Assignment1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "21f09100",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "70b6c9c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Height-Weight Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "41c32b84",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>73.847017</td>\n",
       "      <td>241.893563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68.781904</td>\n",
       "      <td>162.310473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>74.110105</td>\n",
       "      <td>212.740856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>71.730978</td>\n",
       "      <td>220.042470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>69.881796</td>\n",
       "      <td>206.349801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Height      Weight\n",
       "0  73.847017  241.893563\n",
       "1  68.781904  162.310473\n",
       "2  74.110105  212.740856\n",
       "3  71.730978  220.042470\n",
       "4  69.881796  206.349801"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "231c93d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3501.000000</td>\n",
       "      <td>3501.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>69.024411</td>\n",
       "      <td>186.891258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.858583</td>\n",
       "      <td>19.903132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>59.380650</td>\n",
       "      <td>65.780000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>67.193523</td>\n",
       "      <td>173.896237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>69.044211</td>\n",
       "      <td>187.308370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>70.976491</td>\n",
       "      <td>200.356448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>78.998742</td>\n",
       "      <td>269.989698</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Height       Weight\n",
       "count  3501.000000  3501.000000\n",
       "mean     69.024411   186.891258\n",
       "std       2.858583    19.903132\n",
       "min      59.380650    65.780000\n",
       "25%      67.193523   173.896237\n",
       "50%      69.044211   187.308370\n",
       "75%      70.976491   200.356448\n",
       "max      78.998742   269.989698"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e9b1dd61",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[['Height']]  # x = df.drop('rent', axis=1) # ind var\n",
    "y = df['Weight'] #dep variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f1f5ad24",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>73.847017</td>\n",
       "      <td>241.893563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68.781904</td>\n",
       "      <td>162.310473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>74.110105</td>\n",
       "      <td>212.740856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>71.730978</td>\n",
       "      <td>220.042470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>69.881796</td>\n",
       "      <td>206.349801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>67.253016</td>\n",
       "      <td>152.212156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>68.785081</td>\n",
       "      <td>183.927889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>68.348516</td>\n",
       "      <td>167.971111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>67.018950</td>\n",
       "      <td>175.929440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>63.456494</td>\n",
       "      <td>156.399676</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Height      Weight\n",
       "0  73.847017  241.893563\n",
       "1  68.781904  162.310473\n",
       "2  74.110105  212.740856\n",
       "3  71.730978  220.042470\n",
       "4  69.881796  206.349801\n",
       "5  67.253016  152.212156\n",
       "6  68.785081  183.927889\n",
       "7  68.348516  167.971111\n",
       "8  67.018950  175.929440\n",
       "9  63.456494  156.399676"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f8d87edc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split as tts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4df1e55f",
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain , xtest , ytrain, ytest = tts(x,y,train_size=.70,random_state=1) #test_size=.30\n",
    "#train, test = tts(df,train_size=.70,random_state=1) #test_size=.30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a515a1b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2450, 1)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7482c64c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>66.786927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2696</th>\n",
       "      <td>65.192862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1334</th>\n",
       "      <td>68.390109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3402</th>\n",
       "      <td>68.145621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1159</th>\n",
       "      <td>71.647580</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Height\n",
       "221   66.786927\n",
       "2696  65.192862\n",
       "1334  68.390109\n",
       "3402  68.145621\n",
       "1159  71.647580"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain.head() #52, 17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "65f9cd49",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9485d7a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression() # creating object for linear model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "77d39963",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(xtrain ,ytrain) #train the ML model ; xtrain=x ; ytrain=y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4da6d980",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.97744946])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "de69ec7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-225.82603000755844"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cffc4934",
   "metadata": {},
   "outputs": [],
   "source": [
    "m = lr.coef_\n",
    "c = lr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "899236ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 70\n",
    "y = (m * x) + c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "92895410",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([192.59543231])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d71033c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>73.847017</td>\n",
       "      <td>241.893563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68.781904</td>\n",
       "      <td>162.310473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>74.110105</td>\n",
       "      <td>212.740856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>71.730978</td>\n",
       "      <td>220.042470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>69.881796</td>\n",
       "      <td>206.349801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>3496</th>\n",
       "      <td>69.296830</td>\n",
       "      <td>185.649086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3497</th>\n",
       "      <td>70.542201</td>\n",
       "      <td>210.198845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3498</th>\n",
       "      <td>73.912549</td>\n",
       "      <td>202.429960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3499</th>\n",
       "      <td>67.396021</td>\n",
       "      <td>144.277671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3500</th>\n",
       "      <td>72.738970</td>\n",
       "      <td>187.222909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3501 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Height      Weight\n",
       "0     73.847017  241.893563\n",
       "1     68.781904  162.310473\n",
       "2     74.110105  212.740856\n",
       "3     71.730978  220.042470\n",
       "4     69.881796  206.349801\n",
       "...         ...         ...\n",
       "3496  69.296830  185.649086\n",
       "3497  70.542201  210.198845\n",
       "3498  73.912549  202.429960\n",
       "3499  67.396021  144.277671\n",
       "3500  72.738970  187.222909\n",
       "\n",
       "[3501 rows x 2 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "815d1d71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([215.59078213, 185.31432533, 217.16337956, ..., 215.98249712,\n",
       "       177.03027812, 208.96748706])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.predict(df[['Height']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e8b4d8d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Predicted Weight'] = lr.predict(df[['Height']]) # y=mx+c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2dfdf416",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Predicted Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>73.847017</td>\n",
       "      <td>241.893563</td>\n",
       "      <td>215.590782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68.781904</td>\n",
       "      <td>162.310473</td>\n",
       "      <td>185.314325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>74.110105</td>\n",
       "      <td>212.740856</td>\n",
       "      <td>217.163380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>71.730978</td>\n",
       "      <td>220.042470</td>\n",
       "      <td>202.942268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>69.881796</td>\n",
       "      <td>206.349801</td>\n",
       "      <td>191.888873</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Height      Weight  Predicted Weight\n",
       "0  73.847017  241.893563        215.590782\n",
       "1  68.781904  162.310473        185.314325\n",
       "2  74.110105  212.740856        217.163380\n",
       "3  71.730978  220.042470        202.942268\n",
       "4  69.881796  206.349801        191.888873"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a0f93236",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b6770aaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error # mse\n",
    "pred = lr.predict(xtest)\n",
    "mse = mean_squared_error(ytest , pred) #testing error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ab75eb42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110.89221516932378"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "97faf7ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7313258168853622"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(xtest, ytest) # R Squared Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "024de7bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7327243206606744"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(xtrain, ytrain) # R Squared Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5c7af5d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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