Task_1_House_Price_Prediction.ipynb

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      "source": [
        "**Task:- Use a dataset that includes information about\n",
        "housing prices and features like square\n",
        "footage, number of bedrooms, etc. to train a\n",
        "model that can predict the price of a new\n",
        "house**"
      ],
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      "source": [
        "# **1. Importing Libraries**"
      ],
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        "id": "BOH3wM85mMkt"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
        "import statsmodels.api as sm\n",
        "from sklearn.model_selection import train_test_split\n",
        "import xgboost as xgb\n",
        "from sklearn.metrics import mean_absolute_error, r2_score\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "HousePrice = pd.read_csv('/content/kc_house_data.csv')"
      ],
      "metadata": {
        "id": "1Jcn6VR_B6LG"
      },
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "HousePrice.head()"
      ],
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              "           id             date     price  bedrooms  bathrooms  sqft_living  \\\n",
              "0  7129300520  20141013T000000  221900.0         3       1.00         1180   \n",
              "1  6414100192  20141209T000000  538000.0         3       2.25         2570   \n",
              "2  5631500400  20150225T000000  180000.0         2       1.00          770   \n",
              "3  2487200875  20141209T000000  604000.0         4       3.00         1960   \n",
              "4  1954400510  20150218T000000  510000.0         3       2.00         1680   \n",
              "\n",
              "   sqft_lot  floors  waterfront  view  ...  grade  sqft_above  sqft_basement  \\\n",
              "0      5650     1.0           0     0  ...      7        1180              0   \n",
              "1      7242     2.0           0     0  ...      7        2170            400   \n",
              "2     10000     1.0           0     0  ...      6         770              0   \n",
              "3      5000     1.0           0     0  ...      7        1050            910   \n",
              "4      8080     1.0           0     0  ...      8        1680              0   \n",
              "\n",
              "   yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
              "0      1955             0    98178  47.5112 -122.257           1340   \n",
              "1      1951          1991    98125  47.7210 -122.319           1690   \n",
              "2      1933             0    98028  47.7379 -122.233           2720   \n",
              "3      1965             0    98136  47.5208 -122.393           1360   \n",
              "4      1987             0    98074  47.6168 -122.045           1800   \n",
              "\n",
              "   sqft_lot15  \n",
              "0        5650  \n",
              "1        7639  \n",
              "2        8062  \n",
              "3        5000  \n",
              "4        7503  \n",
              "\n",
              "[5 rows x 21 columns]"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 21613 entries, 0 to 21612\n",
            "Data columns (total 21 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   id             21613 non-null  int64  \n",
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            " 5   sqft_living    21613 non-null  int64  \n",
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            " 8   waterfront     21613 non-null  int64  \n",
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            " 10  condition      21613 non-null  int64  \n",
            " 11  grade          21613 non-null  int64  \n",
            " 12  sqft_above     21613 non-null  int64  \n",
            " 13  sqft_basement  21613 non-null  int64  \n",
            " 14  yr_built       21613 non-null  int64  \n",
            " 15  yr_renovated   21613 non-null  int64  \n",
            " 16  zipcode        21613 non-null  int64  \n",
            " 17  lat            21613 non-null  float64\n",
            " 18  long           21613 non-null  float64\n",
            " 19  sqft_living15  21613 non-null  int64  \n",
            " 20  sqft_lot15     21613 non-null  int64  \n",
            "dtypes: float64(5), int64(15), object(1)\n",
            "memory usage: 3.5+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "HousePrice.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gwr6OxB4Nung",
        "outputId": "3e245f4a-8135-43da-e298-719df23fc2c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 id         price      bedrooms     bathrooms   sqft_living  \\\n",
              "count  2.161300e+04  2.161300e+04  21613.000000  21613.000000  21613.000000   \n",
              "mean   4.580302e+09  5.400881e+05      3.370842      2.114757   2079.899736   \n",
              "std    2.876566e+09  3.671272e+05      0.930062      0.770163    918.440897   \n",
              "min    1.000102e+06  7.500000e+04      0.000000      0.000000    290.000000   \n",
              "25%    2.123049e+09  3.219500e+05      3.000000      1.750000   1427.000000   \n",
              "50%    3.904930e+09  4.500000e+05      3.000000      2.250000   1910.000000   \n",
              "75%    7.308900e+09  6.450000e+05      4.000000      2.500000   2550.000000   \n",
              "max    9.900000e+09  7.700000e+06     33.000000      8.000000  13540.000000   \n",
              "\n",
