LinearRegression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn import datasets, linear_model\n",
    "from tabulate import tabulate\n",
    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error, accuracy_score\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes = datasets.load_diabetes()\n",
    "print(diabetes.data.shape)\n",
    "print(tabulate(diabetes.data, tablefmt=\"grid\"))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature names: ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.03807591  0.05068012  0.06169621  0.02187239 -0.0442235  -0.03482076\n",
      " -0.04340085 -0.00259226  0.01990749 -0.01764613]\n"
     ]
    }
   ],
   "source": [
    "print(diabetes.data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(442, 1)\n",
      "(442, 10)\n",
      "[[ 0.06169621]\n",
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      " [ 0.04445121]\n",
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      " [-0.04069594]\n",
      " [-0.04716281]\n",
      " [-0.00189471]\n",
      " [ 0.06169621]\n",
      " [ 0.03906215]\n",
      " [-0.08380842]\n",
      " [ 0.01750591]\n",
      " [-0.02884001]\n",
      " [-0.00189471]\n",
      " [-0.02560657]\n",
      " [-0.01806189]\n",
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      " [ 0.03906215]\n",
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      " [-0.0374625 ]\n",
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      " [-0.02452876]\n",
      " [ 0.03367309]\n",
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      " [-0.03099563]\n",
      " [-0.046085  ]\n",
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      " [ 0.0519959 ]\n",
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      " [-0.00836158]\n",
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      " [-0.04824063]\n",
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      " [-0.03315126]\n",
      " [-0.00728377]\n",
      " [-0.01375064]\n",
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      " [-0.02021751]\n",
      " [-0.05794093]\n",
      " [ 0.06061839]\n",
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      " [-0.07195249]\n",
      " [-0.05578531]\n",
      " [ 0.04552903]\n",
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      " [-0.0105172 ]\n",
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      " [-0.03854032]\n",
      " [ 0.02612841]\n",
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      " [ 0.06061839]\n",
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      " [-0.02991782]\n",
      " [-0.0191397 ]\n",
      " [-0.04069594]\n",
      " [ 0.01535029]\n",
      " [-0.02452876]\n",
      " [ 0.00133873]\n",
      " [ 0.06924089]\n",
      " [-0.06979687]\n",
      " [-0.02991782]\n",
      " [-0.046085  ]\n",
      " [ 0.01858372]\n",
      " [ 0.00133873]\n",
      " [-0.03099563]\n",
      " [-0.00405033]\n",
      " [ 0.01535029]\n",
      " [ 0.02289497]\n",
      " [ 0.04552903]\n",
      " [-0.04500719]\n",
      " [-0.03315126]\n",
      " [ 0.097264  ]\n",
      " [ 0.05415152]\n",
      " [ 0.12313149]\n",
      " [-0.08057499]\n",
      " [ 0.09295276]\n",
      " [-0.05039625]\n",
      " [-0.01159501]\n",
      " [-0.0277622 ]\n",
      " [ 0.05846277]\n",
      " [ 0.08540807]\n",
      " [-0.00081689]\n",
      " [ 0.00672779]\n",
      " [ 0.00888341]\n",
