House_Price_Prediction_&_EDA_.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dO4qxpTskfdM"
      },
      "source": [
        "## **HOUSE PRICE PREDICTION AND EXPLORATORY DATA ANALYSIS**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wgmVh83RktAv"
      },
      "source": [
        "**IMPORTING NECESSARY LIBRARIES**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "metadata": {
        "id": "Fafqba-Okp0i"
      },
      "outputs": [],
      "source": [
        "#FOR EDA AND VISUALISATION\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "#FOR MACHINE LEARNING\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.metrics import mean_absolute_percentage_error\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import svm\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.linear_model import LinearRegression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 111,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "BgWjOUnEtaZa",
        "outputId": "28245ff5-941c-4092-c3e6-95d9d492a2a9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Id  MSSubClass MSZoning  LotArea LotConfig BldgType  OverallCond  \\\n",
              "0   0          60       RL     8450    Inside     1Fam            5   \n",
              "1   1          20       RL     9600       FR2     1Fam            8   \n",
              "2   2          60       RL    11250    Inside     1Fam            5   \n",
              "3   3          70       RL     9550    Corner     1Fam            5   \n",
              "4   4          60       RL    14260       FR2     1Fam            5   \n",
              "5   5          50       RL    14115    Inside     1Fam            5   \n",
              "6   6          20       RL    10084    Inside     1Fam            5   \n",
              "7   7          60       RL    10382    Corner     1Fam            6   \n",
              "8   8          50       RM     6120    Inside     1Fam            5   \n",
              "9   9         190       RL     7420    Corner   2fmCon            6   \n",
              "\n",
              "   YearBuilt  YearRemodAdd Exterior1st  BsmtFinSF2  TotalBsmtSF  SalePrice  \n",
              "0       2003          2003     VinylSd         0.0        856.0   208500.0  \n",
              "1       1976          1976     MetalSd         0.0       1262.0   181500.0  \n",
              "2       2001          2002     VinylSd         0.0        920.0   223500.0  \n",
              "3       1915          1970     Wd Sdng         0.0        756.0   140000.0  \n",
              "4       2000          2000     VinylSd         0.0       1145.0   250000.0  \n",
              "5       1993          1995     VinylSd         0.0        796.0   143000.0  \n",
              "6       2004          2005     VinylSd         0.0       1686.0   307000.0  \n",
              "7       1973          1973     HdBoard        32.0       1107.0   200000.0  \n",
              "8       1931          1950     BrkFace         0.0        952.0   129900.0  \n",
              "9       1939          1950     MetalSd         0.0        991.0   118000.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3c6f5589-aa34-4930-b87d-b146d075ea48\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>LotConfig</th>\n",
              "      <th>BldgType</th>\n",
              "      <th>OverallCond</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>YearRemodAdd</th>\n",
              "      <th>Exterior1st</th>\n",
              "      <th>BsmtFinSF2</th>\n",
              "      <th>TotalBsmtSF</th>\n",
              "      <th>SalePrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>8450</td>\n",
              "      <td>Inside</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>5</td>\n",
              "      <td>2003</td>\n",
              "      <td>2003</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>0.0</td>\n",
              "      <td>856.0</td>\n",
              "      <td>208500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>RL</td>\n",
              "      <td>9600</td>\n",
              "      <td>FR2</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>8</td>\n",
              "      <td>1976</td>\n",
              "      <td>1976</td>\n",
              "      <td>MetalSd</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1262.0</td>\n",
              "      <td>181500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>11250</td>\n",
              "      <td>Inside</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>5</td>\n",
              "      <td>2001</td>\n",
              "      <td>2002</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>0.0</td>\n",
              "      <td>920.0</td>\n",
              "      <td>223500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>70</td>\n",
              "      <td>RL</td>\n",
              "      <td>9550</td>\n",
              "      <td>Corner</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>5</td>\n",
              "      <td>1915</td>\n",
              "      <td>1970</td>\n",
              "      <td>Wd Sdng</td>\n",
              "      <td>0.0</td>\n",
              "      <td>756.0</td>\n",
              "      <td>140000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>14260</td>\n",