              "           sqft_lot        floors    waterfront          view     condition  \\\n",
              "count  2.161300e+04  21613.000000  21613.000000  21613.000000  21613.000000   \n",
              "mean   1.510697e+04      1.494309      0.007542      0.234303      3.409430   \n",
              "std    4.142051e+04      0.539989      0.086517      0.766318      0.650743   \n",
              "min    5.200000e+02      1.000000      0.000000      0.000000      1.000000   \n",
              "25%    5.040000e+03      1.000000      0.000000      0.000000      3.000000   \n",
              "50%    7.618000e+03      1.500000      0.000000      0.000000      3.000000   \n",
              "75%    1.068800e+04      2.000000      0.000000      0.000000      4.000000   \n",
              "max    1.651359e+06      3.500000      1.000000      4.000000      5.000000   \n",
              "\n",
              "              grade    sqft_above  sqft_basement      yr_built  yr_renovated  \\\n",
              "count  21613.000000  21613.000000   21613.000000  21613.000000  21613.000000   \n",
              "mean       7.656873   1788.390691     291.509045   1971.005136     84.402258   \n",
              "std        1.175459    828.090978     442.575043     29.373411    401.679240   \n",
              "min        1.000000    290.000000       0.000000   1900.000000      0.000000   \n",
              "25%        7.000000   1190.000000       0.000000   1951.000000      0.000000   \n",
              "50%        7.000000   1560.000000       0.000000   1975.000000      0.000000   \n",
              "75%        8.000000   2210.000000     560.000000   1997.000000      0.000000   \n",
              "max       13.000000   9410.000000    4820.000000   2015.000000   2015.000000   \n",
              "\n",
              "            zipcode           lat          long  sqft_living15     sqft_lot15  \n",
              "count  21613.000000  21613.000000  21613.000000   21613.000000   21613.000000  \n",
              "mean   98077.939805     47.560053   -122.213896    1986.552492   12768.455652  \n",
              "std       53.505026      0.138564      0.140828     685.391304   27304.179631  \n",
              "min    98001.000000     47.155900   -122.519000     399.000000     651.000000  \n",
              "25%    98033.000000     47.471000   -122.328000    1490.000000    5100.000000  \n",
              "50%    98065.000000     47.571800   -122.230000    1840.000000    7620.000000  \n",
              "75%    98118.000000     47.678000   -122.125000    2360.000000   10083.000000  \n",
              "max    98199.000000     47.777600   -121.315000    6210.000000  871200.000000  "
            ],
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>price</th>\n",
              "      <th>bedrooms</th>\n",
              "      <th>bathrooms</th>\n",
              "      <th>sqft_living</th>\n",
              "      <th>sqft_lot</th>\n",
              "      <th>floors</th>\n",
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              "      <th>condition</th>\n",
              "      <th>grade</th>\n",
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              "      <th>sqft_basement</th>\n",
              "      <th>yr_built</th>\n",
              "      <th>yr_renovated</th>\n",
              "      <th>zipcode</th>\n",
              "      <th>lat</th>\n",
              "      <th>long</th>\n",
              "      <th>sqft_living15</th>\n",
              "      <th>sqft_lot15</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2.161300e+04</td>\n",
              "      <td>2.161300e+04</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
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              "      <td>2.161300e+04</td>\n",
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              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
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              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.580302e+09</td>\n",
              "      <td>5.400881e+05</td>\n",
              "      <td>3.370842</td>\n",
              "      <td>2.114757</td>\n",
              "      <td>2079.899736</td>\n",
              "      <td>1.510697e+04</td>\n",
              "      <td>1.494309</td>\n",
              "      <td>0.007542</td>\n",
              "      <td>0.234303</td>\n",
              "      <td>3.409430</td>\n",
              "      <td>7.656873</td>\n",
              "      <td>1788.390691</td>\n",
              "      <td>291.509045</td>\n",
              "      <td>1971.005136</td>\n",
              "      <td>84.402258</td>\n",
              "      <td>98077.939805</td>\n",
              "      <td>47.560053</td>\n",
              "      <td>-122.213896</td>\n",
              "      <td>1986.552492</td>\n",
              "      <td>12768.455652</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.876566e+09</td>\n",
              "      <td>3.671272e+05</td>\n",
              "      <td>0.930062</td>\n",
              "      <td>0.770163</td>\n",
              "      <td>918.440897</td>\n",
              "      <td>4.142051e+04</td>\n",
              "      <td>0.539989</td>\n",
              "      <td>0.086517</td>\n",
              "      <td>0.766318</td>\n",
              "      <td>0.650743</td>\n",
              "      <td>1.175459</td>\n",
              "      <td>828.090978</td>\n",
              "      <td>442.575043</td>\n",
              "      <td>29.373411</td>\n",
