XGBRegressor\n",
        "from sklearn import metrics"
      ],
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      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "#importing the Boston House Price Dataset\n",
        "house_price_data = pd.read_csv('/content/housing.csv')"
      ],
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      "execution_count": null,
      "outputs": []
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      "source": [
        "print(house_price_data)"
      ],
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          "text": [
            "        RM  LSTAT  PTRATIO      MEDV\n",
            "0    6.575   4.98     15.3  504000.0\n",
            "1    6.421   9.14     17.8  453600.0\n",
            "2    7.185   4.03     17.8  728700.0\n",
            "3    6.998   2.94     18.7  701400.0\n",
            "4    7.147   5.33     18.7  760200.0\n",
            "..     ...    ...      ...       ...\n",
            "484  6.593   9.67     21.0  470400.0\n",
            "485  6.120   9.08     21.0  432600.0\n",
            "486  6.976   5.64     21.0  501900.0\n",
            "487  6.794   6.48     21.0  462000.0\n",
            "488  6.030   7.88     21.0  249900.0\n",
            "\n",
            "[489 rows x 4 columns]\n"
          ]
        }
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      "source": [
        "house_price_data.isnull().sum()"
      ],
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          "data": {
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              "RM         0\n",
              "LSTAT      0\n",
              "PTRATIO    0\n",
              "MEDV       0\n",
              "dtype: int64"
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          },
          "metadata": {},
          "execution_count": 25
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      "source": [
        "house_price_data.describe()"
      ],
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      "outputs": [
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              "               RM       LSTAT     PTRATIO          MEDV\n",
              "count  489.000000  489.000000  489.000000  4.890000e+02\n",
              "mean     6.240288   12.939632   18.516564  4.543429e+05\n",
              "std      0.643650    7.081990    2.111268  1.653403e+05\n",
              "min      3.561000    1.980000   12.600000  1.050000e+05\n",
              "25%      5.880000    7.370000   17.400000  3.507000e+05\n",
              "50%      6.185000   11.690000   19.100000  4.389000e+05\n",
              "75%      6.575000   17.120000   20.200000  5.187000e+05\n",
              "max      8.398000   37.970000   22.000000  1.024800e+06"
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              "      <th>RM</th>\n",
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              "      <td>489.000000</td>\n",
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              "      <td>4.890000e+02</td>\n",
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              "      <th>mean</th>\n",
              "      <td>6.240288</td>\n",
              "      <td>12.939632</td>\n",
              "      <td>18.516564</td>\n",
              "      <td>4.543429e+05</td>\n",
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              "      <th>std</th>\n",
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              "      <td>7.081990</td>\n",
              "      <td>2.111268</td>\n",
              "      <td>1.653403e+05</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>3.561000</td>\n",
              "      <td>1.980000</td>\n",
              "      <td>12.600000</td>\n",
              "      <td>1.050000e+05</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.880000</td>\n",
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              "      <td>3.507000e+05</td>\n",
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              "      <th>50%</th>\n",
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              "      <td>4.389000e+05</td>\n",
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              "      <th>75%</th>\n",
              "      <td>6.575000</td>\n",
              "      <td>17.120000</td>\n",
              "      <td>20.200000</td>\n",
              "      <td>5.187000e+05</td>\n",
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              "      <th>max</th>\n",
              "      <td>8.398000</td>\n",
              "      <td>37.970000</td>\n",
              "      <td>22.000000</td>\n",
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      "source": [
        "correlation = house_price_data.corr()\n",
        "print(correlation)\n",
        "\n",
        "#constructing a heat map \n",
        "plt.figure(figsize=(5,5))\n",
        "sns.heatmap(correlation, cbar=True, fmt='.1f', annot = True, annot_kws={'size':8}, cmap ='Blues' )\n",
        "\n",
        "#light color is negative correlation\n"
      ],
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          "text": [
            "               RM     LSTAT   PTRATIO      MEDV\n",