              "      <td>FR2</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>5</td>\n",
              "      <td>2000</td>\n",
              "      <td>2000</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1145.0</td>\n",
              "      <td>250000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>50</td>\n",
              "      <td>RL</td>\n",
              "      <td>14115</td>\n",
              "      <td>Inside</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>5</td>\n",
              "      <td>1993</td>\n",
              "      <td>1995</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>0.0</td>\n",
              "      <td>796.0</td>\n",
              "      <td>143000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>20</td>\n",
              "      <td>RL</td>\n",
              "      <td>10084</td>\n",
              "      <td>Inside</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>5</td>\n",
              "      <td>2004</td>\n",
              "      <td>2005</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1686.0</td>\n",
              "      <td>307000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>10382</td>\n",
              "      <td>Corner</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>6</td>\n",
              "      <td>1973</td>\n",
              "      <td>1973</td>\n",
              "      <td>HdBoard</td>\n",
              "      <td>32.0</td>\n",
              "      <td>1107.0</td>\n",
              "      <td>200000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>50</td>\n",
              "      <td>RM</td>\n",
              "      <td>6120</td>\n",
              "      <td>Inside</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>5</td>\n",
              "      <td>1931</td>\n",
              "      <td>1950</td>\n",
              "      <td>BrkFace</td>\n",
              "      <td>0.0</td>\n",
              "      <td>952.0</td>\n",
              "      <td>129900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "      <td>190</td>\n",
              "      <td>RL</td>\n",
              "      <td>7420</td>\n",
              "      <td>Corner</td>\n",
              "      <td>2fmCon</td>\n",
              "      <td>6</td>\n",
              "      <td>1939</td>\n",
              "      <td>1950</td>\n",
              "      <td>MetalSd</td>\n",
              "      <td>0.0</td>\n",
              "      <td>991.0</td>\n",
              "      <td>118000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3c6f5589-aa34-4930-b87d-b146d075ea48')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3c6f5589-aa34-4930-b87d-b146d075ea48 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3c6f5589-aa34-4930-b87d-b146d075ea48');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ],
      "source": [
        "df = pd.read_csv('/content/HousePricePrediction.csv')\n",
        "df\n",
        "df.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 112,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gyMdTlWMtoQ-",
        "outputId": "cec197f7-8156-4658-c004-f3a792b408f1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2919, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6YYyaZv4xUQa"
      },
      "source": [
        "### **DATA PREPROCESSING**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 113,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ut61WGefw4tD",
        "outputId": "fc57d72f-e56b-4fb2-928e-9910346250db"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "categorical variables 4\n"
          ]
        }
      ],
      "source": [
        "obj = (df.dtypes =='object')\n",
        "object_cols = list(obj[obj].index)\n",
        "print('categorical variables',len(object_cols))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9oPY6VXVzBY9",
        "outputId": "be36f188-66a3-4935-8ce5-f4ce8b894092"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Integers variables 6\n"
          ]
        }
      ],
      "source": [
        "Int = (df.dtypes == 'int')\n",
        "Int_cols = list(Int[Int].index)\n",
        "print('Integers variables', len(Int_cols))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 115,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9fa4odl_2iCl",
        "outputId": "2ba6b014-857d-411f-cde4-78378c29ff99"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Float Variables 3\n"
          ]
        }
      ],
      "source": [
        "Float = (df.dtypes == 'float')\n",
        "Float_cols = list(Float[Float].index)\n",
        "print('Float Variables',len(Float_cols))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s2Om90cu29Uj"
      },
      "source": [
        "### **EXPLORATORY DATA ANALYSIS**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 116,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "id": "WPfRoP2L25QO",
        "outputId": "5d6e7709-72f7-4cb5-8766-2efbc4ed4176"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'The code outputs the resulting heatmap, which shows the correlation between each pair of variables in the dataset.\\nDarker colors indicate stronger positive correlations, while lighter colors indicate weaker or negative correlations.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 116