              "      <td>401.679240</td>\n",
              "      <td>53.505026</td>\n",
              "      <td>0.138564</td>\n",
              "      <td>0.140828</td>\n",
              "      <td>685.391304</td>\n",
              "      <td>27304.179631</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000102e+06</td>\n",
              "      <td>7.500000e+04</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>290.000000</td>\n",
              "      <td>5.200000e+02</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>290.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1900.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>98001.000000</td>\n",
              "      <td>47.155900</td>\n",
              "      <td>-122.519000</td>\n",
              "      <td>399.000000</td>\n",
              "      <td>651.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.123049e+09</td>\n",
              "      <td>3.219500e+05</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.750000</td>\n",
              "      <td>1427.000000</td>\n",
              "      <td>5.040000e+03</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1190.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1951.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>98033.000000</td>\n",
              "      <td>47.471000</td>\n",
              "      <td>-122.328000</td>\n",
              "      <td>1490.000000</td>\n",
              "      <td>5100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.904930e+09</td>\n",
              "      <td>4.500000e+05</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>1910.000000</td>\n",
              "      <td>7.618000e+03</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1560.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1975.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>98065.000000</td>\n",
              "      <td>47.571800</td>\n",
              "      <td>-122.230000</td>\n",
              "      <td>1840.000000</td>\n",
              "      <td>7620.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.308900e+09</td>\n",
              "      <td>6.450000e+05</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>2550.000000</td>\n",
              "      <td>1.068800e+04</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>2210.000000</td>\n",
              "      <td>560.000000</td>\n",
              "      <td>1997.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>98118.000000</td>\n",
              "      <td>47.678000</td>\n",
              "      <td>-122.125000</td>\n",
              "      <td>2360.000000</td>\n",
              "      <td>10083.000000</td>\n",
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              "      <td>9.900000e+09</td>\n",
              "      <td>7.700000e+06</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>13540.000000</td>\n",
              "      <td>1.651359e+06</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>9410.000000</td>\n",
              "      <td>4820.000000</td>\n",
              "      <td>2015.000000</td>\n",
              "      <td>2015.000000</td>\n",
              "      <td>98199.000000</td>\n",
              "      <td>47.777600</td>\n",
              "      <td>-121.315000</td>\n",
              "      <td>6210.000000</td>\n",
              "      <td>871200.000000</td>\n",
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              "      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-808749c9-2799-47cb-8682-da0a69a0179f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **2. Data Checks**"
      ],
      "metadata": {
        "id": "QPL8e4bWmfh5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **2.1 Check for Null Values**"
      ],
      "metadata": {
        "id": "E_gJlLrmmjuG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "HousePrice.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SWVh9VVwCl7O",
        "outputId": "3451834d-cd3f-4115-ec39-2a984756ed21"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id               0\n",
              "date             0\n",
              "price            0\n",
              "bedrooms         0\n",
              "bathrooms        0\n",
              "sqft_living      0\n",
              "sqft_lot         0\n",
              "floors           0\n",
              "waterfront       0\n",
              "view             0\n",
              "condition        0\n",
              "grade            0\n",
              "sqft_above       0\n",
              "sqft_basement    0\n",
              "yr_built         0\n",
              "yr_renovated     0\n",
              "zipcode          0\n",
              "lat              0\n",
              "long             0\n",
              "sqft_living15    0\n",
              "sqft_lot15       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **2.2 Checks for Unique Values**"
      ],
      "metadata": {
        "id": "KrLY06CDm33N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "HousePrice.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "euA5ujNvB6Q6",
        "outputId": "2f7b69a2-e796-4160-f6c9-b56112bb2f1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id               21436\n",
              "date               372\n",
              "price             4028\n",
              "bedrooms            13\n",
              "bathrooms           30\n",
              "sqft_living       1038\n",
              "sqft_lot          9782\n",
              "floors               6\n",
              "waterfront           2\n",
