Edited_Version_LRA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r'C:\\Users\\yunus\\Downloads\\cereal.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "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>calories</th>\n",
       "      <th>protein</th>\n",
       "      <th>fat</th>\n",
       "      <th>sodium</th>\n",
       "      <th>fiber</th>\n",
       "      <th>carbo</th>\n",
       "      <th>sugars</th>\n",
       "      <th>potass</th>\n",
       "      <th>vitamins</th>\n",
       "      <th>shelf</th>\n",
       "      <th>weight</th>\n",
       "      <th>cups</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>77.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>106.883117</td>\n",
       "      <td>2.545455</td>\n",
       "      <td>1.012987</td>\n",
       "      <td>159.675325</td>\n",
       "      <td>2.151948</td>\n",
       "      <td>14.597403</td>\n",
       "      <td>6.922078</td>\n",
       "      <td>96.077922</td>\n",
       "      <td>28.246753</td>\n",
       "      <td>2.207792</td>\n",
       "      <td>1.029610</td>\n",
       "      <td>0.821039</td>\n",
       "      <td>42.665705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>19.484119</td>\n",
       "      <td>1.094790</td>\n",
       "      <td>1.006473</td>\n",
       "      <td>83.832295</td>\n",
       "      <td>2.383364</td>\n",
       "      <td>4.278956</td>\n",
       "      <td>4.444885</td>\n",
       "      <td>71.286813</td>\n",
       "      <td>22.342523</td>\n",
       "      <td>0.832524</td>\n",
       "      <td>0.150477</td>\n",
       "      <td>0.232716</td>\n",
       "      <td>14.047289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>18.042851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.670000</td>\n",
       "      <td>33.174094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>110.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>40.400208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>110.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>210.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50.828392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>160.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>93.704912</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         calories    protein        fat      sodium      fiber      carbo  \\\n",
       "count   77.000000  77.000000  77.000000   77.000000  77.000000  77.000000   \n",
       "mean   106.883117   2.545455   1.012987  159.675325   2.151948  14.597403   \n",
       "std     19.484119   1.094790   1.006473   83.832295   2.383364   4.278956   \n",
       "min     50.000000   1.000000   0.000000    0.000000   0.000000  -1.000000   \n",
       "25%    100.000000   2.000000   0.000000  130.000000   1.000000  12.000000   \n",
       "50%    110.000000   3.000000   1.000000  180.000000   2.000000  14.000000   \n",
       "75%    110.000000   3.000000   2.000000  210.000000   3.000000  17.000000   \n",
       "max    160.000000   6.000000   5.000000  320.000000  14.000000  23.000000   \n",
       "\n",
       "          sugars      potass    vitamins      shelf     weight       cups  \\\n",
       "count  77.000000   77.000000   77.000000  77.000000  77.000000  77.000000   \n",
       "mean    6.922078   96.077922   28.246753   2.207792   1.029610   0.821039   \n",
       "std     4.444885   71.286813   22.342523   0.832524   0.150477   0.232716   \n",
       "min    -1.000000   -1.000000    0.000000   1.000000   0.500000   0.250000   \n",
       "25%     3.000000   40.000000   25.000000   1.000000   1.000000   0.670000   \n",
       "50%     7.000000   90.000000   25.000000   2.000000   1.000000   0.750000   \n",
       "75%    11.000000  120.000000   25.000000   3.000000   1.000000   1.000000   \n",
       "max    15.000000  330.000000  100.000000   3.000000   1.500000   1.500000   \n",
       "\n",
       "          rating  \n",
       "count  77.000000  \n",
       "mean   42.665705  \n",
       "std    14.047289  \n",
       "min    18.042851  \n",
       "25%    33.174094  \n",
       "50%    40.400208  \n",
       "75%    50.828392  \n",
       "max    93.704912  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 77 entries, 0 to 76\n",
      "Data columns (total 16 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   name      77 non-null     object \n",
      " 1   mfr       77 non-null     object \n",
      " 2   type      77 non-null     object \n",
      " 3   calories  77 non-null     int64  \n",
      " 4   protein   77 non-null     int64  \n",
      " 5   fat       77 non-null     int64  \n",
      " 6   sodium    77 non-null     int64  \n",
      " 7   fiber     77 non-null     float64\n",
      " 8   carbo     77 non-null     float64\n",
      " 9   sugars    77 non-null     int64  \n",
      " 10  potass    77 non-null     int64  \n",
      " 11  vitamins  77 non-null     int64  \n",
      " 12  shelf     77 non-null     int64  \n",
