LinearRegression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "23555a0e",
   "metadata": {},
   "source": [
    "# Linear Regression Inbuilt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9766056",
   "metadata": {},
   "source": [
    "Importing Library and loading data into a variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3d9d33bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 32.50234527,  31.70700585],\n",
       "       [ 53.42680403,  68.77759598],\n",
       "       [ 61.53035803,  62.5623823 ],\n",
       "       [ 47.47563963,  71.54663223],\n",
       "       [ 59.81320787,  87.23092513],\n",
       "       [ 55.14218841,  78.21151827],\n",
       "       [ 52.21179669,  79.64197305],\n",
       "       [ 39.29956669,  59.17148932],\n",
       "       [ 48.10504169,  75.3312423 ],\n",
       "       [ 52.55001444,  71.30087989],\n",
       "       [ 45.41973014,  55.16567715],\n",
       "       [ 54.35163488,  82.47884676],\n",
       "       [ 44.1640495 ,  62.00892325],\n",
       "       [ 58.16847072,  75.39287043],\n",
       "       [ 56.72720806,  81.43619216],\n",
       "       [ 48.95588857,  60.72360244],\n",
       "       [ 44.68719623,  82.89250373],\n",
       "       [ 60.29732685,  97.37989686],\n",
       "       [ 45.61864377,  48.84715332],\n",
       "       [ 38.81681754,  56.87721319],\n",
       "       [ 66.18981661,  83.87856466],\n",
       "       [ 65.41605175, 118.5912173 ],\n",
       "       [ 47.48120861,  57.25181946],\n",
       "       [ 41.57564262,  51.39174408],\n",
       "       [ 51.84518691,  75.38065167],\n",
       "       [ 59.37082201,  74.76556403],\n",
       "       [ 57.31000344,  95.45505292],\n",
       "       [ 63.61556125,  95.22936602],\n",
       "       [ 46.73761941,  79.05240617],\n",
       "       [ 50.55676015,  83.43207142],\n",
       "       [ 52.22399609,  63.35879032],\n",
       "       [ 35.56783005,  41.4128853 ],\n",
       "       [ 42.43647694,  76.61734128],\n",
       "       [ 58.16454011,  96.76956643],\n",
       "       [ 57.50444762,  74.08413012],\n",
       "       [ 45.44053073,  66.58814441],\n",
       "       [ 61.89622268,  77.76848242],\n",
       "       [ 33.09383174,  50.71958891],\n",
       "       [ 36.43600951,  62.12457082],\n",
       "       [ 37.67565486,  60.81024665],\n",
       "       [ 44.55560838,  52.68298337],\n",
       "       [ 43.31828263,  58.56982472],\n",
       "       [ 50.07314563,  82.90598149],\n",
       "       [ 43.87061265,  61.4247098 ],\n",
       "       [ 62.99748075, 115.2441528 ],\n",
       "       [ 32.66904376,  45.57058882],\n",
       "       [ 40.16689901,  54.0840548 ],\n",
       "       [ 53.57507753,  87.99445276],\n",
       "       [ 33.86421497,  52.72549438],\n",
       "       [ 64.70713867,  93.57611869],\n",
       "       [ 38.11982403,  80.16627545],\n",
       "       [ 44.50253806,  65.10171157],\n",
       "       [ 40.59953838,  65.56230126],\n",
       "       [ 41.72067636,  65.28088692],\n",
       "       [ 51.08863468,  73.43464155],\n",
       "       [ 55.0780959 ,  71.13972786],\n",
       "       [ 41.37772653,  79.10282968],\n",
       "       [ 62.49469743,  86.52053844],\n",
       "       [ 49.20388754,  84.74269781],\n",
       "       [ 41.10268519,  59.35885025],\n",
       "       [ 41.18201611,  61.68403752],\n",
       "       [ 50.18638949,  69.84760416],\n",
       "       [ 52.37844622,  86.09829121],\n",
       "       [ 50.13548549,  59.10883927],\n",
       "       [ 33.64470601,  69.89968164],\n",
       "       [ 39.55790122,  44.86249071],\n",
       "       [ 56.13038882,  85.49806778],\n",
       "       [ 57.36205213,  95.53668685],\n",