              "view                 5\n",
              "condition            5\n",
              "grade               12\n",
              "sqft_above         946\n",
              "sqft_basement      306\n",
              "yr_built           116\n",
              "yr_renovated        70\n",
              "zipcode             70\n",
              "lat               5034\n",
              "long               752\n",
              "sqft_living15      777\n",
              "sqft_lot15        8689\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **2.3 Check for Duplicated Values**"
      ],
      "metadata": {
        "id": "Ic0vs9TTnIIK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "HousePrice.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iJa8ea5PnEEc",
        "outputId": "f88b765d-2ca8-4e43-f0f7-c742425b7de7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **3. Exploratory Data Analysis**"
      ],
      "metadata": {
        "id": "74imqYPRnXWr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **3.1 Histplot of \"Pricing\"**"
      ],
      "metadata": {
        "id": "vyFAH9fJrpg2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(HousePrice['price'], kde=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "1O6IFbTwQbeE",
        "outputId": "c424ab1b-d515-404e-a76b-6ce4515a492d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='price', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **3.2 Countplot of \"Bedrooms\"**"
      ],
      "metadata": {
        "id": "H6YzNJLcnq1M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(y=HousePrice['bedrooms'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "Vq5-XlhERyc4",
        "outputId": "8a8f23a2-6d85-44d9-86c7-461094d6f47e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='count', ylabel='bedrooms'>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **3.3 Countplot of \"Condition\"**"
      ],
      "metadata": {
        "id": "_yvgfnRJn0Gt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=HousePrice['condition'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "wdOBVMHFbgp6",
        "outputId": "f6d08e82-8a77-438d-b36c-505fe7b74170"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='condition', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **3.4 Catplot of \"floors\"**"
      ],
      "metadata": {
        "id": "1OUPCQJbn5UY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.catplot(data=HousePrice, x=\"floors\", kind=\"count\", palette=\"ch:.50\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "IKmM7a5tESwb",
        "outputId": "bb17d190-76cc-4f71-ab6a-4db71e0c6fa5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7b2022217e50>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **3.5 Histplot of \"Bathrooms\"**"
      ],
      "metadata": {
        "id": "W5djzaApoCw4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(HousePrice, x='bathrooms', element='bars', fill=True, binwidth=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "muY4yoFRULH7",
        "outputId": "10d669a9-013a-4079-b8d2-158ad94ca8de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='bathrooms', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **3.6 Scatterplot of \"square ft\"**"
      ],
      "metadata": {
        "id": "FpoBovhqsOha"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(HousePrice, x='sqft_living', y='price')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "Yrpn0r-_VkYY",
        "outputId": "0395acc2-8f13-4e33-cfd0-5f981004d23c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='sqft_living', ylabel='price'>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**The graph represents the relationship between the number of bedrooms in a house and its average price. This information can be helpful for people who are thinking about buying a house, as it can help them to determine how many bedrooms they need and how much they are willing to spend.**"
      ],
      "metadata": {
        "id": "xryLC_khq6zB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **3.7 Catplot of \"Bedrooms\"**"
      ],
      "metadata": {
        "id": "Q4iBX_6zocM8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.catplot(data=HousePrice, kind=\"bar\", x=\"bedrooms\", y=\"price\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "VSJcjmtHBSsl",
        "outputId": "3ccc8831-e195-447f-b253-7c20c4d6b8ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7b2021a4acb0>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**The demand for houses with more bedrooms is higher than the demand for houses with fewer bedrooms. This is likely because houses with more bedrooms are more suitable for families with children.**\n"
      ],
      "metadata": {
        "id": "htqUdJD6qBrX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **3.8 Histplot of \"floors\" with \"price\"**"
      ],
      "metadata": {
        "id": "2w_S1I-nousg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(data=HousePrice, x='floors', y='price', bins=20, cbar=True, cmap='rocket')"
      ],
      "metadata": {
        "id": "AoBsHSubUWWS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "outputId": "cd6c4127-13be-4033-9feb-30b4f9e5df55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='floors', ylabel='price'>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- These insights suggest that the demand for houses with more floors is higher than the demand for houses with fewer floors. This is likely because houses with more floors are more suitable for families with children."
