Price_of_houses_prediction.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Amishachoudhary22/AI-ML/blob/main/Price_of_houses_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vN99YjPTDena"
      },
      "source": [
        "# Linear Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZIx_naXnDyHd"
      },
      "source": [
        "## Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FjnmdyPLD2tS"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6c8YExmOD5x5"
      },
      "source": [
        "## Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nQOdXhjXD_AE"
      },
      "source": [
        "dataset = pd.read_csv('train.csv')\n",
        "test_data = pd.read_csv('test.csv')\n",
        "\n",
        "X = dataset.iloc[:, 1:-1].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5bYpBznnuPwl",
        "outputId": "a3f8294c-d0be-4b1e-f099-6d7b38e2583b"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
            "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
            "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
            "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
            "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
            "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
            "\n",
            "  LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n",
            "0         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
            "1         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      5   \n",
            "2         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      9   \n",
            "3         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
            "4         Lvl    AllPub  ...        0    NaN   NaN         NaN       0     12   \n",
            "\n",
            "  YrSold  SaleType  SaleCondition  SalePrice  \n",
            "0   2008        WD         Normal     208500  \n",
            "1   2007        WD         Normal     181500  \n",
            "2   2008        WD         Normal     223500  \n",
            "3   2006        WD        Abnorml     140000  \n",
            "4   2008        WD         Normal     250000  \n",
            "\n",
            "[5 rows x 81 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Le8SEL-YEOLb"
      },
      "source": [
        "## Training the Linear Regression model on the whole dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Select relevant features and target\n",
        "features = ['LotArea', 'OverallQual', 'YearBuilt', 'GrLivArea', 'FullBath', 'GarageCars', 'GarageArea', 'YrSold']\n",
        "target = 'SalePrice'\n",
        "\n",
        "X = dataset[features]\n",
        "y = dataset[target]\n"
      ],
      "metadata": {
        "id": "Oh-ZVg_puY81"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
      ],
      "metadata": {
        "id": "5hEgm0INvEx0"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
        "from sklearn.pipeline import Pipeline\n",
        "numeric_features = X.select_dtypes(include=['int64', 'float64']).columns\n",
        "categorical_features = X.select_dtypes(include=['object']).columns\n",
        "\n",
        "# Create preprocessing pipeline\n",
        "numeric_transformer = Pipeline(steps=[\n",
        "    ('scaler', StandardScaler())\n",
        "])\n",
        "\n",
        "categorical_transformer = Pipeline(steps=[\n",
        "    ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
        "])\n",
        "\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('num', numeric_transformer, numeric_features),\n",
        "        ('cat', categorical_transformer, categorical_features)\n",
        "    ])\n"
      ],
      "metadata": {
        "id": "oHfq5VM3vIhZ"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Append linear regression model to preprocessing pipeline\n",
        "model = Pipeline(steps=[('preprocessor', preprocessor),\n",
        "                        ('regressor', LinearRegression())])\n",
        "\n",
        "# Train the model\n",
        "model.fit(X_train, y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 188
        },
        "id": "O8qjUj2PvV9x",
        "outputId": "c09f60bd-fdb2-43db-e555-07f7da4b9eef"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('preprocessor',\n",
              "                 ColumnTransformer(transformers=[('num',\n",
              "                                                  Pipeline(steps=[('scaler',\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  Index(['LotArea', 'OverallQual', 'YearBuilt', 'GrLivArea', 'FullBath',\n",
              "       'GarageCars', 'GarageArea', 'YrSold'],\n",
              "      dtype='object')),\n",
              "                                                 ('cat',\n",
              "                                                  Pipeline(steps=[('onehot',\n",
              "                                                                   OneHotEncoder(handle_unknown='ignore'))]),\n",
              "                                                  Index([], dtype='object'))])),\n",
              "                ('regressor', LinearRegression())])"
            ],
            "text/html": [
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              "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;scaler&#x27;,\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  Index([&#x27;LotArea&#x27;, &#x27;OverallQual&#x27;, &#x27;YearBuilt&#x27;, &#x27;GrLivArea&#x27;, &#x27;FullBath&#x27;,\n",
              "       &#x27;GarageCars&#x27;, &#x27;GarageArea&#x27;, &#x27;YrSold&#x27;],\n",
              "      dtype=&#x27;object&#x27;)),\n",
              "                                                 (&#x27;cat&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;onehot&#x27;,\n",
              "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                                  Index([], dtype=&#x27;object&#x27;))])),\n",
              "                (&#x27;regressor&#x27;, LinearRegression())])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;scaler&#x27;,\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  Index([&#x27;LotArea&#x27;, &#x27;OverallQual&#x27;, &#x27;YearBuilt&#x27;, &#x27;GrLivArea&#x27;, &#x27;FullBath&#x27;,\n",
              "       &#x27;GarageCars&#x27;, &#x27;GarageArea&#x27;, &#x27;YrSold&#x27;],\n",
              "      dtype=&#x27;object&#x27;)),\n",
              "                                                 (&#x27;cat&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;onehot&#x27;,\n",
