Multiple Linear Regression code-from-scratch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ce061ba9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import load_diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "52c810ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y = load_diabetes(return_X_y=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c35266d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.03807591,  0.05068012,  0.06169621, ..., -0.00259226,\n",
       "         0.01990842, -0.01764613],\n",
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       "        -0.06832974, -0.09220405],\n",
       "       [ 0.08529891,  0.05068012,  0.04445121, ..., -0.00259226,\n",
       "         0.00286377, -0.02593034],\n",
       "       ...,\n",
       "       [ 0.04170844,  0.05068012, -0.01590626, ..., -0.01107952,\n",
       "        -0.04687948,  0.01549073],\n",
       "       [-0.04547248, -0.04464164,  0.03906215, ...,  0.02655962,\n",
       "         0.04452837, -0.02593034],\n",
       "       [-0.04547248, -0.04464164, -0.0730303 , ..., -0.03949338,\n",
       "        -0.00421986,  0.00306441]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bc90b59f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        71.,  47., 187., 125.,  78.,  51., 258., 215., 303., 243.,  91.,\n",
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       "        77., 109., 272.,  60.,  54., 221.,  90., 311., 281., 182., 321.,\n",
       "        58., 262., 206., 233., 242., 123., 167.,  63., 197.,  71., 168.,\n",
       "       140., 217., 121., 235., 245.,  40.,  52., 104., 132.,  88.,  69.,\n",
       "       219.,  72., 201., 110.,  51., 277.,  63., 118.,  69., 273., 258.,\n",
       "        43., 198., 242., 232., 175.,  93., 168., 275., 293., 281.,  72.,\n",
       "       140., 189., 181., 209., 136., 261., 113., 131., 174., 257.,  55.,\n",
       "        84.,  42., 146., 212., 233.,  91., 111., 152., 120.,  67., 310.,\n",
       "        94., 183.,  66., 173.,  72.,  49.,  64.,  48., 178., 104., 132.,\n",
       "       220.,  57.])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0094b62a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(442, 10)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e119ca95",
   "metadata": {},
   "source": [
    "# using sklearn's linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "799e821c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "116379aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(X,y,test_size=.3,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c686f412",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(309, 10)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e96e6ea0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(133, 10)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "daee52ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ecb195db",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7240ebd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "293c828c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=reg.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c26fb33c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e43e9072",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5012620528324084"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "03a8a8e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5012620528324084"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d270bb4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -31.80536962, -214.67167622,  445.13750645,  376.35494358,\n",
       "       -802.45172889,  525.95185517,  106.39967097,  172.31122486,\n",
       "        799.47904182,   57.16768747])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "076be848",
   "metadata": {},
   "source": [
    "# Making own class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "767798d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "class meralr:\n",
    "    def __init__(self):\n",
    "        self.coef_=None\n",
    "        self.intercept_=None\n",
    "    def fit(self,x_train,y_train):\n",
    "        x_train=np.insert(x_train,0,1,axis=1)\n",
    "        betas=np.linalg.inv(np.dot(x_train.T,x_train)).dot((x_train.T).dot(y_train))\n",
    "        self.intercept_=betas[0]\n",
    "        self.coef_=betas[1:]\n",
    "        print(betas)\n",
    "    def predict(self,x_test):\n",
    "        y_pred=np.dot(x_test,self.coef_)+self.intercept_\n",
    "        return y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8975234d",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=meralr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f67f83af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 152.62754194  -31.80536962 -214.67167622  445.13750645  376.35494358\n",
      " -802.45172889  525.95185517  106.39967097  172.31122486  799.47904182\n",
      "   57.16768747]\n"
     ]
    }
   ],
   "source": [
    "lr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f8feffaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([153.24537424, 204.26239453, 125.21081597, 108.22025298,\n",
       "       256.03179654, 253.23323854, 117.42180956, 120.55273333,\n",
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       "       180.06723313, 135.90604692, 287.68304903,  90.25399564,\n",
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       "       154.97442421, 114.96217894, 192.56745582,  82.7500241 ,\n",
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       "       119.35293801, 129.17639593, 215.8735864 , 199.82513892,\n",
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       "        73.12466422,  71.2226165 , 172.17584117,  83.05034487,\n",
       "        81.99506992,  94.64184176, 179.92909231, 279.58837678,\n",
       "       209.80414234, 138.71089822, 287.32550245, 202.66857922,\n",
       "        94.89495332,  74.55140407, 153.01235986,  42.90220621,\n",
       "       185.82667308,  77.05211609, 144.20654629, 249.60404688,\n",
       "       150.43323287, 201.06792465, 153.89186922, 179.13246032,\n",
       "       114.38431383, 143.74614796,  99.45013277, 171.00090747,\n",
       "       159.25695809, 188.12598052, 120.11477683, 142.02219393,\n",
       "       184.37428495, 149.67945181, 234.95448306, 164.74947899,\n",
       "       231.1395338 , 168.27550268,  60.65855882, 109.31711784,\n",
       "        70.25002086, 159.63949505, 187.51136048,  76.13554606,\n",
       "       201.50087214, 254.57509617, 178.86213148, 203.93623616,\n",
       "       263.52654311, 198.36669512, 180.84040721,  56.73315886,\n",
       "       180.75502983,  50.73073786, 132.3695464 ,  97.82695508,\n",
       "       107.9780635 ])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "57526bb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5012620528324084"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5a425725",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -31.80536962, -214.67167622,  445.13750645,  376.35494358,\n",
       "       -802.45172889,  525.95185517,  106.39967097,  172.31122486,\n",
       "        799.47904182,   57.16768747])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "651d8feb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "152.62754194200335"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70b07f19",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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