weight_height_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Weight-Height Project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('weight-height.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "      <td>73.847017</td>\n",
       "      <td>241.893563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>68.781904</td>\n",
       "      <td>162.310473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>74.110105</td>\n",
       "      <td>212.740856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>71.730978</td>\n",
       "      <td>220.042470</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender     Height      Weight\n",
       "0   Male  73.847017  241.893563\n",
       "1   Male  68.781904  162.310473\n",
       "2   Male  74.110105  212.740856\n",
       "3   Male  71.730978  220.042470"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 3 columns):\n",
      "Gender    10000 non-null object\n",
      "Height    10000 non-null float64\n",
      "Weight    10000 non-null float64\n",
      "dtypes: float64(2), object(1)\n",
      "memory usage: 234.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>66.367560</td>\n",
       "      <td>161.440357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>3.847528</td>\n",
       "      <td>32.108439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>54.263133</td>\n",
       "      <td>64.700127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>63.505620</td>\n",
       "      <td>135.818051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>66.318070</td>\n",
       "      <td>161.212928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>69.174262</td>\n",
       "      <td>187.169525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>78.998742</td>\n",
       "      <td>269.989699</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Height        Weight\n",
       "count  10000.000000  10000.000000\n",
       "mean      66.367560    161.440357\n",
       "std        3.847528     32.108439\n",
       "min       54.263133     64.700127\n",
       "25%       63.505620    135.818051\n",
       "50%       66.318070    161.212928\n",
       "75%       69.174262    187.169525\n",
       "max       78.998742    269.989699"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      5000\n",
       "Female    5000\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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": [
    "df.plot(kind='scatter',x='Height',y='Weight');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the linear relationship between weight and height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "males = df[df['Gender']=='Male']\n",
    "females = df[df['Gender']=='Female']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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": [
    "fig,ax = plt.subplots()\n",
    "males.plot(kind='scatter',x='Height',y='Weight',\n",
    "          ax=ax,color='blue',alpha=0.3,\n",
    "          title='Male and Female Populations')\n",
    "females.plot(kind='scatter',x='Height',y='Weight',\n",
    "          ax=ax,color='red',alpha=0.3,\n",
    "          title='Male and Female Populations');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As per the figure we can see the seperation between male and female and male has heigher height than that of female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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": [
    "males['Height'].plot(kind='hist',bins=50,range=(50,80),alpha=0.3,color='blue')\n",
    "females['Height'].plot(kind='hist',bins=50,range=(50,80),alpha=0.3,color='red')\n",
    "plt.title('Height distribution')\n",
    "plt.legend(['Males','Females'])\n",
    "plt.xlabel('Height in')\n",
    "plt.axvline(males['Height'].mean(),color='blue',linewidth=2)\n",
    "plt.axvline(females['Height'].mean(),color='red',linewidth=2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected men are taller than Females.The red and blue lines shows the mean of Female and Male height."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting inches into feet in height column and pounds into kg in weight column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height In Feet</th>\n",
       "      <th>Weight In Kgs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "      <td>6.153918</td>\n",
       "      <td>109.702296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>5.731825</td>\n",
       "      <td>73.610192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>6.175842</td>\n",
       "      <td>96.481114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>5.977582</td>\n",
       "      <td>99.792504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>5.823483</td>\n",
       "      <td>93.582676</td>\n",
       "    </tr>\n",
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       "      <td>...</td>\n",
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       "      <td>9995</td>\n",
       "      <td>Female</td>\n",
       "      <td>5.514388</td>\n",
       "      <td>62.030591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9996</td>\n",
       "      <td>Female</td>\n",
       "      <td>5.588930</td>\n",
       "      <td>77.491114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9997</td>\n",
       "      <td>Female</td>\n",
       "      <td>5.322333</td>\n",
       "      <td>58.265451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9998</td>\n",
       "      <td>Female</td>\n",
       "      <td>5.752854</td>\n",
       "      <td>74.309506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9999</td>\n",
       "      <td>Female</td>\n",
       "      <td>5.162020</td>\n",
       "      <td>51.541543</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Gender  Height In Feet  Weight In Kgs\n",
       "0       Male        6.153918     109.702296\n",
       "1       Male        5.731825      73.610192\n",
       "2       Male        6.175842      96.481114\n",
       "3       Male        5.977582      99.792504\n",