            "RM       1.000000 -0.612033 -0.304559  0.697209\n",
            "LSTAT   -0.612033  1.000000  0.360445 -0.760670\n",
            "PTRATIO -0.304559  0.360445  1.000000 -0.519034\n",
            "MEDV     0.697209 -0.760670 -0.519034  1.000000\n"
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              "<Axes: >"
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              "<Figure size 500x500 with 2 Axes>"
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X =  house_price_data.drop(['MEDV'], axis=1)\n",
        "Y = house_price_data['MEDV']\n",
        "print(X)\n",
        "print(Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M-IHSczsbciA",
        "outputId": "b0943d6c-853a-4320-af24-76ffb9d77a77"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        RM  LSTAT  PTRATIO\n",
            "0    6.575   4.98     15.3\n",
            "1    6.421   9.14     17.8\n",
            "2    7.185   4.03     17.8\n",
            "3    6.998   2.94     18.7\n",
            "4    7.147   5.33     18.7\n",
            "..     ...    ...      ...\n",
            "484  6.593   9.67     21.0\n",
            "485  6.120   9.08     21.0\n",
            "486  6.976   5.64     21.0\n",
            "487  6.794   6.48     21.0\n",
            "488  6.030   7.88     21.0\n",
            "\n",
            "[489 rows x 3 columns]\n",
            "0      504000.0\n",
            "1      453600.0\n",
            "2      728700.0\n",
            "3      701400.0\n",
            "4      760200.0\n",
            "         ...   \n",
            "484    470400.0\n",
            "485    432600.0\n",
            "486    501900.0\n",
            "487    462000.0\n",
            "488    249900.0\n",
            "Name: MEDV, Length: 489, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size=0.2, random_state=2, shuffle =True)"
      ],
      "metadata": {
        "id": "9NwjRXzdn6_o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#model training\n",
        "model = XGBRegressor()\n",
        "model.fit(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "wzUJWHAPogxY",
        "outputId": "0601b9f1-62d9-4e11-b0c0-e4d16a5470f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "             predictor=None, random_state=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "             predictor=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "             predictor=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#model evaluation\n",
        "training_data_prediction = model.predict(X_train)\n",
        "print(training_data_prediction)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UM0AgEXIokXd",
        "outputId": "ab5229de-ed8b-46e9-d9e0-78b25bd1adcb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 405247.28  426922.75  559945.2   499750.2   181138.83  458718.4\n",
            "  360702.75  657246.5   665706.94  229552.39  449962.8   356688.03\n",
            "  475844.12  387991.06  726962.6   302455.84  597436.94  437218.88\n",
            "  466406.53  366179.25  427043.5   377458.6   181921.48  242247.2\n",
            "  360655.5   517901.5   231625.8   456420.    180035.44  509141.9\n",
            "  619996.1  1008413.25  498681.2   508653.5   334431.06  373152.34\n",
            "  494776.94  327879.3   264194.2   835349.    316813.47  282274.66\n",
            "  366723.1   401584.56  569765.9   473543.6   576158.06  407330.5\n",
            "  397615.78  500850.16  271308.66  487782.03  111468.3   441769.22\n",
            "  208889.64  289186.2   685153.06  533914.9   511094.22  375052.12\n",
            "  430940.84  417979.6   364737.84  590299.94  594458.2   702265.7\n",
            "  530272.1   490490.72  161760.55  687188.7   477163.2   289788.62\n",
            "  380863.06  490419.6   214232.94  483072.16  264607.7   661569.5\n",
            "  428290.22  724769.1   220151.    478548.12  410908.    858892.\n",
            "  472726.72  395720.    461245.53  472419.97  414860.88  406499.06\n",
            "  396337.03  405211.9   627381.94  205223.28  697517.06  380283.97\n",
            "  233762.23  394568.3   537963.06  377580.94  464166.06  523315.22\n",
            "  416187.5   657860.7   305112.53  675647.3   307528.9   917112.94\n",
            "  610103.9   423577.4   308700.1   293214.88  437189.84  249337.83\n",
            "  391589.03  322787.22  943683.1   173397.92  407709.47  639847.06\n",
            "  493977.1   409935.38  507496.6   438753.56  508643.16  618132.56\n",
            "  345184.78  331808.72  409961.9   584076.25  341051.7   518993.2\n",
            "  408598.25  670353.75  213427.19  404181.5   290146.22  709635.2\n",
            "  289996.    506113.44  372308.5   406295.53  465140.66  214348.14\n",
            "  490309.5   148327.69  675757.7   303296.78  276639.2   176742.5\n",
            "  667792.44  455090.16  562920.06  700337.94  640814.7   623643.\n",
            "  489470.44  465989.75  187024.83  401705.78  181926.4   756541.1\n",
            "  362851.12  422314.53  221694.11  299253.9   498929.12  404542.4\n",
            "  550419.44  223873.53 1022739.4   519543.16  119767.27  489802.06\n",