      " [ 0.08001901]\n",
      " [ 0.07139652]\n",
      " [-0.02452876]\n",
      " [-0.0547075 ]\n",
      " [-0.03638469]\n",
      " [ 0.0164281 ]\n",
      " [ 0.07786339]\n",
      " [-0.03961813]\n",
      " [ 0.01103904]\n",
      " [-0.04069594]\n",
      " [-0.03422907]\n",
      " [ 0.00564998]\n",
      " [ 0.08864151]\n",
      " [-0.03315126]\n",
      " [-0.05686312]\n",
      " [-0.03099563]\n",
      " [ 0.05522933]\n",
      " [-0.06009656]\n",
      " [ 0.00133873]\n",
      " [-0.02345095]\n",
      " [-0.07410811]\n",
      " [ 0.01966154]\n",
      " [-0.01590626]\n",
      " [-0.01590626]\n",
      " [ 0.03906215]\n",
      " [-0.0730303 ]]\n"
     ]
    }
   ],
   "source": [
    "diabetes_X  =  diabetes.data[:,np.newaxis,2]\n",
    "print(diabetes_X.shape)\n",
    "print(diabetes.data.shape)\n",
    "# print(diabetes_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(422, 1)\n",
      "(20, 1)\n",
      "[[ 0.06169621]\n",
      " [-0.05147406]\n",
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      " [-0.01159501]\n",
      " [-0.03638469]\n",
      " [-0.04069594]\n",
      " [-0.04716281]\n",
      " [-0.00189471]\n",
      " [ 0.06169621]\n",
      " [ 0.03906215]\n",
      " [-0.08380842]\n",
      " [ 0.01750591]\n",
      " [-0.02884001]\n",
      " [-0.00189471]\n",
      " [-0.02560657]\n",
      " [-0.01806189]\n",
      " [ 0.04229559]\n",
      " [ 0.01211685]\n",
      " [-0.0105172 ]\n",
      " [-0.01806189]\n",
      " [-0.05686312]\n",
      " [-0.02237314]\n",
      " [-0.00405033]\n",
      " [ 0.06061839]\n",
      " [ 0.03582872]\n",
      " [-0.01267283]\n",
      " [-0.07734155]\n",
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      " [-0.0105172 ]\n",
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      " [-0.01159501]\n",
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      " [ 0.01427248]\n",
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      " [-0.00405033]\n",
      " [ 0.00457217]\n",
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      " [-0.0374625 ]\n",
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      " [-0.02452876]\n",
      " [-0.01806189]\n",
      " [-0.01482845]\n",
      " [-0.02991782]\n",
      " [-0.046085  ]\n",
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      " [ 0.03367309]\n",
      " [-0.00405033]\n",
      " [-0.02021751]\n",
      " [ 0.00241654]\n",
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      " [-0.03638469]\n",
      " [-0.05794093]\n",
      " [-0.0374625 ]\n",
      " [ 0.01211685]\n",
      " [-0.02237314]\n",
      " [-0.03530688]\n",
      " [ 0.00996123]\n",
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      " [-0.04824063]\n",
      " [-0.02560657]\n",
      " [ 0.0519959 ]\n",
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      " [-0.01698407]\n",
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      " [ 0.00996123]\n",
      " [ 0.08864151]\n",
      " [-0.00512814]\n",
      " [-0.06440781]\n",
      " [ 0.01750591]\n",
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      " [ 0.02828403]\n",
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      " [-0.03207344]\n",
      " [ 0.00457217]\n",
      " [ 0.02073935]\n",
      " [ 0.01427248]\n",
      " [ 0.11019775]\n",
      " [ 0.00133873]\n",
      " [ 0.05846277]\n",
      " [-0.02129532]\n",
