simple_linear_regression_food

simple_linear_regression_food.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 3.69]\n",
      " [ 4.39]\n",
      " [ 4.75]\n",
      " [ 6.03]\n",
      " [12.47]\n",
      " [12.98]\n",
      " [14.2 ]\n",
      " [14.76]\n",
      " [15.32]\n",
      " [16.39]\n",
      " [17.35]\n",
      " [17.77]\n",
      " [17.93]\n",
      " [18.43]\n",
      " [18.55]\n",
      " [18.8 ]\n",
      " [18.81]\n",
      " [19.04]\n",
      " [19.22]\n",
      " [19.93]\n",
      " [20.13]\n",
      " [20.33]\n",
      " [20.37]\n",
      " [20.43]\n",
      " [21.45]\n",
      " [22.52]\n",
      " [22.55]\n",
      " [22.86]\n",
      " [24.2 ]\n",
      " [24.39]\n",
      " [24.42]\n",
      " [25.2 ]\n",
      " [25.5 ]\n",
      " [26.61]\n",
      " [26.7 ]\n",
      " [27.14]\n",
      " [27.16]\n",
      " [28.62]\n",
      " [29.4 ]\n",
      " [33.4 ]]\n",
      "[115.22 135.98 119.34 114.96 187.05 243.92 267.43 238.71 295.94 317.78\n",
      " 216.   240.35 386.57 261.53 249.34 309.87 345.89 165.54 196.98 395.26\n",
      " 406.34 171.92 303.23 377.04 194.35 213.48 293.87 259.61 323.71 275.02\n",
      " 109.71 359.19 201.51 460.36 447.76 482.55 438.29 587.66 257.95 375.73]\n"
     ]
    }
   ],
   "source": [
    "income = np.array([3.69, 4.39, 4.75, 6.03, 12.47, 12.98, 14.2, 14.76, 15.32, 16.39, 17.35, 17.77, 17.93, 18.43, 18.55, 18.8, 18.81, 19.04, 19.22, 19.93, 20.13, 20.33, 20.37, 20.43, 21.45, 22.52, 22.55, 22.86, 24.2, 24.39, 24.42, 25.2, 25.5, 26.61, 26.7, 27.14, 27.16, 28.62, 29.4, 33.4,\n",
    "]).reshape((-1, 1))\n",
    "food_exp = np.array([115.22, 135.98, 119.34, 114.96, 187.05, 243.92, 267.43, 238.71, 295.94, 317.78, 216, 240.35, 386.57, 261.53, 249.34, 309.87, 345.89, 165.54, 196.98, 395.26, 406.34, 171.92, 303.23, 377.04, 194.35, 213.48, 293.87, 259.61, 323.71, 275.02, 109.71, 359.19, 201.51, 460.36, 447.76, 482.55, 438.29, 587.66, 257.95, 375.73,\n",
    "])\n",
    "print(income)\n",
    "print(food_exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression().fit(income, food_exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coefficient of determination: 0.3850022272112529\n",
      "intercept: 83.41600202075946\n",
      "slope: [10.20964297]\n"
     ]
    }
   ],
   "source": [
    "r_squared = model.score(income, food_exp)\n",
    "print('coefficient of determination:', r_squared)\n",
    "\n",
    "print('intercept:', model.intercept_) \n",
    "print('slope:', model.coef_) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted response:\n",
      "[121.08958457 128.23633465 131.91180612 144.98014912 210.73024983\n",
      " 215.93716775 228.39293217 234.11033223 239.82773229 250.75205027\n",
      " 260.55330752 264.84135756 266.47490044 271.57972192 272.80487908\n",
      " 275.35728982 275.45938625 277.80760413 279.64533987 286.89418638\n",
      " 288.93611497 290.97804356 291.38642928 291.99900786 302.41284369\n",
      " 313.33716166 313.64345095 316.80844027 330.48936185 332.42919401\n",
      " 332.7354833  340.69900482 343.76189771 355.0946014  356.01346927\n",
      " 360.50571218 360.70990503 375.61598377 383.57950528 424.41807716]\n"
     ]
    }
   ],
   "source": [
    "food_exp_pred = model.predict(income)\n",
    "print('predicted response:', food_exp_pred, sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(9,7))\n",
    "plt.scatter(income, food_exp, color = \"blue\")\n",
    "plt.plot(income, model.predict(income), color = \"black\")\n",
    "plt.title(\"example\")\n",
    "plt.xlabel(\"income = weekly income in $100\")\n",
    "plt.ylabel(\"food_exp = weekly food expenditure in $\")\n",
    "plt.axis([0, 40, 0, 700])\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}