linear_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "# Example dataset (substitute with real-world data if available)\n",
    "data = {\n",
    "    \"SquareFeet\": [850, 900, 1000, 1100, 1200, 1250, 1300, 1400, 1500],\n",
    "    \"Price\": [200000, 210000, 230000, 240000, 260000, 270000, 280000, 300000, 320000]\n",
    "}\n",
    "df = pd.DataFrame(data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pandas: For data manipulation.\n",
    "\n",
    "numpy: For numerical operations.\n",
    "\n",
    "matplotlib: For visualizing data.\n",
    "\n",
    "scikit-learn: For machine learning (model training and evaluation).\n",
    "\n",
    "The data is loaded into a DataFrame (a tabular structure) for analysis. Each row represents a data point, and each column represents a variable (SquareFootage and Price)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot of the data\n",
    "plt.scatter(df['SquareFeet'], df['Price'], color='blue')\n",
    "plt.title(\"House Prices vs. Square Footage\")\n",
    "plt.xlabel(\"Square Footage\")\n",
    "plt.ylabel(\"Price\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A scatter plot is created to visualize the relationship between square footage and price. This helps to see if there is a linear trend in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Features and target variable\n",
    "X = df[['SquareFeet']]  # Independent variable\n",
    "y = df['Price']  # Dependent variable\n",
    "\n",
    "# Train-test split (80% train, 20% test)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset is divided into training (80%) and testing (20%) sets using train_test_split.\n",
    "\n",
    "Training set: Used to train the model.\n",
    "\n",
    "Testing set: Used to evaluate the model's performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: 44297.082228116866\n",
      "Coefficient: 181.69761273209537\n"
     ]
    }
   ],
   "source": [
    "# Initialize the Linear Regression model\n",
    "model = LinearRegression()\n",
    "\n",
    "# Train the model on the training data\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Print the model's parameters\n",
    "print(\"Intercept:\", model.intercept_)\n",
    "print(\"Coefficient:\", model.coef_[0])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "inearRegression() initializes the model.\n",
    "\n",
    "fit() trains the model using the training data. The algorithm calculates the optimal values for the intercept and coefficient that minimize the prediction error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Actual      Predicted\n",
      "7  300000  298673.740053\n",
      "1  210000  207824.933687\n"
     ]
    }
   ],
   "source": [
    "# Predict on the test set\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Display predictions\n",
    "results = pd.DataFrame({\"Actual\": y_test, \"Predicted\": y_pred})\n",
    "print(results)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "predict() uses the trained model to make predictions on the test data.\n",
    "\n",
    "The predictions (y_pred) are compared to the actual prices (y_test)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error (MSE): 3244939.4564093202\n",
      "R-squared (R2): 0.998397560762267\n"
     ]
    }
   ],
   "source": [
    "# Calculate performance metrics\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(\"Mean Squared Error (MSE):\", mse)\n",
    "print(\"R-squared (R2):\", r2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mean Squared Error (MSE): Measures the average squared difference between predicted and actual values. Lower MSE indicates better predictions.\n",
    "\n",
    "R-squared (R2): Indicates how well the model explains the variance in the target variable. A value closer to 1 indicates a better fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training data and regression line\n",
    "plt.scatter(X_train, y_train, color='blue', label='Training data')\n",
    "plt.plot(X_train, model.predict(X_train), color='red', label='Regression line')\n",
    "plt.title(\"Linear Regression Model\")\n",
    "plt.xlabel(\"Square Footage\")\n",
    "plt.ylabel(\"Price\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The regression line (in red) represents the model's predictions.\n",
    "\n",
    "The scatter plot shows the actual data points.\n",
    "\n",
    "This visualization helps validate whether the model captures the trend accurately.\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}