      " [-0.0105172 ]\n",
      " [-0.04716281]\n",
      " [ 0.00457217]\n",
      " [ 0.01750591]\n",
      " [ 0.08109682]\n",
      " [ 0.0347509 ]\n",
      " [ 0.02397278]\n",
      " [-0.00836158]\n",
      " [-0.06117437]\n",
      " [-0.00189471]\n",
      " [-0.06225218]\n",
      " [ 0.0164281 ]\n",
      " [ 0.09618619]\n",
      " [-0.06979687]\n",
      " [-0.02129532]\n",
      " [-0.05362969]\n",
      " [ 0.0433734 ]\n",
      " [ 0.05630715]\n",
      " [-0.0816528 ]\n",
      " [ 0.04984027]\n",
      " [ 0.11127556]\n",
      " [ 0.06169621]\n",
      " [ 0.01427248]\n",
      " [ 0.04768465]\n",
      " [ 0.01211685]\n",
      " [ 0.00564998]\n",
      " [ 0.04660684]\n",
      " [ 0.12852056]\n",
      " [ 0.05954058]\n",
      " [ 0.09295276]\n",
      " [ 0.01535029]\n",
      " [-0.00512814]\n",
      " [ 0.0703187 ]\n",
      " [-0.00405033]\n",
      " [-0.00081689]\n",
      " [-0.04392938]\n",
      " [ 0.02073935]\n",
      " [ 0.06061839]\n",
      " [-0.0105172 ]\n",
      " [-0.03315126]\n",
      " [-0.06548562]\n",
      " [ 0.0433734 ]\n",
      " [-0.06225218]\n",
      " [ 0.06385183]\n",
      " [ 0.03043966]\n",
      " [ 0.07247433]\n",
      " [-0.0191397 ]\n",
      " [-0.06656343]\n",
      " [-0.06009656]\n",
      " [ 0.06924089]\n",
      " [ 0.05954058]\n",
      " [-0.02668438]\n",
      " [-0.02021751]\n",
      " [-0.046085  ]\n",
      " [ 0.07139652]\n",
      " [-0.07949718]\n",
      " [ 0.00996123]\n",
      " [-0.03854032]\n",
      " [ 0.01966154]\n",
      " [ 0.02720622]\n",
      " [-0.00836158]\n",
      " [-0.01590626]\n",
      " [ 0.00457217]\n",
      " [-0.04285156]\n",
      " [ 0.00564998]\n",
      " [-0.03530688]\n",
      " [ 0.02397278]\n",
      " [-0.01806189]\n",
      " [ 0.04229559]\n",
      " [-0.0547075 ]\n",
      " [-0.00297252]\n",
      " [-0.06656343]\n",
      " [-0.01267283]\n",
      " [-0.04177375]\n",
      " [-0.03099563]\n",
      " [-0.00512814]\n",
      " [-0.05901875]\n",
      " [ 0.0250506 ]\n",
      " [-0.046085  ]\n",
      " [ 0.00349435]\n",
      " [ 0.05415152]\n",
      " [-0.04500719]\n",
      " [-0.05794093]\n",
      " [-0.05578531]\n",
      " [ 0.00133873]\n",
      " [ 0.03043966]\n",
      " [ 0.00672779]\n",
      " [ 0.04660684]\n",
      " [ 0.02612841]\n",
      " [ 0.04552903]\n",
      " [ 0.04013997]\n",
      " [-0.01806189]\n",
      " [ 0.01427248]\n",
      " [ 0.03690653]\n",
      " [ 0.00349435]\n",
      " [-0.07087468]\n",
      " [-0.03315126]\n",
      " [ 0.09403057]\n",
      " [ 0.03582872]\n",
      " [ 0.03151747]\n",
      " [-0.06548562]\n",
      " [-0.04177375]\n",
      " [-0.03961813]\n",
      " [-0.03854032]\n",
      " [-0.02560657]\n",
      " [-0.02345095]\n",
      " [-0.06656343]\n",
      " [ 0.03259528]\n",
      " [-0.046085  ]\n",
      " [-0.02991782]\n",
      " [-0.01267283]\n",
      " [-0.01590626]\n",
      " [ 0.07139652]\n",
      " [-0.03099563]\n",
      " [ 0.00026092]\n",
      " [ 0.03690653]\n",
      " [ 0.03906215]\n",
      " [-0.01482845]\n",
      " [ 0.00672779]\n",
      " [-0.06871905]\n",
      " [-0.00943939]\n",
      " [ 0.01966154]\n",
      " [ 0.07462995]\n",
      " [-0.00836158]\n",
      " [-0.02345095]\n",
      " [-0.046085  ]\n",
      " [ 0.05415152]\n",
      " [-0.03530688]\n",
      " [-0.03207344]\n",
      " [-0.0816528 ]\n",
      " [ 0.04768465]\n",
