Linear Regression in Python - using numpy polyfit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data36 Tutorial. Find the full article here: https://data36.com/linear-regression-python-sklearn-numpy-polyfit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## STEP \\#1 - Importing the libraries"
   ]
  },
  {
   "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",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step \\#2 - Getting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "students = {'hours': [29, 9, 10, 38, 16, 26, 50, 10, 30, 33, 43, 2, 39, 15, 44, 29, 41, 15, 24, 50],\n",
    "            'test_results': [65, 7, 8, 76, 23, 56, 100, 3, 74, 48, 73, 0, 62, 37, 74, 40, 90, 42, 58, 100]}\n",
    "student_data = pd.DataFrame(data=students)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step \\#3 - Defining the feature (x) and target (y) values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = student_data.hours\n",
    "y = student_data.test_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x121f987f0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.scatter(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step \\#4 - Machine Learning: Linear Regression (line fitting)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = np.polyfit(x, y, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Step \\#5 - The result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.01467487, -3.9057602 ])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict = np.poly1d(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36.38773723347843"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hours_studied = 20\n",
    "predict(hours_studied)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8777480188408424"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r2_score(y, predict(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step \\#6 - Dataviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a24579080>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gF14Ydtecc07UUYkAquhFCpM7DKShARYsgHXrlOQlVlTRi+Rrw4awZXLrVrj2Wpg3L3SbjIlEjkmUklBFL9JTbW3wta/BiBHw1luhN83TT8cuySdqTKKUlBK9yLFyDwl98OCwBn/LLbBtWywnPiVqTKKUnJZuRI7Fzp0weTKsXh0eflq1KtZ94hMxJlHKRhW9yNFkh4GcfTa88AI88ECY+BTjJA8laKcsiaZEL9KVzZvh/PPh1lvh4ovDMs20aXB8/P8hXNR2ypJ4SvQiHWWHgfz1X4dhIE89lbhhIPmMSZT0in9pIlJOq1fDzTfDrl2hT81990FNTdRR5SXvdsqSOqroRSAMA7nuurCD5sQTw0NPDz+c2CQvkkuJXirb4cMhoQ8eDCtWhGEgjY1hj7xISmjpRipX7jCQSy4JCf/MM6OOSqToVNFL5WlvP3IYyGOPQX29kryklip6OSap6ZuSOwxkwoSwL1594iXlVNFLt1LRNyV3GIg7PPdcGOunJC8VQIleupXovinuIaFnh4Hcdhs0NcFll0UdmUjZFLx0Y2ZVQAPQ4u5jzOwM4EngFGAzcIO7v13oeSQ6ie2bEvNhIKlZDpPYK0ZFPxVoznn9HWCOuw8E3gC+UoRzSIQS1zclAcNAUrEcJolRUKI3s37AlcAjmdcGjAKWZt6yGBhXyDkkeonqm7J+PZx3XthVM3YsNDeHqv64eK1SJno5TBKn0J/+ucDfA4czrz8CtLn7wczr3UCn/xY1s0lm1mBmDfv27SswDCmlRPRNaWsLCX3kyDAMZMWK2A0DyZXY5TBJpLzX6M1sDLDX3TeZ2cXZw5281Tv7endfBCwCqKur6/Q9Eh+x7ZviDkuXwpQpsHdvGAZy552hjUGM9a2ppqWTpB7b5TBJtEIq+hHAVWb2KuHm6yhChV9jZtm/QPoBrQVFKNKVXbvC8sx110GfPqFP/IMPxj7JQ8KWwyTx8k707j7T3fu5+wDgemCtu38BeAG4NvO2icAzBUcpkVve2MKI+9ZyxozVjLhvbbQ3DQ8ehDlzYMiQRA0DyZWI5TBJjVI8Gfst4EkzuwtoBL5fgnNIGWV3iGRvHmZ3iADlT0ybN8ONN4aPV1wB8+fDgAHljaFIYrscJqlTlK0I7v6iu4/JfP47dx/u7h939/Hu/udinEOiE4sdIrnDQFpawjCQVasSm+RFykm9bqRbke8QSdEwEJEoxGtzscRSZA9MdRwG8tJLGgYikgcleulW2XeIdDUMZOTI0pxPJOW0dCPdyt4wLEtfFg0DESk6JXo5JiXfIXLgQKjcv/td+NCHwjCQCRPAOnsGT0R6Qoleopc7DOSGG8K++N69o45KJDW0Ri/R6WwYyOOPK8mLFJkSvZSfe0joGgYiUhZaupHyeuUV+NrXwjDuGA4DEUkjVfRSHm+/DffcE4aB/OpXsRwGIpJWquil9DZsCFsmt26F8eNh3rzQbVJEykIVvZROW1tYphkxIgwDWbkSlixRkhcpMyV6KT73MN1p8OCwBn/LLbBtW2hlICJlp6UbKa6dO2Hy5NCIbNiw0GEyQX3iRdJIFb0UR3YYyNlnJ3YYiEhaqaKXwuUOA7nyyjAM5GMfizoqEclQRS/5278fbr01DANpbQ03WleuVJIXiRlV9JIfDQMRSQxV9NIzHYeBrFunYSAiMadEL8fm8GFYuPD9w0BGjIg6MhHphpZupHu5w0BGjQoV/MCBUUclIsdIFb107cABmDUr7Iffvj0MA3n+eSV5kYRRRS+dyx0GMmFC2Bd/6qlRRyUieVBFL0d6/XX40pfCMBAIFfzixUryIgmmil4Cd/jXf4Vp0+DNN8MwkFmzoLr6qF+2vLGlPEPDRSRvSvSS9zCQ5Y0tzFzWRPs7hwBoaWtn5rImACV7kRjR0k0lK3AYyOw1299N8lnt7xxi9prtpYhWRPKkir5SFWEYSGtbe4+Oi0g0VNFXmiIOA+lb0/n6fVfHRSQaeSd6MzvdzF4ws2Yz22pmUzPHTzGz58zslczHk4sXruStBMNApo8eRHWvqiOOVfeqYvroQYVGKyJFVEhFfxC41d0HAxcAk81sCDADqHf3gUB95rVEadcuuOqq0KOmT5/QJ/7BB0OvmgKMG1bLvdcMpbamGgNqa6q595qhuhErEjN5r9G7+x5gT+bz/zazZqAWuBq4OPO2xcCLwLcKilLyc/Ag/PM/w7e/HSr6Bx6AKVPg+OLdmhk3rFaJXSTmivJ/vJkNAIYBG4HTMn8J4O57zOyjXXzNJGASQP/+/YsRhuTKHQZyxRXw0EPqEy9SoQq+GWtmJwI/Af7O3d861q9z90XuXufudb179y40DMnavz889JQdBvLUU2Fuq5K8SMUqKNGbWS9Ckv+Ruy/LHH7NzPpkfr8PsLewEOWYrVoVZrbOmROq+ebmsC5vFnVkIhKhQnbdGPB9oNndH8z5rRXAxMznE4Fn8g9Pjkl2GMjYsRoGIiLvU0hFPwK4ARhlZr/O/LoCuA/4jJm9Anwm81pK4fDhkNCzw0D+8R81DERE3qeQXTfrgK7WBC7N98+VY5Q7DOSSS8L0J/WJF5FO6MnYpDlwIGyXzB0GUl+vJC8iXVKvmyRZuxa++tXQbVLDQETkGKmiT4LcYSCHD8Nzz2kYiIgcMyX6OMsOAznrLPjRj8IwkKYmuOyyqCMTkQTR0k1c7dgRlml6OAxERKQjVfRx03EYyEMP9WgYiIhIR6ro42TDBrjpJtiyBT73Ofinf4K+faOOSkQSThV9HLz5Jtx8M4wcGT5fsQKWLlWSF5GiUKKPkntI6IMHhweepk4ND0KNHRt1ZCKSIlq6icquXTB5cmhE9slPhiq+ri7qqEQkhVTRl9uhQzB3LgwZEh6Auv/+cNNVSV5ESkQVfTlt3hz602zaFIaBzJ8PAwZEHZWIpJwq+nLYvx9uvTUMA9m9+71hIEryIlIGquhLbfXqsKNm165Qzd93H5x8ctRRiUgFUaIvlT/8IeyiWbIk7Kp56aWwfbIAyxtbmL1mO61t7fStqWb66EEazC0i3dLSTbEdPhy2Sp51FjzzDNx5ZxgGUoQkP3NZEy1t7TjQ0tbOzGVNLG9sKU7cIpJaquiLaevW8GTr+vVw8cUh4Z95Zo//mM4q99lrttP+zqEj3tf+ziFmr9muql5EjkqJvhgOHIC774bvfAdOOgl+8AOYODGvodzZyj2b1LOVe8ckn9Xa1l5Q6CKSfkr0hcodBnLDDWEYSO/eef9xXVXuVWYccn/f+/vWVOd9LhGpDFqjz1dnw0Aef7ygJA9dV+iH3KnuVXXEsepeVUwfPaig84lI+inR91R2GMjgwWEYyMyZRR0G0lWFXltTzb3XDKW2phrLea31eRHpjpZueiJ3GMgFF4RhIEOHFvUU00cPet+afLZyHzesVoldRHpMFf2x6DgMZP78sLOmyEkeYNywWlXuIlJUqui78+//Hp5oLeMwEFXuIlJMSvRdefPNsP7+8MPQr19oIxxxn3g9GSsi+VCi78gdfvITmDIFXnsttDG4886wPz5CXe2vB5TsReSotEafa9cuuOoqGD8eTjsNNm6EOXMiT/LQ9f762Wu2RxSRiCSFEj0kYhhIV/vr9WSsiHRHSzeNjXDjjWEYyOWXw0MPxbJPfN+aalo6Sep6MlZEulOSit7MPmtm281sh5nNKMU5emp5Ywsj7lvLGTNWM+K+taxc/9swDKSuLgwDefLJ0Ds+hkkewv56PRkrIvkoekVvZlXAfOAzwG7gV2a2wt23Fftcx6rjjcwzN/2CT927AN7am5hhINkbrtp1IyI9VYqlm+HADnf/HYCZPQlcDUSW6LM3Mnvvf4M76hcx5jcv8duP9OdrN81lwcNTowqrx7S/XkTyUYpEXwv8Puf1buD8jm8ys0nAJID+/fuXIIz37HnjT/ztf6xhxouP8b8Ovs39n/4iC8//HAerepX0vCIicVCKRN9ZE/b39dd190XAIoC6urr3998tlq1b+elTt/GJnVvY0P9cbh89mf86JVTFtbqRKSIVoBSJfjdwes7rfkBrCc5zdDnDQAZ/4IPMHDuNJwZf8u4wEN3IFJFKUYpdN78CBprZGWZ2AnA9sKIE5+naCy/AuefCXXfB9ddzwiu/5fx/mEbtyR9QozARqThFr+jd/aCZfR1YA1QBj7r71mKfp1Ovvw7f/CYsXgx/9VdhGEimT/y43moVICKVqSQPTLn7s8Czpfizuzgh/PCHMG0atLXBbbfBrFlQrTV4EZHkPxmbOwzkwgth4cKS9IkXEUmqZPe6efTR94aBPPQQrFunJC8i0kGyK/ozz4QxY2DevJIPAxERSapkJ/qRI8MvERHpUrKXbkREpFtK9CIiKadELyKSckr0IiIpp0QvIpJySvQiIimnRC8iknJK9CIiKWfupZv5ccxBmO0Ddub55acCrxcxnCTQNVcGXXNlKOSaP+buvbt7UywSfSHMrMHd66KOo5x0zZVB11wZynHNWroREUk5JXoRkZRLQ6JfFHUAEdA1VwZdc2Uo+TUnfo1eRESOLg0VvYiIHEWiE72ZfdbMtpvZDjObEXU8pWBmj5rZXjPbknPsFDN7zsxeyXw8OcoYi8nMTjezF8ys2cy2mtnUzPE0X/NfmNkvzew/Mtf8D5njZ5jZxsw1P2VmJ0Qda7GZWZWZNZrZqszrVF+zmb1qZk1m9msza8gcK/nPdmITvZlVAfOBy4EhwOfNbEi0UZXEY8BnOxybAdS7+0CgPvM6LQ4Ct7r7YOACYHLmv2uar/nPwCh3/wTwSeCzZnYB8B1gTuaa3wC+EmGMpTIVaM55XQnXfIm7fzJnS2XJf7YTm+iB4cAOd/+du78NPAlcHXFMRefuvwD+2OHw1cDizOeLgXFlDaqE3H2Pu2/OfP7fhCRQS7qv2d19f+Zlr8wvB0YBSzPHU3XNAGbWD7gSeCTz2kj5NXeh5D/bSU70tcDvc17vzhyrBKe5+x4IiRH4aMTxlISZDQCGARtJ+TVnljB+DewFngP+H9Dm7gczb0njz/dc4O+Bw5nXHyH91+zAz8xsk5lNyhwr+c92kmfGWifHtIUoJczsROAnwN+5+1uh2Esvdz8EfNLMaoCfAoM7e1t5oyodMxsD7HX3TWZ2cfZwJ29NzTVnjHD3VjP7KPCcmf2mHCdNckW/Gzg953U/oDWiWMrtNTPrA5D5uDfieIrKzHoRkvyP3H1Z5nCqrznL3duAFwn3J2rMLFuMpe3newRwlZm9Slh2HUWo8NN8zbh7a+bjXsJf6MMpw892khP9r4CBmbv0JwDXAysijqlcVgATM59PBJ6JMJaiyqzTfh9odvcHc34rzdfcO1PJY2bVwGWEexMvANdm3paqa3b3me7ez90HEP7fXevuXyDF12xmHzSzk7KfA/8H2EIZfrYT/cCUmV1BqAKqgEfd/e6IQyo6M3sCuJjQ4e414A5gObAE6A/sAsa7e8cbtolkZiOBl4Am3lu7vY2wTp/Waz6XcBOuilB8LXH3O83sLwnV7ilAI/BFd/9zdJGWRmbp5pvuPibN15y5tp9mXh4P/Njd7zazj1Din+1EJ3oREelekpduRETkGCjRi4iknBK9iEjKKdGLiKScEr2ISMop0YuIpJwSvYhIyinRi4ik3P8Hi58zFUxeNmMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_lin_reg = range(0, 51)\n",
    "y_lin_reg = predict(x_lin_reg)\n",
    "plt.scatter(x, y)\n",
    "plt.plot(x_lin_reg, y_lin_reg, c = 'r')"
   ]
  }
 ],
 "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",
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