{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "9da41178",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e1c05e3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"C:/Users/User/Downloads/height_weight.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "af7f33f6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Unnamed: 0     Height      Weight\n",
      "0            0  73.847017  241.893563\n",
      "1            1  68.781904  162.310473\n",
      "2            2  74.110105  212.740856\n",
      "3            3  71.730978  220.042470\n",
      "4            4  69.881796  206.349801\n",
      "..         ...        ...         ...\n",
      "94          94  67.103551  164.974581\n",
      "95          95  65.117485  165.717112\n",
      "96          96  71.701234  193.094164\n",
      "97          97  66.832878  180.683887\n",
      "98          98  66.471275  172.773723\n",
      "\n",
      "[99 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "75460e56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>73.847017</td>\n",
       "      <td>241.893563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>68.781904</td>\n",
       "      <td>162.310473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>74.110105</td>\n",
       "      <td>212.740856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>71.730978</td>\n",
       "      <td>220.042470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>69.881796</td>\n",
       "      <td>206.349801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     Height      Weight\n",
       "0           0  73.847017  241.893563\n",
       "1           1  68.781904  162.310473\n",
       "2           2  74.110105  212.740856\n",
       "3           3  71.730978  220.042470\n",
       "4           4  69.881796  206.349801"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "8488061b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     73.847017\n",
      "1     68.781904\n",
      "2     74.110105\n",
      "3     71.730978\n",
      "4     69.881796\n",
      "        ...    \n",
      "94    67.103551\n",
      "95    65.117485\n",
      "96    71.701234\n",
      "97    66.832878\n",
      "98    66.471275\n",
      "Name: Height, Length: 99, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "X = df[\"Height\"]\n",
    "y = df[\"Weight\"]\n",
    "plt.plot(X, y, 'ro')\n",
    "plt.show()\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "95622336",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(99,) (99,)\n"
     ]
    }
   ],
   "source": [
    "linear_regression = LinearRegression()\n",
    "linear_regression.fit(X.values.reshape(-1,1), y)\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "e7ff7bcb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unnamed: 0      0.000000\n",
      "Height         63.456494\n",
      "Weight        149.173566\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "61390ab3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-102.20778526]\n"
     ]
    }
   ],
   "source": [
    "print(linear_regression.predict([[19.685]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "590898f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.85772425]\n"
     ]
    }
   ],
   "source": [
    "print(linear_regression.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "3e826059",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-217.51708705211206\n"
     ]
    }
   ],
   "source": [
    "print(linear_regression.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "5d96f6ae",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X, y, 'o')\n",
    "plt.plot(X,linear_regression.predict(X.values.reshape(-1,1)))\n",
    "plt.xlabel(\"Height\")\n",
    "plt.ylabel(\"Weight\")\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}