      " [ 0.06061839]\n",
      " [ 0.05630715]\n",
      " [ 0.09834182]\n",
      " [ 0.05954058]\n",
      " [ 0.03367309]\n",
      " [ 0.05630715]\n",
      " [-0.06548562]\n",
      " [ 0.16085492]\n",
      " [-0.05578531]\n",
      " [-0.02452876]\n",
      " [-0.03638469]\n",
      " [-0.00836158]\n",
      " [-0.04177375]\n",
      " [ 0.12744274]\n",
      " [-0.07734155]\n",
      " [ 0.02828403]\n",
      " [-0.02560657]\n",
      " [-0.06225218]\n",
      " [-0.00081689]\n",
      " [ 0.08864151]\n",
      " [-0.03207344]\n",
      " [ 0.03043966]\n",
      " [ 0.00888341]\n",
      " [ 0.00672779]\n",
      " [-0.02021751]\n",
      " [-0.02452876]\n",
      " [-0.01159501]\n",
      " [ 0.02612841]\n",
      " [-0.05901875]\n",
      " [-0.03638469]\n",
      " [-0.02452876]\n",
      " [ 0.01858372]\n",
      " [-0.0902753 ]\n",
      " [-0.00512814]\n",
      " [-0.05255187]\n",
      " [-0.02237314]\n",
      " [-0.02021751]\n",
      " [-0.0547075 ]\n",
      " [-0.00620595]\n",
      " [-0.01698407]\n",
      " [ 0.05522933]\n",
      " [ 0.07678558]\n",
      " [ 0.01858372]\n",
      " [-0.02237314]\n",
      " [ 0.09295276]\n",
      " [-0.03099563]\n",
      " [ 0.03906215]\n",
      " [-0.06117437]\n",
      " [-0.00836158]\n",
      " [-0.0374625 ]\n",
      " [-0.01375064]\n",
      " [ 0.07355214]\n",
      " [-0.02452876]\n",
      " [ 0.03367309]\n",
      " [ 0.0347509 ]\n",
      " [-0.03854032]\n",
      " [-0.03961813]\n",
      " [-0.00189471]\n",
      " [-0.03099563]\n",
      " [-0.046085  ]\n",
      " [ 0.00133873]\n",
      " [ 0.06492964]\n",
      " [ 0.04013997]\n",
      " [-0.02345095]\n",
      " [ 0.05307371]\n",
      " [ 0.04013997]\n",
      " [-0.02021751]\n",
      " [ 0.01427248]\n",
      " [-0.03422907]\n",
      " [ 0.00672779]\n",
      " [ 0.00457217]\n",
      " [ 0.03043966]\n",
      " [ 0.0519959 ]\n",
      " [ 0.06169621]\n",
      " [-0.00728377]\n",
      " [ 0.00564998]\n",
      " [ 0.05415152]\n",
      " [-0.00836158]\n",
      " [ 0.114509  ]\n",
      " [ 0.06708527]\n",
      " [-0.05578531]\n",
      " [ 0.03043966]\n",
      " [-0.02560657]\n",
      " [ 0.10480869]\n",
      " [-0.00620595]\n",
      " [-0.04716281]\n",
      " [-0.04824063]\n",
      " [ 0.08540807]\n",
      " [-0.01267283]\n",
      " [-0.03315126]\n",
      " [-0.00728377]\n",
      " [-0.01375064]\n",
      " [ 0.05954058]\n",
      " [ 0.02181716]\n",
      " [ 0.01858372]\n",
      " [-0.01159501]\n",
      " [-0.00297252]\n",
      " [ 0.01750591]\n",
      " [-0.02991782]\n",
      " [-0.02021751]\n",
      " [-0.05794093]\n",
      " [ 0.06061839]\n",
      " [-0.04069594]\n",
      " [-0.07195249]\n",
      " [-0.05578531]\n",
      " [ 0.04552903]\n",
      " [-0.00943939]\n",
      " [-0.03315126]\n",
      " [ 0.04984027]\n",
      " [-0.08488624]\n",
      " [ 0.00564998]\n",
      " [ 0.02073935]\n",
      " [-0.00728377]\n",
      " [ 0.10480869]\n",
      " [-0.02452876]\n",
      " [-0.00620595]\n",
      " [-0.03854032]\n",
      " [ 0.13714305]\n",
      " [ 0.17055523]\n",
      " [ 0.00241654]\n",
      " [ 0.03798434]\n",
      " [-0.05794093]\n",
      " [-0.00943939]\n",
      " [-0.02345095]\n",
      " [-0.0105172 ]\n",
      " [-0.03422907]\n",
      " [-0.00297252]\n",
      " [ 0.06816308]\n",
      " [ 0.00996123]\n",
      " [ 0.00241654]\n",