       "       [ 60.26921439,  70.25193442],\n",
       "       [ 35.67809389,  52.72173496],\n",
       "       [ 31.588117  ,  50.39267014],\n",
       "       [ 53.66093226,  63.64239878],\n",
       "       [ 46.68222865,  72.24725107],\n",
       "       [ 43.10782022,  57.81251298],\n",
       "       [ 70.34607562, 104.25710159],\n",
       "       [ 44.49285588,  86.64202032],\n",
       "       [ 57.5045333 ,  91.486778  ],\n",
       "       [ 36.93007661,  55.23166089],\n",
       "       [ 55.80573336,  79.55043668],\n",
       "       [ 38.95476907,  44.84712424],\n",
       "       [ 56.9012147 ,  80.20752314],\n",
       "       [ 56.86890066,  83.14274979],\n",
       "       [ 34.3331247 ,  55.72348926],\n",
       "       [ 59.04974121,  77.63418251],\n",
       "       [ 57.78822399,  99.05141484],\n",
       "       [ 54.28232871,  79.12064627],\n",
       "       [ 51.0887199 ,  69.58889785],\n",
       "       [ 50.28283635,  69.51050331],\n",
       "       [ 44.21174175,  73.68756432],\n",
       "       [ 38.00548801,  61.36690454],\n",
       "       [ 32.94047994,  67.17065577],\n",
       "       [ 53.69163957,  85.66820315],\n",
       "       [ 68.76573427, 114.85387123],\n",
       "       [ 46.2309665 ,  90.12357207],\n",
       "       [ 68.31936082,  97.91982104],\n",
       "       [ 50.03017434,  81.53699078],\n",
       "       [ 49.23976534,  72.11183247],\n",
       "       [ 50.03957594,  85.23200734],\n",
       "       [ 48.14985889,  66.22495789],\n",
       "       [ 25.12848465,  53.45439421]])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "data=np.loadtxt(\"data.csv\",delimiter=\",\")\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c08ebd83",
   "metadata": {},
   "source": [
    "Input aur Output mei split iss numpy array ko"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f5f9ad9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[32.50234527 53.42680403 61.53035803 47.47563963 59.81320787 55.14218841\n",
      " 52.21179669 39.29956669 48.10504169 52.55001444 45.41973014 54.35163488\n",
      " 44.1640495  58.16847072 56.72720806 48.95588857 44.68719623 60.29732685\n",
      " 45.61864377 38.81681754 66.18981661 65.41605175 47.48120861 41.57564262\n",
      " 51.84518691 59.37082201 57.31000344 63.61556125 46.73761941 50.55676015\n",
      " 52.22399609 35.56783005 42.43647694 58.16454011 57.50444762 45.44053073\n",
      " 61.89622268 33.09383174 36.43600951 37.67565486 44.55560838 43.31828263\n",
      " 50.07314563 43.87061265 62.99748075 32.66904376 40.16689901 53.57507753\n",
      " 33.86421497 64.70713867 38.11982403 44.50253806 40.59953838 41.72067636\n",
      " 51.08863468 55.0780959  41.37772653 62.49469743 49.20388754 41.10268519\n",
      " 41.18201611 50.18638949 52.37844622 50.13548549 33.64470601 39.55790122\n",
      " 56.13038882 57.36205213 60.26921439 35.67809389 31.588117   53.66093226\n",
      " 46.68222865 43.10782022 70.34607562 44.49285588 57.5045333  36.93007661\n",
      " 55.80573336 38.95476907 56.9012147  56.86890066 34.3331247  59.04974121\n",
      " 57.78822399 54.28232871 51.0887199  50.28283635 44.21174175 38.00548801\n",
      " 32.94047994 53.69163957 68.76573427 46.2309665  68.31936082 50.03017434\n",
      " 49.23976534 50.03957594 48.14985889 25.12848465]\n",
      "[ 31.70700585  68.77759598  62.5623823   71.54663223  87.23092513\n",
      "  78.21151827  79.64197305  59.17148932  75.3312423   71.30087989\n",
      "  55.16567715  82.47884676  62.00892325  75.39287043  81.43619216\n",
      "  60.72360244  82.89250373  97.37989686  48.84715332  56.87721319\n",
      "  83.87856466 118.5912173   57.25181946  51.39174408  75.38065167\n",