      ],
      "metadata": {
        "id": "2wr7OLl6gxMZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **3.9 Dealing with Outliers:**"
      ],
      "metadata": {
        "id": "BZqX9EkChq9b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(1, 4, figsize=(16, 4))\n",
        "\n",
        "sns.boxplot(y=HousePrice['bathrooms'], ax=axes[0]).set(xlabel='Bathrooms')\n",
        "sns.boxplot(y=HousePrice['bedrooms'], ax=axes[1]).set(xlabel='Bedrooms')\n",
        "sns.boxplot(y=HousePrice['sqft_living'], ax=axes[2]).set(xlabel='Sqft Living')\n",
        "sns.boxplot(y=HousePrice['sqft_lot'], ax=axes[3]).set(xlabel='Sqft Lot')\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "bW8yx2JXPFa-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "69e2e78e-2b18-44de-f496-ec9f2c29db08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(1, 4, figsize=(16, 4))\n",
        "\n",
        "sns.boxplot(y=HousePrice['sqft_above'], ax=axes[0]).set(xlabel='Sqft above')\n",
        "sns.boxplot(y=HousePrice['price'], ax=axes[1]).set(xlabel='Price')\n",
        "sns.boxplot(y=HousePrice['sqft_living15'], ax=axes[2]).set(xlabel='Sqft living15')\n",
        "sns.boxplot(y=HousePrice['sqft_lot15'], ax=axes[3]).set(xlabel='Sqft lot15')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "o75srBCeKB2e",
        "outputId": "bf4a6d8b-5a7c-483d-cd7e-930432ae084c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Capping Outliers:**"
      ],
      "metadata": {
        "id": "8zQekfE3hioR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "House_price = HousePrice.copy()\n",
        "House_price = House_price.drop(columns=['id', 'date', 'zipcode'],axis=1)\n",
        "Q1 = House_price.quantile(0.25)\n",
        "Q3 = House_price.quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "lower_bounds = Q1 - 1.5 * IQR\n",
        "upper_bounds = Q3 + 1.5 * IQR"
      ],
      "metadata": {
        "id": "kOkPJJEetOyC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "House_price_capped = House_price.copy()\n",
        "for column in House_price.columns:\n",
        "    House_price_capped[column] = np.where(\n",
        "        House_price_capped[column] > upper_bounds[column], upper_bounds[column], House_price_capped[column]\n",
        "    )\n",
        "    House_price_capped[column] = np.where(\n",
        "        House_price_capped[column] < lower_bounds[column], lower_bounds[column], House_price_capped[column]\n",
        "    )\n",
        "\n",
        "House_price_capped.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YufgZJAOR7VW",
        "outputId": "14f22275-7f66-4547-9dac-8912a529858a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(21613, 18)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(1, 4, figsize=(16, 4))\n",
        "\n",
        "sns.boxplot(y=House_price_capped['sqft_above'], ax=axes[0]).set(xlabel='Sqft above')\n",
        "sns.boxplot(y=House_price_capped['price'], ax=axes[1]).set(xlabel='Price')\n",
        "sns.boxplot(y=House_price_capped['sqft_living15'], ax=axes[2]).set(xlabel='Sqft living15')\n",
        "sns.boxplot(y=House_price_capped['sqft_lot15'], ax=axes[3]).set(xlabel='Sqft lot15')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "nE4crfFCKo7p",
        "outputId": "e4029393-9805-4fd4-b8ac-4b55feac1803"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(1, 4, figsize=(16, 4))\n",
        "\n",
        "sns.boxplot(y=House_price_capped['bathrooms'], ax=axes[0]).set(xlabel='Bathrooms')\n",
        "sns.boxplot(y=House_price_capped['bedrooms'], ax=axes[1]).set(xlabel='Bedrooms')\n",
        "sns.boxplot(y=House_price_capped['sqft_living'], ax=axes[2]).set(xlabel='Sqft Living')\n",
        "sns.boxplot(y=House_price_capped['sqft_lot'], ax=axes[3]).set(xlabel='Sqft Lot')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "GL-4OMwqJdMK",
        "outputId": "ee4daab9-e620-440c-f85e-30138bba9d36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **4. Training and Testing Data**"
      ],
      "metadata": {
        "id": "PBqq1SD8O9wU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = House_price_capped.drop('price',axis=1)\n",
        "y = House_price_capped['price']"
      ],
      "metadata": {
        "id": "WJvy5QKQxhdL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vif = pd.DataFrame()\n",