      " [-0.03854032]\n",
      " [ 0.02612841]\n",
      " [-0.08919748]\n",
      " [ 0.06061839]\n",
      " [-0.02884001]\n",
      " [-0.02991782]\n",
      " [-0.0191397 ]\n",
      " [-0.04069594]\n",
      " [ 0.01535029]\n",
      " [-0.02452876]\n",
      " [ 0.00133873]\n",
      " [ 0.06924089]\n",
      " [-0.06979687]\n",
      " [-0.02991782]\n",
      " [-0.046085  ]\n",
      " [ 0.01858372]\n",
      " [ 0.00133873]\n",
      " [-0.03099563]\n",
      " [-0.00405033]\n",
      " [ 0.01535029]\n",
      " [ 0.02289497]\n",
      " [ 0.04552903]\n",
      " [-0.04500719]\n",
      " [-0.03315126]\n",
      " [ 0.097264  ]\n",
      " [ 0.05415152]\n",
      " [ 0.12313149]\n",
      " [-0.08057499]\n",
      " [ 0.09295276]\n",
      " [-0.05039625]\n",
      " [-0.01159501]\n",
      " [-0.0277622 ]\n",
      " [ 0.05846277]\n",
      " [ 0.08540807]\n",
      " [-0.00081689]\n",
      " [ 0.00672779]\n",
      " [ 0.00888341]\n",
      " [ 0.08001901]\n",
      " [ 0.07139652]\n",
      " [-0.02452876]\n",
      " [-0.0547075 ]\n",
      " [-0.03638469]\n",
      " [ 0.0164281 ]]\n"
     ]
    }
   ],
   "source": [
    "diabetes_X_train  = diabetes_X[:-20]\n",
    "diabetes_X_test = diabetes_X[-20:]\n",
    "print(diabetes_X_train.shape)\n",
    "print(diabetes_X_test.shape)\n",
    "print(diabetes_X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(422,)\n",
      "(20,)\n",
      "[151.  75. 141. 206. 135.  97. 138.  63. 110. 310. 101.  69. 179. 185.\n",
      " 118. 171. 166. 144.  97. 168.  68.  49.  68. 245. 184. 202. 137.  85.\n",
      " 131. 283. 129.  59. 341.  87.  65. 102. 265. 276. 252.  90. 100.  55.\n",
      "  61.  92. 259.  53. 190. 142.  75. 142. 155. 225.  59. 104. 182. 128.\n",
      "  52.  37. 170. 170.  61. 144.  52. 128.  71. 163. 150.  97. 160. 178.\n",
      "  48. 270. 202. 111.  85.  42. 170. 200. 252. 113. 143.  51.  52. 210.\n",
      "  65. 141.  55. 134.  42. 111.  98. 164.  48.  96.  90. 162. 150. 279.\n",
      "  92.  83. 128. 102. 302. 198.  95.  53. 134. 144. 232.  81. 104.  59.\n",
      " 246. 297. 258. 229. 275. 281. 179. 200. 200. 173. 180.  84. 121. 161.\n",
      "  99. 109. 115. 268. 274. 158. 107.  83. 103. 272.  85. 280. 336. 281.\n",
      " 118. 317. 235.  60. 174. 259. 178. 128.  96. 126. 288.  88. 292.  71.\n",
      " 197. 186.  25.  84.  96. 195.  53. 217. 172. 131. 214.  59.  70. 220.\n",
      " 268. 152.  47.  74. 295. 101. 151. 127. 237. 225.  81. 151. 107.  64.\n",
      " 138. 185. 265. 101. 137. 143. 141.  79. 292. 178.  91. 116.  86. 122.\n",
      "  72. 129. 142.  90. 158.  39. 196. 222. 277.  99. 196. 202. 155.  77.\n",
      " 191.  70.  73.  49.  65. 263. 248. 296. 214. 185.  78.  93. 252. 150.\n",
      "  77. 208.  77. 108. 160.  53. 220. 154. 259.  90. 246. 124.  67.  72.\n",
      " 257. 262. 275. 177.  71.  47. 187. 125.  78.  51. 258. 215. 303. 243.\n",
      "  91. 150. 310. 153. 346.  63.  89.  50.  39. 103. 308. 116. 145.  74.\n",
      "  45. 115. 264.  87. 202. 127. 182. 241.  66.  94. 283.  64. 102. 200.\n",
      " 265.  94. 230. 181. 156. 233.  60. 219.  80.  68. 332. 248.  84. 200.\n",
      "  55.  85.  89.  31. 129.  83. 275.  65. 198. 236. 253. 124.  44. 172.\n",
      " 114. 142. 109. 180. 144. 163. 147.  97. 220. 190. 109. 191. 122. 230.\n",
      " 242. 248. 249. 192. 131. 237.  78. 135. 244. 199. 270. 164.  72.  96.\n",
