{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "TcS8IwGsSljs"
      },
      "outputs": [],
      "source": [
        "# Basic Example: Predicting House Price based on Size\n",
        "\n",
        "# Step 1: Import necessary libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 2: Generate synthetic data (for illustration purposes)\n",
        "np.random.seed(42)\n",
        "house_size = np.random.randint(1000, 3000, 50)  # House sizes between 1000 and 3000 sq. ft.\n",
        "house_price = 50 + 20 * house_size + np.random.normal(0, 20000, 50)  # Price with some noise"
      ],
      "metadata": {
        "id": "bFktNCZrT-X3"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 3: Create a DataFrame\n",
        "df = pd.DataFrame({'HouseSize': house_size, 'HousePrice': house_price})"
      ],
      "metadata": {
        "id": "DdeDvjPOWs1w"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 4: Visualize the data\n",
        "plt.scatter(df['HouseSize'], df['HousePrice'])\n",
        "plt.xlabel('House Size (sq. ft.)')\n",
        "plt.ylabel('House Price')\n",
        "plt.title('Synthetic Data: House Size vs. House Price')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Yrz3L5EuW6WC",
        "outputId": "93d5b715-9dac-48a5-87ab-de884e3bfd32"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 5: Split the data into training and testing sets\n",
        "X = df['HouseSize'].values.reshape(-1, 1)\n",
        "y = df['HousePrice'].values\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "fJfKN5z8XNeR"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 6: Fit the linear regression model on training data\n",
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "vQ1vENPqXS0d",
        "outputId": "009f3882-39a9-4377-f543-7a02dc698e35"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 7: Make predictions on test data\n",
        "y_pred = model.predict(X_test)"
      ],
      "metadata": {
        "id": "n928sThyXVu2"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 8: Visualize the regression line\n",
        "plt.scatter(X_test, y_test, label='Actual Data')\n",
        "plt.plot(X_test, y_pred, 'r-', label='Regression Line')\n",
        "plt.xlabel('House Size (sq. ft.)')\n",
        "plt.ylabel('House Price')\n",
        "plt.title('Linear Regression: House Size vs. House Price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "3VkNkkTHXe2h",
        "outputId": "4f0ee431-c30b-4699-cd6c-56d4d45e6feb"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 9: Print the coefficients and equation\n",
        "slope = model.coef_[0]\n",
        "intercept = model.intercept_\n",
        "equation = f'House Price = {intercept:.2f} + {slope:.2f} * House Size'\n",
        "print(f'Equation of the regression line: {equation}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yKT9k7hVXn-S",
        "outputId": "6fa20e0a-c46a-48ca-aacb-068fa71e4617"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Equation of the regression line: House Price = 15277.74 + 12.52 * House Size\n"
          ]
        }
      ]
    }
  ]
}