      "  74.76556403  95.45505292  95.22936602  79.05240617  83.43207142\n",
      "  63.35879032  41.4128853   76.61734128  96.76956643  74.08413012\n",
      "  66.58814441  77.76848242  50.71958891  62.12457082  60.81024665\n",
      "  52.68298337  58.56982472  82.90598149  61.4247098  115.2441528\n",
      "  45.57058882  54.0840548   87.99445276  52.72549438  93.57611869\n",
      "  80.16627545  65.10171157  65.56230126  65.28088692  73.43464155\n",
      "  71.13972786  79.10282968  86.52053844  84.74269781  59.35885025\n",
      "  61.68403752  69.84760416  86.09829121  59.10883927  69.89968164\n",
      "  44.86249071  85.49806778  95.53668685  70.25193442  52.72173496\n",
      "  50.39267014  63.64239878  72.24725107  57.81251298 104.25710159\n",
      "  86.64202032  91.486778    55.23166089  79.55043668  44.84712424\n",
      "  80.20752314  83.14274979  55.72348926  77.63418251  99.05141484\n",
      "  79.12064627  69.58889785  69.51050331  73.68756432  61.36690454\n",
      "  67.17065577  85.66820315 114.85387123  90.12357207  97.91982104\n",
      "  81.53699078  72.11183247  85.23200734  66.22495789  53.45439421]\n"
     ]
    }
   ],
   "source": [
    "input=data[:,0]\n",
    "output=data[:,1]\n",
    "print(input)\n",
    "print(output)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8338b3eb",
   "metadata": {},
   "source": [
    "Now we have input array alag aur output array alag as need that to train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c6c196af",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "18cbb08d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data\n",
      "( 45.44053072531998 , 66.5881444142286 )\n",
      "( 68.31936081825536 , 97.91982103524285 )\n",
      "( 63.61556125145331 , 95.22936601755531 )\n",
      "( 40.1668990087037 , 54.08405479622361 )\n",
      "( 36.43600951138687 , 62.12457081807178 )\n",
      "( 57.36205213323824 , 95.53668684646722 )\n",
      "( 25.128484647772304 , 53.454394214850524 )\n",
      "( 51.088634678336796 , 73.4346415463243 )\n",
      "( 50.03017434031214 , 81.53699078301503 )\n",
      "( 44.21174175209011 , 73.68756431831729 )\n",
      "( 44.555608383275356 , 52.68298336638778 )\n",
      "( 33.86421497177824 , 52.725494375900425 )\n",
      "( 41.102685187349664 , 59.35885024862493 )\n",
      "( 41.57564261748702 , 51.39174407983231 )\n",
      "( 57.50444761534179 , 74.08413011660252 )\n",
      "( 51.08871989897914 , 69.58889785111847 )\n",
      "( 41.72067635634129 , 65.28088692082282 )\n",
      "( 36.93007660919181 , 55.231660886212836 )\n",
      "( 48.95588856609372 , 60.723602440673965 )\n",
      "( 59.04974121466681 , 77.63418251167786 )\n",
      "( 43.31828263186572 , 58.56982471769287 )\n",
      "( 32.66904376346719 , 45.570588823376085 )\n",
      "( 53.691639571070056 , 85.66820314500154 )\n",
      "( 57.31000343834809 , 95.45505292257474 )\n",
      "( 48.14985889102886 , 66.22495788805463 )\n",
      "( 43.87061264521837 , 61.42470980433912 )\n",
      "( 55.0780959049232 , 71.13972785861894 )\n",
      "( 51.84518690563943 , 75.38065166531236 )\n",
      "( 59.81320786951232 , 87.23092513368739 )\n",
      "( 49.203887540826 , 84.74269780782622 )\n",
      "( 64.7071386661213 , 93.57611869265824 )\n",
      "( 65.41605174513407 , 118.5912173025225 )\n",
      "( 50.28283634823073 , 69.51050331149439 )\n",
      "( 47.4756396347861 , 71.54663223356778 )\n",
      "( 70.34607561504933 , 104.25710158543822 )\n",
      "( 52.22399608555305 , 63.35879031749788 )\n",
      "( 56.13038881687547 , 85.49806777884022 )\n",
      "( 31.58811699813283 , 50.3926701350799 )\n",
      "( 50.073145632289034 , 82.90598148507051 )\n",