        "vif[\"Variable\"] = X.columns\n",
        "vif[\"VIF\"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
        "print(vif)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kKqtT-Ns60TM",
        "outputId": "4b680b7a-36ed-447d-be4f-9c0e5df44989"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/statsmodels/regression/linear_model.py:1783: RuntimeWarning: invalid value encountered in double_scalars\n",
            "  return 1 - self.ssr/self.uncentered_tss\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "         Variable            VIF\n",
            "0        bedrooms      28.611312\n",
            "1       bathrooms      31.375477\n",
            "2     sqft_living     572.369219\n",
            "3        sqft_lot      24.112687\n",
            "4          floors      19.999506\n",
            "5      waterfront            NaN\n",
            "6            view            NaN\n",
            "7       condition      34.381584\n",
            "8           grade     178.985968\n",
            "9      sqft_above     441.810585\n",
            "10  sqft_basement      28.810188\n",
            "11       yr_built    7828.225074\n",
            "12   yr_renovated            NaN\n",
            "13            lat  119724.891151\n",
            "14           long  131125.958048\n",
            "15  sqft_living15      30.599209\n",
            "16     sqft_lot15      28.355536\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.options.display.float_format = '{:.10f}'.format\n",
        "X_train = sm.add_constant(X)\n",
        "\n",
        "model = sm.OLS(y, X).fit()\n",
        "p_values = model.pvalues\n",
        "p_values_df = pd.DataFrame({\"variable\" : X.columns, \"P-value\" : p_values})\n",
        "\n",
        "print(\"Variable Names and P-values:\")\n",
        "print(p_values_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zi-mTlxd5C-I",
        "outputId": "d10e1f94-b9d3-4e55-d618-206ed51b972a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Variable Names and P-values:\n",
            "                    variable      P-value\n",
            "bedrooms            bedrooms 0.0000000000\n",
            "bathrooms          bathrooms 0.0000000000\n",
            "sqft_living      sqft_living 0.0000000000\n",
            "sqft_lot            sqft_lot 0.0002271235\n",
            "floors                floors 0.0000000000\n",
            "waterfront        waterfront 0.0000000000\n",
            "view                    view 0.0000000000\n",
            "condition          condition 0.0000000000\n",
            "grade                  grade 0.0000000000\n",
            "sqft_above        sqft_above 0.0078250241\n",
            "sqft_basement  sqft_basement 0.2266670007\n",
            "yr_built            yr_built 0.0000000000\n",
            "yr_renovated    yr_renovated 0.0000000000\n",
            "lat                      lat 0.0000000000\n",
            "long                    long 0.0000000000\n",
            "sqft_living15  sqft_living15 0.0000000000\n",
            "sqft_lot15        sqft_lot15 0.0002772832\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.30)"
      ],
      "metadata": {
        "id": "cAKGLLAvqgFy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **5. Model Building:**"
      ],
      "metadata": {
        "id": "-IkkbvBBg6Vf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **5.1 XGBoost Regressor**"
      ],
      "metadata": {
        "id": "0I8D6Q4YfR24"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "xg_model = xgb.XGBRegressor(objective='reg:linear',\n",
        "                            n_estimators=100,\n",
        "                            learning_rate=0.1,\n",
        "                            max_depth=3\n",
        "                          )\n",
        "\n",
        "xg_model.fit(X_train, y_train)\n",
        "\n",
        "y_pred_test = xg_model.predict(X_test)\n",
        "mae_test = mean_absolute_error(y_test, y_pred_test)\n",
        "r2_test = r2_score(y_test, y_pred_test)\n",
        "\n",
        "print(\"\\nTesting Data Metrics: XG-Boost : \")\n",
        "print(f\"Mean Absolute Error (MAE): {mae_test:.2f}\")\n",