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 936x504 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(13,7))\n",
        "sns.heatmap(df.corr(),\n",
        "            cmap = 'BrBG',\n",
        "            fmt = '.2f',\n",
        "            linewidths = 2,\n",
        "            annot = True)\n",
        "\n",
        "'''The code outputs the resulting heatmap, which shows the correlation between each pair of variables in the dataset.\n",
        "Darker colors indicate stronger positive correlations, while lighter colors indicate weaker or negative correlations.'''"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 117,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "EqpgpGiy78GI",
        "outputId": "bfaad90f-84b1-41da-f760-cb7301bf979f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'NO. OF UNIQUE VALUES IN CATEGORICAL VARIABLES'}>"
            ]
          },
          "metadata": {},
          "execution_count": 117
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "unique_values = []\n",
        "for col in object_cols:\n",
        "  unique_values.append(df[col].unique().size)\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.title(\"NO. OF UNIQUE VALUES IN CATEGORICAL VARIABLES\")\n",
        "plt.xticks(rotation = 15)\n",
        "sns.barplot(x=object_cols,y=unique_values) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 118,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "J1L9dB3R-2-i",
        "outputId": "9c983d43-0262-4f4b-8ef0-5b25fcfd09e9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'NO. OF UNIQUE VALUES IN INTEGER VARIABLES'}>"
            ]
          },
          "metadata": {},
          "execution_count": 118
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "unique_values = []\n",
        "for col in Int_cols:\n",
        "  unique_values.append(df[col].unique().size)\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.title(\"NO. OF UNIQUE VALUES IN INTEGER VARIABLES\")\n",
        "plt.xticks(rotation = 15)\n",
        "sns.lineplot(x=Int_cols,y=unique_values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 119,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "UPefA1Cw_24u",
        "outputId": "b1617c79-0e04-410f-be42-a08a3ff77aad"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'NO. OF UNIQUE VALUES IN FLOAT VARIABLES'}>"
            ]
          },
          "metadata": {},
          "execution_count": 119
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "unique_values = []\n",
        "for col in Float_cols:\n",
        "  unique_values.append(df[col].unique().size)\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.title(\"NO. OF UNIQUE VALUES IN FLOAT VARIABLES\")\n",
        "plt.xticks(rotation = 15)\n",
        "sns.lineplot(x=Float_cols,y=unique_values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 120,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        },
        "id": "fwDhMtfEDMMl",
        "outputId": "c8be0b94-a8b2-4cfb-a4ba-9ea59c7e0afc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-120-3c706f13d12e>:8: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
            "  ax.set_xticklabels(y.index, rotation=90)\n",
            "<ipython-input-120-3c706f13d12e>:8: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
            "  ax.set_xticklabels(y.index, rotation=90)\n",
            "<ipython-input-120-3c706f13d12e>:8: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
            "  ax.set_xticklabels(y.index, rotation=90)\n",
            "<ipython-input-120-3c706f13d12e>:8: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
            "  ax.set_xticklabels(y.index, rotation=90)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2160x6192 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "fig = plt.figure(figsize=(30, 86))\n",
        "fig.suptitle('Categorical Features: Distribution')\n",
        "index = 1\n",
        "\n",
        "for col in object_cols:\n",
        "    y = df[col].value_counts()\n",
        "    ax = fig.add_subplot(11, 4, index)\n",
        "    ax.set_xticklabels(y.index, rotation=90)\n",
        "    sns.barplot(x=y.index, y=y, ax=ax)\n",
        "    index += 1\n",
        "    ax.set_title(col)\n",
        "    \n",
        "    # Explicitly remove overlapping axes\n",
        "    for ax in fig.axes:\n",
        "        plt.sca(ax)\n",
        "        plt.xticks(rotation=90)\n",
        "        plt.tight_layout()\n",
        "        plt.subplots_adjust(top=0.95)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **DATA CLEANING**"
      ],
      "metadata": {
        "id": "mPVdKOYMa-P0"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 121,
      "metadata": {
        "id": "MBnp-iW9DfGl"
      },
      "outputs": [],
      "source": [
        "df['SalePrice'] = df['SalePrice'].fillna(\n",
        "    df['SalePrice'].mean()\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df = df.dropna()\n",
        "new_df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F4NTMB1jcPeK",
        "outputId": "b7bddf09-2c5e-4906-e0e6-8d6f383a8934"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id              0\n",
              "MSSubClass      0\n",