      "( 53.57507753167366 , 87.99445275811041 )\n",
      "( 38.00548800806069 , 61.36690453724013 )\n",
      "( 48.10504169176825 , 75.33124229706306 )\n",
      "( 41.18201610516982 , 61.68403752483363 )\n",
      "( 32.50234526945303 , 31.70700584656992 )\n",
      "( 32.940479942618296 , 67.17065576899512 )\n",
      "( 62.49469742726979 , 86.52053844034715 )\n",
      "( 47.48120860786787 , 57.25181946226897 )\n",
      "( 50.1863894948806 , 69.84760415824918 )\n",
      "( 50.13548548628612 , 59.10883926769964 )\n",
      "( 39.299566694317065 , 59.17148932186951 )\n",
      "( 49.23976534275376 , 72.11183246961566 )\n",
      "( 58.16847071685779 , 75.39287042599496 )\n",
      "( 60.269214393997906 , 70.25193441977159 )\n",
      "( 54.28232870596741 , 79.12064627468003 )\n",
      "( 56.72720805709661 , 81.43619215887864 )\n",
      "( 50.55676014854777 , 83.43207142132371 )\n",
      "( 46.73761940797697 , 79.05240616956559 )\n",
      "( 54.35163488122892 , 82.47884675749792 )\n",
      "( 45.61864377295583 , 48.84715331735507 )\n",
      "( 66.1898166067526 , 83.87856466460276 )\n",
      "( 38.119824026822805 , 80.16627544737096 )\n",
      "( 59.37082201108952 , 74.76556403215137 )\n",
      "( 52.211796692214 , 79.64197304980874 )\n",
      "( 53.66093226167304 , 63.64239877565775 )\n",
      "( 57.50453330326841 , 91.48677800011014 )\n",
      "( 53.42680403327502 , 68.77759598163891 )\n",
      "( 44.687196231480904 , 82.89250373145371 )\n",
      "( 33.64470600619178 , 69.89968164362763 )\n",
      "( 38.954769073377065 , 44.8471242424676 )\n",
      "( 55.14218841394382 , 78.21151827079923 )\n",
      "( 44.49285588085407 , 86.642020318822 )\n",
      "( 52.55001444273382 , 71.30087988685035 )\n",
      "( 46.68222864947192 , 72.24725106866237 )\n",
      "( 44.16404949677335 , 62.008923245725825 )\n",
      "( 33.09383173616396 , 50.71958891231208 )\n",
      "Testing Data\n",
      "( 56.901214702247074 , 80.20752313968276 )\n",
      "( 57.78822399323067 , 99.05141484174827 )\n",
      "( 58.16454011019286 , 96.7695664261082 )\n",
      "( 56.868900661384046 , 83.14274979204346 )\n",
      "( 46.23096649831025 , 90.12357206996742 )\n",
      "( 60.297326851333466 , 97.37989686216608 )\n",
      "( 61.89622268029126 , 77.76848241779302 )\n",
      "( 34.33312470421609 , 55.723489260543914 )\n",
      "( 35.67809388941073 , 52.72173496477499 )\n",
      "( 39.55790122290683 , 44.8624907111644 )\n",
      "( 68.76573426962166 , 114.85387123391394 )\n",
      "( 37.67565486085074 , 60.81024664990221 )\n",
      "( 41.3777265348952 , 79.10282968354986 )\n",
      "( 40.59953838455232 , 65.56230126040037 )\n",
      "( 44.5025380646451 , 65.10171157056033 )\n",
      "( 42.43647694405564 , 76.61734128007404 )\n",
      "( 35.56783004774663 , 41.41288530370056 )\n",
      "( 62.99748074755309 , 115.24415280079529 )\n",
      "( 55.80573335794274 , 79.55043667850761 )\n",
      "( 45.419730144973755 , 55.16567714595912 )\n",
      "( 61.53035802563644 , 62.5623822979458 )\n",
      "( 43.107820219102464 , 57.8125129761814 )\n",
      "( 50.03957593987599 , 85.23200734232567 )\n",
      "( 52.37844621923622 , 86.0982912057741 )\n",
      "( 38.81681753744564 , 56.877213186268506 )\n"
     ]
    }
   ],
   "source": [
    "input=input.reshape(-1,1) #convert to 2D array as want input as 2D array only\n",
    "x_train,x_test,y_train,y_test=train_test_split(input,output,test_size=0.25,random_state=1)\n",
    "print(\"Training Data\")\n",
    "for i in range(len(x_train)):\n",
    "    print(\"(\",x_train[i][0],\",\",y_train[i],\")\")\n",
    "print(\"Testing Data\")\n",
    "for i in range(len(x_test)):\n",
    "    print(\"(\",x_test[i][0],\",\",y_test[i],\")\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e4de2d5",