            "  517686.62  282321.53  454416.25  465917.78  693063.94  956503.7\n",
            "  523421.66  376468.78  461217.34  416337.3   275578.7   414159.34\n",
            "  333468.    402084.34  553620.44  131454.84  598007.06  447857.47\n",
            "  394821.2   979209.94  293433.3   589816.7   382639.56  529918.56\n",
            "  520577.03  427027.38  527036.4   384706.8   375085.28  464455.97\n",
            "  585245.4   734158.75  465354.34  471436.25  412326.06  513131.8\n",
            "  246737.53  394040.94  431466.94  405962.97  631052.44  343464.88\n",
            "  433576.06  420886.28  301487.3   425397.78  470904.1   520813.12\n",
            "  292138.28  423812.6   356044.25  486469.16  440644.22  489903.7\n",
            " 1016939.    644441.25  373547.97  387513.16  431057.53  432478.38\n",
            "  346602.47  373365.12  246326.08  447541.2   638361.06  464330.4\n",
            "  412295.88  618169.94  961040.8   299226.72  508912.94  317737.62\n",
            "  445698.06  398070.34  421745.12  242262.52  372620.3   249359.27\n",
            "  419799.84  669058.25  314342.1   561051.1   760219.25  377379.62\n",
            "  510936.53  312719.78  449543.6   449360.66  521583.72  693614.7\n",
            "  482077.66  898716.6   465648.5   510516.88  310582.62  414764.25\n",
            "  637092.56  290119.38  743692.4   762011.56  372169.5   436575.5\n",
            "  290812.84  327972.53  344796.84  507186.3   326791.9   676366.06\n",
            "  412078.7   420260.97  431530.22  523996.6   249300.44  324004.03\n",
            "  362220.53  434364.1   479593.16  876017.5   279504.72  660677.56\n",
            "  447624.    465946.38  381898.22  813502.94  446225.28  332638.88\n",
            "  503273.47  254758.53  439271.94  251871.36  519870.22  381452.84\n",
            "  466206.88  389973.16  466443.9   356868.34  467207.56  282905.66\n",
            "  593438.75  279477.78  650525.2   420294.62  315432.22  354493.78\n",
            "  613651.1   518581.34  458353.34  482043.9   905005.56  248037.92\n",
            "  261426.64  774772.5   490385.53  321030.53  922507.4   286157.\n",
            "  197287.75  440996.12  572771.75  428159.8   746164.    365906.28\n",
            "  277753.8   701294.1   405533.5   491248.25  171350.5   319052.28\n",
            "  482068.44  422545.78  497769.94  525503.9   590847.6   312241.3\n",
            "  425742.    450917.97  436113.47  456831.6   891892.75  503662.75\n",
            "  408820.5   444346.8   765374.8   183796.64  485144.97  276404.62\n",
            "  505377.16  288994.5   437883.3   220229.5   318169.9   448920.47\n",
            "  397816.34  505279.34  404881.1   750087.25  445527.62  396086.47\n",
            "  299929.25  792770.44  472160.    420688.62  354092.78  568636.4\n",
            "  366721.62  589427.4   288425.5   521954.84  743118.06  279261.2\n",
            "  457890.47  450817.03  337340.88  323717.5   479225.38  416715.8\n",
            "  484013.3 ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score1 = metrics.r2_score(Y_train, training_data_prediction)\n",
        "print(score1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mA3KmGIk4IIp",
        "outputId": "d210b868-1a35-4614-a671-7dbcc178c636"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9989101715438984\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_data_prediction = model.predict(X_test)\n",
        "score2 = metrics.r2_score(Y_test , test_data_prediction)\n",
        "print(score2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T1L-VqLP48dT",
        "outputId": "26905d8c-98cd-409b-bcfc-946875c2518a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7895987898308828\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(Y_train, training_data_prediction)\n",
        "plt.xlabel('Actual Prizes')\n",
        "plt.ylabel('Predicted Prizes')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "NtS0KRJE5vBH",
        "outputId": "7398e10f-613d-4488-9a08-2f3791ee5c34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#changing the input data to numpy array\n",
        "\n",
        "input_data = (6.575,4.98,15.3)\n",
        "input_data_as_numpy_array = np.asarray(input_data)\n",
        "\n",
        "#reshape the np array as we are predicting for one instance\n",
        "input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n",
        "\n",
        "prediction = model.predict(input_data_reshaped)\n",
        "print(prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6N3RlVeK6LQY",
        "outputId": "a62be95f-7158-456b-deb9-7298e9a7056d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[510516.88]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "filename = 'trained_model.sav'\n",
        "pickle.dump(model, open(filename, 'wb'))\n",
        "\n",
        "# loading the saved model\n",
        "loaded_model = pickle.load(open('trained_model.sav', 'rb'))\n"
      ],
      "metadata": {
        "id": "VveoQ4Ab7_Hf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "m_W2XuXYDp6Q"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}