              "MSZoning        0\n",
              "LotArea         0\n",
              "LotConfig       0\n",
              "BldgType        0\n",
              "OverallCond     0\n",
              "YearBuilt       0\n",
              "YearRemodAdd    0\n",
              "Exterior1st     0\n",
              "BsmtFinSF2      0\n",
              "TotalBsmtSF     0\n",
              "SalePrice       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **ONE HOT ENCODER - FOR LABEL CATEGORICAL FEATURES AND SPLITTING DATASET INTO TRAINING AND TESTING**"
      ],
      "metadata": {
        "id": "OyK7j6ydfpUc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "s = (df.dtypes == 'object')\n",
        "object_cols = list(s[s].index)\n",
        "print('catogorical Variables: ')\n",
        "print(object_cols)\n",
        "print('no.of catogorical Variables:',len(object_cols))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "auuFnhIZf6oK",
        "outputId": "3182f479-71bf-4e76-f844-8f963d17d074"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "catogorical Variables: \n",
            "['MSZoning', 'LotConfig', 'BldgType', 'Exterior1st']\n",
            "no.of catogorical Variables: 4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "one_hot_encoder = OneHotEncoder(sparse_output = False)\n",
        "one_hot_cols = pd.DataFrame(one_hot_encoder.fit_transform(new_df[object_cols]))\n",
        "one_hot_cols.index = new_df.index\n",
        "one_hot_columns = one_hot_encoder.get_feature_names_out()\n",
        "df_final = new_df.drop(object_cols, axis =1)\n",
        "df_final = pd.concat([df_final,one_hot_cols],axis =1)"
      ],
      "metadata": {
        "id": "RpSSOZulgaPR"
      },
      "execution_count": 124,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "'''Now splitting data into Training and Testing'''\n",
        "X = df_final.drop(['SalePrice'], axis = 1)\n",
        "Y = df_final['SalePrice']\n",
        "X.columns = X.columns.astype(str)\n",
        "\n",
        "\n",
        "#splitting the training set into\n",
        "#training and validation set\n",
        "X_train, X_valid,Y_train,Y_valid = train_test_split(\n",
        "    X,Y,train_size = 0.8,test_size = 0.2,random_state = 0\n",
        ")\n"
      ],
      "metadata": {
        "id": "kkr-GpPepC-G"
      },
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **MODEL AND ACCURACY**\n",
        "SVM,RANDOM FOREST REGRESSION, LINEAR REGRESSION"
      ],
      "metadata": {
        "id": "jjFqsRWsueds"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **SUPPORT VECTOR MACHINE**"
      ],
      "metadata": {
        "id": "V5RT0U58utUx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "model_SVR = svm.SVR()\n",
        "model_SVR.fit(X_train, Y_train)\n",
        "\n",
        "# Make predictions\n",
        "Y_pred = model_SVR.predict(X_valid)\n",
        "\n",
        "# Evaluate the model\n",
        "mspe = mean_absolute_percentage_error(Y_valid, Y_pred)\n",
        "print('Mean absolute percentage error:', mspe)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m-73PXcPt7uV",
        "outputId": "8029b9ee-277f-45a2-ec2d-8e6441d21263"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean absolute percentage error: 0.18704778826125987\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **RANDOM FOREST REGRESSION**"
      ],
      "metadata": {
        "id": "yJjj88-AOdqa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "model_RFR = RandomForestRegressor(n_estimators=10)\n",
        "model_RFR.fit(X_train, Y_train)\n",
        "\n",
        "# Make predictions\n",
        "Y_pred = model_RFR.predict(X_valid)\n",
        "\n",
        "# Evaluate the model\n",
        "mspe = mean_absolute_percentage_error(Y_valid, Y_pred)\n",
        "print('Mean absolute percentage error:', mspe)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XrRFryDgxGEd",
        "outputId": "95b6a824-7ae1-46ec-c32e-3d1110453d33"
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean absolute percentage error: 0.08014337920038953\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **LINEAR REGRESSION**"
      ],
      "metadata": {
        "id": "GsiTYbgOPGkC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "model_LR = LinearRegression()\n",
        "model_LR.fit(X_train, Y_train)\n",
        "\n",
        "# Make predictions\n",
        "Y_pred = model_LR.predict(X_valid)\n",
        "\n",
        "# Evaluate the model\n",
        "mspe = mean_absolute_percentage_error(Y_valid, Y_pred)\n",
        "print('Mean absolute percentage error:', mspe)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SytsXQyZPKxQ",
        "outputId": "91ad7215-b1c9-46f5-89fd-3f9b9296bb6b"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean absolute percentage error: 0.18633155158087458\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **CONCLUDING:**\n",
        "**Clearly, SVM model is giving better accuracy as the mean absolute error is the least among all the other regressor models i.e. 0.18 approx. To get much better results ensemble learning techniques like Bagging and Boosting can also be used.**"
      ],
      "metadata": {
        "id": "EBkdoABWQ0Cg"
      }
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}