   "metadata": {},
   "source": [
    "Now training data aur testing data mil gaya so training data se train algorithm ke object ko aur hypothesis function derive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d8c77ab1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5acb8b8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "algorithm=LinearRegression() #object of type LinearRegression ie will assume linear relationship of output with all input\n",
    "#isi ko train karenge training data se to get hypothesis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f3928481",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algorithm.fit(x_train,y_train) #aisa karne se x_train,y_train corresponding pair ko (x,y) jaisa take as datapoint\n",
    "#aur train karega object ko to get parameters desired"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cad0a07",
   "metadata": {},
   "source": [
    "Now we got m and c of the best fitting line(as linear regression lga rhe) so we can display that as below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "70017b7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slope of the best fitting line is :  1.2310127687660946\n",
      "Intercept of the best fitting line is :  11.72677420352737\n"
     ]
    }
   ],
   "source": [
    "print(\"Slope of the best fitting line is : \",algorithm.coef_[0]) #coef_[0] as coef_ gives an array so 1 element hence 0th\n",
    "print(\"Intercept of the best fitting line is : \",algorithm.intercept_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddefb3cf",
   "metadata": {},
   "source": [
    "Need to know line kis range mei plot so obtaining x_test ka min and max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4dbdff50",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test_min=x_test.min()\n",
    "x_test_max=x_test.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46f2ea95",
   "metadata": {},
   "source": [
    "Now Plot using matplotlib first x_test and y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "86d95eda",
   "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": [
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(x_test,y_test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a565b328",
   "metadata": {},
   "source": [
    "Now proceed making the line y=mx+c ie. hypothesis function"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f798f07f",
   "metadata": {},
   "source": [
    "For this x and y array make of same size and plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a711b9d0",
   "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": [
    "x=np.arange(x_test_min,x_test_max+0.1,0.1)\n",
    "#so x ek np array [30.67,30.77,30.87,.......,70.56] aisa\n",
    "y=(algorithm.coef_)*x+(algorithm.intercept_) #so y bhi ek numpy array corresponding x ke elements into m and add c\n",
    "#now plot\n",
    "plt.plot(x,y,color=\"red\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68f6a71f",
   "metadata": {},
   "source": [
    "Together plot ke liye need to write both codes together then at the end plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5a55ef62",
   "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": [
    "x=np.arange(x_test_min,x_test_max+0.1,0.1)\n",
    "#so x ek np array [30.67,30.77,30.87,.......,70.56] aisa\n",
    "y=(algorithm.coef_)*x+(algorithm.intercept_) #so y bhi ek numpy array corresponding x ke elements into m and add c\n",
    "#now plot\n",
    "plt.plot(x,y,color=\"red\")\n",
    "plt.scatter(x_test,y_test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02b024bf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}