              "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                                  Index([], dtype=&#x27;object&#x27;))])),\n",
              "                (&#x27;regressor&#x27;, LinearRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler())]),\n",
              "                                 Index([&#x27;LotArea&#x27;, &#x27;OverallQual&#x27;, &#x27;YearBuilt&#x27;, &#x27;GrLivArea&#x27;, &#x27;FullBath&#x27;,\n",
              "       &#x27;GarageCars&#x27;, &#x27;GarageArea&#x27;, &#x27;YrSold&#x27;],\n",
              "      dtype=&#x27;object&#x27;)),\n",
              "                                (&#x27;cat&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;onehot&#x27;,\n",
              "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                 Index([], dtype=&#x27;object&#x27;))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;LotArea&#x27;, &#x27;OverallQual&#x27;, &#x27;YearBuilt&#x27;, &#x27;GrLivArea&#x27;, &#x27;FullBath&#x27;,\n",
              "       &#x27;GarageCars&#x27;, &#x27;GarageArea&#x27;, &#x27;YrSold&#x27;],\n",
              "      dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>Index([], dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Make Predictions\n"
      ],
      "metadata": {
        "id": "qCuM7npEycOp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions on the test set\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "print(f\"Mean Squared Error on test data: {mse:.2f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UZ9gZHJtvcID",
        "outputId": "cb92383c-8416-42fa-f935-f0cfd9d821ec"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error on test data: 1726999818.45\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Plotting the linear regresion graph"
      ],
      "metadata": {
        "id": "35q2JoMpyiPp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract the numerical feature for plotting against SalePrice\n",
        "feature_for_plot = 'GrLivArea'\n",
        "\n",
        "# Plotting the linear regression line\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.scatter(X_train[feature_for_plot], y_train, color='blue', label='Training data')\n",
        "plt.scatter(X_test[feature_for_plot], y_test, color='green', label='Testing data')\n",
        "plt.plot(X_train[feature_for_plot], model.predict(X_train), color='red', linewidth=2, label='Linear regression')\n",
        "plt.xlabel(feature_for_plot)\n",
        "plt.ylabel('SalePrice')\n",
        "plt.title('Linear Regression - Predicting House Prices')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "iTTY66kZvxen",
        "outputId": "b2cd97fe-fb0e-49ce-b5f3-7495f496a6fa"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Predicting Prices for a new house"
      ],
      "metadata": {
        "id": "G4SFP93byqPm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example prediction for a new house\n",
        "new_house = pd.DataFrame([[10000, 8, 2010, 2000, 3, 2, 500, 2023]], columns=features)\n",
        "predicted_price = model.predict(new_house)\n",
        "\n",
        "print(f\"Predicted price for the new house: ${predicted_price[0]:,.2f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tkC-CnNmwQM7",
        "outputId": "b15b42c6-ca96-4c79-fed0-d2e9cc0035a3"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted price for the new house: $270,127.25\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Example cases for prediction\n",
        "cases = [\n",
        "    {'LotArea': 10000, 'OverallQual': 8, 'YearBuilt': 2010, 'GrLivArea': 2000, 'FullBath': 3, 'GarageCars': 2, 'GarageArea': 500, 'YrSold': 2023},\n",
        "    {'LotArea': 12000, 'OverallQual': 7, 'YearBuilt': 2005, 'GrLivArea': 1800, 'FullBath': 2, 'GarageCars': 2, 'GarageArea': 480, 'YrSold': 2022},\n",
        "    {'LotArea': 8000, 'OverallQual': 6, 'YearBuilt': 1995, 'GrLivArea': 1600, 'FullBath': 2, 'GarageCars': 1, 'GarageArea': 400, 'YrSold': 2024},\n",
        "    {'LotArea': 15000, 'OverallQual': 9, 'YearBuilt': 2015, 'GrLivArea': 2500, 'FullBath': 3, 'GarageCars': 3, 'GarageArea': 600, 'YrSold': 2021}\n",
        "]\n",
        "\n",
        "# Convert the cases into a DataFrame\n",
        "cases_df = pd.DataFrame(cases)\n",
        "\n",
        "# Predict house prices for each case using the trained model\n",
        "predicted_prices = model.predict(cases_df)\n",
        "\n",
        "# Print the predicted prices for each case\n",
        "for i, price in enumerate(predicted_prices):\n",
        "    print(f\"Predicted price for case {i+1}: ${price:,.2f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q3JE4rn_wyGh",
        "outputId": "85319076-8008-4f8b-8309-1c70048ea9f1"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted price for case 1: $270,127.25\n",
            "Predicted price for case 2: $241,670.16\n",
            "Predicted price for case 3: $190,860.63\n",
            "Predicted price for case 4: $335,808.96\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Finding Accuracy"
      ],
      "metadata": {
        "id": "J_SiUMmJyxPs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "import numpy as np\n",
        "\n",
        "# Predict house prices for each case using the trained model\n",
        "predicted_prices = model.predict(cases_df)\n",
        "\n",
        "# Create a dummy y_test with the same number of samples as predicted_prices\n",
        "y_test_dummy = np.zeros_like(predicted_prices)  # Creating dummy zeros\n",
        "\n",
        "# Compute RMSE\n",
        "rmse = np.sqrt(mean_squared_error(y_test_dummy, predicted_prices))\n",
        "print(f\"Root Mean Squared Error (RMSE) for predicted cases: {rmse:.2f}\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dbHJFv5KxI7a",
        "outputId": "4815e9bc-bc16-43aa-ed69-05b47351980c"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Root Mean Squared Error (RMSE) for predicted cases: 264843.65\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "\n",
        "# Predict house prices for test data using the trained model\n",
        "predicted_prices_test = model.predict(X_test)\n",
        "\n",
        "# Compute R-squared for test data\n",
        "r2_test = r2_score(y_test, predicted_prices_test)\n",
        "accuracy_percentage_test = r2_test * 100\n",
        "print(f\"Accuracy (R-squared) for test data: {accuracy_percentage_test:.2f}%\")\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cDcwNG-fxoWo",
        "outputId": "45362b88-e9b3-4de7-ac1e-435662bb8167"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy (R-squared) for test data: 77.48%\n"
          ]
        }
      ]
    }
  ]
}