      " 306.  91. 214.  95. 216. 263. 178. 113. 200. 139. 139.  88. 148.  88.\n",
      " 243.  71.  77. 109. 272.  60.  54. 221.  90. 311. 281. 182. 321.  58.\n",
      " 262. 206. 233. 242. 123. 167.  63. 197.  71. 168. 140. 217. 121. 235.\n",
      " 245.  40.  52. 104. 132.  88.  69. 219.  72. 201. 110.  51. 277.  63.\n",
      " 118.  69. 273. 258.  43. 198. 242. 232. 175.  93. 168. 275. 293. 281.\n",
      "  72. 140. 189. 181. 209. 136. 261. 113. 131. 174. 257.  55.  84.  42.\n",
      " 146. 212.]\n"
     ]
    }
   ],
   "source": [
    "diabetes_y_train  = diabetes.target[:-20]\n",
    "diabetes_y_test = diabetes.target[-20:]\n",
    "print(diabetes_y_train.shape)\n",
    "print(diabetes_y_test.shape)\n",
    "print(diabetes_y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[151.  75. 141. 206. 135.  97. 138.  63. 110. 310. 101.  69. 179. 185.\n",
      " 118. 171. 166. 144.  97. 168.  68.  49.  68. 245. 184. 202. 137.  85.\n",
      " 131. 283. 129.  59. 341.  87.  65. 102. 265. 276. 252.  90. 100.  55.\n",
      "  61.  92. 259.  53. 190. 142.  75. 142. 155. 225.  59. 104. 182. 128.\n",
      "  52.  37. 170. 170.  61. 144.  52. 128.  71. 163. 150.  97. 160. 178.\n",
      "  48. 270. 202. 111.  85.  42. 170. 200. 252. 113. 143.  51.  52. 210.\n",
      "  65. 141.  55. 134.  42. 111.  98. 164.  48.  96.  90. 162. 150. 279.\n",
      "  92.  83. 128. 102. 302. 198.  95.  53. 134. 144. 232.  81. 104.  59.\n",
      " 246. 297. 258. 229. 275. 281. 179. 200. 200. 173. 180.  84. 121. 161.\n",
      "  99. 109. 115. 268. 274. 158. 107.  83. 103. 272.  85. 280. 336. 281.\n",
      " 118. 317. 235.  60. 174. 259. 178. 128.  96. 126. 288.  88. 292.  71.\n",
      " 197. 186.  25.  84.  96. 195.  53. 217. 172. 131. 214.  59.  70. 220.\n",
      " 268. 152.  47.  74. 295. 101. 151. 127. 237. 225.  81. 151. 107.  64.\n",
      " 138. 185. 265. 101. 137. 143. 141.  79. 292. 178.  91. 116.  86. 122.\n",
      "  72. 129. 142.  90. 158.  39. 196. 222. 277.  99. 196. 202. 155.  77.\n",
      " 191.  70.  73.  49.  65. 263. 248. 296. 214. 185.  78.  93. 252. 150.\n",
      "  77. 208.  77. 108. 160.  53. 220. 154. 259.  90. 246. 124.  67.  72.\n",
      " 257. 262. 275. 177.  71.  47. 187. 125.  78.  51. 258. 215. 303. 243.\n",
      "  91. 150. 310. 153. 346.  63.  89.  50.  39. 103. 308. 116. 145.  74.\n",
      "  45. 115. 264.  87. 202. 127. 182. 241.  66.  94. 283.  64. 102. 200.\n",
      " 265.  94. 230. 181. 156. 233.  60. 219.  80.  68. 332. 248.  84. 200.\n",
      "  55.  85.  89.  31. 129.  83. 275.  65. 198. 236. 253. 124.  44. 172.\n",
      " 114. 142. 109. 180. 144. 163. 147.  97. 220. 190. 109. 191. 122. 230.\n",
      " 242. 248. 249. 192. 131. 237.  78. 135. 244. 199. 270. 164.  72.  96.\n",
      " 306.  91. 214.  95. 216. 263. 178. 113. 200. 139. 139.  88. 148.  88.\n",
      " 243.  71.  77. 109. 272.  60.  54. 221.  90. 311. 281. 182. 321.  58.\n",
      " 262. 206. 233. 242. 123. 167.  63. 197.  71. 168. 140. 217. 121. 235.\n",
      " 245.  40.  52. 104. 132.  88.  69. 219.  72. 201. 110.  51. 277.  63.\n",
      " 118.  69. 273. 258.  43. 198. 242. 232. 175.  93. 168. 275. 293. 281.\n",
      "  72. 140. 189. 181. 209. 136. 261. 113. 131. 174. 257.  55.  84.  42.\n",
      " 146. 212. 233.  91. 111. 152. 120.  67. 310.  94. 183.  66. 173.  72.\n",
      "  49.  64.  48. 178. 104. 132. 220.  57.]\n"
     ]
    }
