{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression using scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Enable inline plotting in Jupyter Notebook\n",
    "%matplotlib inline\n",
    "# Importing necessary libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "# Set the theme for Seaborn visualizations\n",
    "sns.set_theme()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate random data using a fixed random seed for reproducibility\n",
    "rng = np.random.RandomState(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate 50 random x values scaled between 0 and 10\n",
    "x = 10 * rng.rand(50)\n",
    "\n",
    "# Generate corresponding y values using a linear relation with some added noise\n",
    "y = 2 * x - 5 + rng.randn(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(x), len(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a scatter plot of the data\n",
    "plt.scatter(x, y, label=\"Data Points\")\n",
    "\n",
    "# Add labels to the axes\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "\n",
    "# Add a legend to the plot\n",
    "plt.legend()\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import LinearRegression from scikit-learn\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# Initialize the Linear Regression model\n",
    "model = LinearRegression(fit_intercept=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit the model to the data\n",
    "model.fit(x[:, np.newaxis], y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate a range of x values for prediction\n",
    "xfit = np.linspace(0, 10, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict y values based on the fitted model\n",
    "yfit = model.predict(xfit[:, np.newaxis])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the original data and the fitted line\n",
    "plt.scatter(x, y, label=\"Data Points\")\n",
    "plt.plot(xfit, yfit, color=\"red\", label=\"Fitted Line\")\n",
    "\n",
    "# Add labels to the axes\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "\n",
    "# Add a legend to the plot\n",
    "plt.legend()\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Print the best-fit values of slope and y-intercept. \n",
    "print(\"Model slope:\\t %0.2f\" %(model.coef_[0]))\n",
    "print(\"Model intercept:\\t %0.2f\" %(model.intercept_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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