        "print(f\"R-squared (R2) Score: {r2_test:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dgsIQ4LyfPv8",
        "outputId": "398f7f52-d7a7-4c9d-f0da-3fb2deac6d8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Testing Data Metrics: XG-Boost : \n",
            "Mean Absolute Error (MAE): 65176.82\n",
            "R-squared (R2) Score: 0.86\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:160: UserWarning: [08:27:10] WARNING: /workspace/src/objective/regression_obj.cu:209: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "  warnings.warn(smsg, UserWarning)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **5.2 Linear Regression**"
      ],
      "metadata": {
        "id": "n0AUQYV2sS99"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "linear_model = LinearRegression()\n",
        "linear_model.fit(X_train, y_train)\n",
        "\n",
        "y_pred_test = linear_model.predict(X_test)\n",
        "mae_test = mean_absolute_error(y_test, y_pred_test)\n",
        "r2_test = r2_score(y_test, y_pred_test)\n",
        "\n",
        "print(\"\\nTesting Data Metrics: Linear Regression : \")\n",
        "print(f\"Mean Absolute Error (MAE): {mae_test:.2f}\")\n",
        "print(f\"R-squared (R2) Score: {r2_test:.2f}\")"
      ],
      "metadata": {
        "id": "BoZmw5qG5EyH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1403ce2f-2f54-408d-8f83-2e75de38f9f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Testing Data Metrics: Linear Regression : \n",
            "Mean Absolute Error (MAE): 97722.47\n",
            "R-squared (R2) Score: 0.73\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **5.3 Gradient Boosting Regressor**"
      ],
      "metadata": {
        "id": "giODxIdluJnO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gb_regressor = GradientBoostingRegressor(\n",
        "    n_estimators=100,\n",
        "    learning_rate=0.1,\n",
        "    max_depth=5,\n",
        "    random_state=100,\n",
        "    min_samples_leaf=3\n",
        ")\n",
        "\n",
        "gb_regressor.fit(X_train, y_train)\n",
        "\n",
        "y_pred_test_gb = gb_regressor.predict(X_test)\n",
        "mae_gb_test = mean_absolute_error(y_test, y_pred_test_gb)\n",
        "r2_gb_test = r2_score(y_test, y_pred_test_gb)\n",
        "\n",
        "print(\"Test Data Metrics : Gradient Boosting Regression : \")\n",
        "print(f\"Mean Absolute Error (MAE): {mae_gb_test:.2f}\")\n",
        "print(f\"R-squared (R2) Score: {r2_gb_test:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R63LWFQrWSL-",
        "outputId": "387ad468-1472-407d-d1db-601298411203"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Data Metrics : Gradient Boosting Regression : \n",
            "Mean Absolute Error (MAE): 57251.31\n",
            "R-squared (R2) Score: 0.89\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **5.4 Random Forest Regressor**"
      ],
      "metadata": {
        "id": "yYEDm34N8cyl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rfc = RandomForestRegressor(max_depth=7, random_state=120, min_samples_split=5, min_samples_leaf=3)\n",
        "rfc.fit(X_train, y_train)\n",
        "\n",
        "y_pred_test = rfc.predict(X_test)\n",
        "mae_test = mean_absolute_error(y_test, y_pred_test)\n",
        "r2_test = r2_score(y_test, y_pred_test)\n",
        "\n",
        "print(\"Testing Data Metrics : RandomForest Regressor : \")\n",
        "print(f\"Mean Absolute Error (MAE): {mae_test:.2f}\")\n",
        "print(f\"R-squared (R2) Score: {r2_test:.2f}\")"
      ],
      "metadata": {
        "id": "I24MTR5d8biO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "99de1319-08f0-4303-f65f-c0fb174797af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Testing Data Metrics : RandomForest Regressor : \n",
            "Mean Absolute Error (MAE): 70216.15\n",
            "R-squared (R2) Score: 0.84\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**The Gradient Boosting Regression model seems to be performing the best, closely followed by XG-Boost. These models provide the comparatively lower mean absolute errors and the highest R2 scores, indicating their ability to predict house prices effectively.**"
      ],
      "metadata": {
        "id": "P-3G15jM2ECA"
      }
    }
  ]
}