   ],
   "source": [
    "print(diabetes.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.07786339]\n",
      " [-0.03961813]\n",
      " [ 0.01103904]\n",
      " [-0.04069594]\n",
      " [-0.03422907]\n",
      " [ 0.00564998]\n",
      " [ 0.08864151]\n",
      " [-0.03315126]\n",
      " [-0.05686312]\n",
      " [-0.03099563]\n",
      " [ 0.05522933]\n",
      " [-0.06009656]\n",
      " [ 0.00133873]\n",
      " [-0.02345095]\n",
      " [-0.07410811]\n",
      " [ 0.01966154]\n",
      " [-0.01590626]\n",
      " [-0.01590626]\n",
      " [ 0.03906215]\n",
      " [-0.0730303 ]]\n",
      "[233.  91. 111. 152. 120.  67. 310.  94. 183.  66. 173.  72.  49.  64.\n",
      "  48. 178. 104. 132. 220.  57.]\n",
      "[225.9732401  115.74763374 163.27610621 114.73638965 120.80385422\n",
      " 158.21988574 236.08568105 121.81509832  99.56772822 123.83758651\n",
      " 204.73711411  96.53399594 154.17490936 130.91629517  83.3878227\n",
      " 171.36605897 137.99500384 137.99500384 189.56845268  84.3990668 ]\n"
     ]
    }
   ],
   "source": [
    "model=linear_model.LinearRegression()\n",
    "print(diabetes_X_test)\n",
    "print(diabetes_y_test)\n",
    "model.fit(diabetes_X_train,diabetes_y_train)\n",
    "diabetes_y_predicted = model.predict(diabetes_X_test)\n",
    "print(diabetes_y_predicted)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error is:  2548.07239872597\n",
      "Mean absolute error is:  41.22709128976145\n",
      "Weights:  [938.23786125]\n",
      "Intercept:  152.91886182616113\n",
      "Accuracy:  0.47257544798227147\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean squared error is: \", mean_squared_error(diabetes_y_test,diabetes_y_predicted))\n",
    "print(\"Mean absolute error is: \", mean_absolute_error(diabetes_y_test,diabetes_y_predicted))\n",
    "print(\"Weights: \", model.coef_)\n",
    "print(\"Intercept: \", model.intercept_)\n",
    "print(\"Accuracy: \", model.score(diabetes_X_test,diabetes_y_test))\n",
    "# print(\"R2 score: \", r2_score(diabetes_y_test,diabetes_y_predicted))\n",
    "# print(\"Accuracy: \", accuracy_score(diabetes_y_test,diabetes_y_predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(diabetes_X_test, diabetes_y_test)\n",
    "plt.plot(diabetes_X_test, diabetes_y_predicted)\n",
    "plt.xlabel(\"Diabetes_X_test\")\n",
    "plt.ylabel(\"Diabetes_Y_test\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[190.44837628]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict= model.predict([[0.04]])\n",
    "print(predict)\n",
    "plt.scatter(0.04,predict)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy: 0.34750872665083865\n",
      "Test accuracy: 0.3002338481404334\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "\n",
    "# Example: Splitting data into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(diabetes_X_train,diabetes_y_train, test_size=0.3, random_state=42)\n",
    "\n",
    "\n",
    "# Example: Training a simple linear regression model\n",
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Example: Evaluating model performance\n",
    "train_accuracy = model.score(X_train, y_train)\n",
    "test_accuracy = model.score(X_test, y_test)\n",
    "\n",
    "print(\"Training accuracy:\", train_accuracy)\n",
    "print(\"Test accuracy:\", test_accuracy)\n"
   ]
  }
 ],
 "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}