linear_regression_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Define slope and intercept\n",
    "a = 10\n",
    "b = 5\n",
    "\n",
    "# Simulate x and y values for linear regression\n",
    "x = np.random.randn(30)*5\n",
    "y = a + b*x + np.random.randn(30)*20\n",
    "x_less = x[:10].reshape(-1,1)\n",
    "y_less = y[:10]\n",
    "x_full = x.reshape(-1,1)\n",
    "y_full = y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Regression plot\n",
    "def plot(x, y , predicted):\n",
    "    plt.scatter(x, y, color='black')\n",
    "    plt.plot(x, predicted,'-r')\n",
    "    plt.xlabel('X')\n",
    "    plt.ylabel('Y')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit regression model\n",
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fit with less data and plot\n",
    "model.fit(x_less, y_less)\n",
    "predicted_less = model.predict(x_less)\n",
    "plot(x_less, y_less, predicted_less)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fit with full data\n",
    "model.fit(x_full, y_full)\n",
    "predicted_full = model.predict(x_full)\n",
    "plot(x_full, y_full, predicted_full)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}