       "4       Male        5.823483      93.582676\n",
       "...      ...             ...            ...\n",
       "9995  Female        5.514388      62.030591\n",
       "9996  Female        5.588930      77.491114\n",
       "9997  Female        5.322333      58.265451\n",
       "9998  Female        5.752854      74.309506\n",
       "9999  Female        5.162020      51.541543\n",
       "\n",
       "[10000 rows x 3 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Height In Feet'] = df[\"Height\"].astype(float) / 12\n",
    "df['Weight In Kgs'] = df[\"Weight\"].astype(float) / 2.205\n",
    "\n",
    "df2 = df.drop(['Height','Weight'], axis=\"columns\")\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height In Feet</th>\n",
       "      <th>Weight In Kgs</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>73.847017</td>\n",
       "      <td>241.893563</td>\n",
       "      <td>6.153918</td>\n",
       "      <td>109.702296</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>68.781904</td>\n",
       "      <td>162.310473</td>\n",
       "      <td>5.731825</td>\n",
       "      <td>73.610192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>74.110105</td>\n",
       "      <td>212.740856</td>\n",
       "      <td>6.175842</td>\n",
       "      <td>96.481114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>71.730978</td>\n",
       "      <td>220.042470</td>\n",
       "      <td>5.977582</td>\n",
       "      <td>99.792504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>69.881796</td>\n",
       "      <td>206.349801</td>\n",
       "      <td>5.823483</td>\n",
       "      <td>93.582676</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender     Height      Weight  Height In Feet  Weight In Kgs\n",
       "0       1  73.847017  241.893563        6.153918     109.702296\n",
       "1       1  68.781904  162.310473        5.731825      73.610192\n",
       "2       1  74.110105  212.740856        6.175842      96.481114\n",
       "3       1  71.730978  220.042470        5.977582      99.792504\n",
       "4       1  69.881796  206.349801        5.823483      93.582676"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Gender = df.Gender.apply(lambda x: 1 if x == 'Male' else 0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Gender</th>\n",
       "      <th>Height In Feet</th>\n",
       "      <th>Weight In Kgs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.153918</td>\n",
       "      <td>109.702296</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.731825</td>\n",
       "      <td>73.610192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6.175842</td>\n",
       "      <td>96.481114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5.977582</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>9995</td>\n",
       "      <td>0</td>\n",
       "      <td>5.514388</td>\n",
       "      <td>62.030591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9996</td>\n",
       "      <td>0</td>\n",
       "      <td>5.588930</td>\n",
       "      <td>77.491114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9997</td>\n",
       "      <td>0</td>\n",
       "      <td>5.322333</td>\n",
       "      <td>58.265451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9998</td>\n",
       "      <td>0</td>\n",
       "      <td>5.752854</td>\n",
       "      <td>74.309506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9999</td>\n",
       "      <td>0</td>\n",
       "      <td>5.162020</td>\n",
       "      <td>51.541543</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Gender  Height In Feet  Weight In Kgs\n",
       "0          1        6.153918     109.702296\n",
       "1          1        5.731825      73.610192\n",
       "2          1        6.175842      96.481114\n",
       "3          1        5.977582      99.792504\n",
       "4          1        5.823483      93.582676\n",
       "...      ...             ...            ...\n",
       "9995       0        5.514388      62.030591\n",
       "9996       0        5.588930      77.491114\n",
       "9997       0        5.322333      58.265451\n",
       "9998       0        5.752854      74.309506\n",
       "9999       0        5.162020      51.541543\n",
       "\n",
       "[10000 rows x 3 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['Height', 'Weight'], 1)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:,0:2]\n",
    "y = df.iloc[:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Gender</th>\n",
       "      <th>Height In Feet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.153918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.731825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6.175842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5.977582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5.823483</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Height In Feet\n",
       "0       1        6.153918\n",
       "1       1        5.731825\n",
       "2       1        6.175842\n",
       "3       1        5.977582\n",
       "4       1        5.823483"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    109.702296\n",
       "1     73.610192\n",
       "2     96.481114\n",
       "3     99.792504\n",
       "4     93.582676\n",
       "Name: Weight In Kgs, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Spliting into train and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8000, 2000)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train), len(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9033321846361925"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "linear=LinearRegression()\n",
    "linear.fit(X_train,y_train)\n",
    "linear.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([89.5927596])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pickle\n",
    "pickle.dump(linear, open('model.pkl','wb'))\n",
    "model=pickle.load(open('model.pkl','rb')) \n",
    "model.predict([[1, 5.9]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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