      " 13  weight    77 non-null     float64\n",
      " 14  cups      77 non-null     float64\n",
      " 15  rating    77 non-null     float64\n",
      "dtypes: float64(5), int64(8), object(3)\n",
      "memory usage: 9.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data contains float, interger and object values and there is no null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(77, 16)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset comprises of 77 observations and 16 characteristics. Out of which one is dependent variable and the rest are independent variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'mfr', 'type', 'calories', 'protein', 'fat', 'sodium', 'fiber',\n",
       "       'carbo', 'sugars', 'potass', 'vitamins', 'shelf', 'weight', 'cups',\n",
       "       'rating'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Sugars')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plot.figure()\n",
    "plot.hist(df['sugars'])\n",
    "plot.title('Sugars')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Vitamins')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plot.figure()\n",
    "plot.hist(df['vitamins'])\n",
    "plot.title('Vitamins')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='count', ylabel='mfr_name'>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "import seaborn as sns\n",
    "values_dict = {'N': 'Nabisco', 'Q': 'Quaker Oats', 'K': 'Kelloggs', 'R': 'Raslston Purina', 'G': 'General Mills' , 'P' :'Post' , 'A':'American Home Foods Products'}\n",
    "df['mfr_name'] = df['mfr'].map(values_dict)\n",
    "sns.countplot(y = 'mfr_name' , hue = 'type' , data=df , palette ='cool')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'mfr', 'type', 'calories', 'protein', 'fat', 'sodium', 'fiber',\n",
       "       'carbo', 'sugars', 'potass', 'vitamins', 'shelf', 'weight', 'cups',\n",
       "       'rating', 'mfr_name'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>calories</th>\n",
       "      <th>protein</th>\n",
       "      <th>fat</th>\n",
       "      <th>sodium</th>\n",
       "      <th>fiber</th>\n",
       "      <th>carbo</th>\n",
       "      <th>sugars</th>\n",
       "      <th>potass</th>\n",
       "      <th>vitamins</th>\n",
       "      <th>shelf</th>\n",
       "      <th>weight</th>\n",
       "      <th>cups</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>70</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6</td>\n",
       "      <td>280</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.33</td>\n",
       "      <td>68.402973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>120</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8</td>\n",
       "      <td>135</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>33.983679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>260</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5</td>\n",
       "      <td>320</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.33</td>\n",
       "      <td>59.425505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>330</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>93.704912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>110</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>-1</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>34.384843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>110</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>39.106174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>110</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>140</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>27.753301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>230</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3</td>\n",
       "      <td>115</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.67</td>\n",
       "      <td>49.787445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>200</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3</td>\n",
       "      <td>110</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>51.592193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>110</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>60</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>36.187559</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    calories  protein  fat  sodium  fiber  carbo  sugars  potass  vitamins  \\\n",
       "0         70        4    1     130   10.0    5.0       6     280        25   \n",
       "1        120        3    5      15    2.0    8.0       8     135         0   \n",
       "2         70        4    1     260    9.0    7.0       5     320        25   \n",
       "3         50        4    0     140   14.0    8.0       0     330        25   \n",
       "4        110        2    2     200    1.0   14.0       8      -1        25   \n",
       "..       ...      ...  ...     ...    ...    ...     ...     ...       ...   \n",
       "72       110        2    1     250    0.0   21.0       3      60        25   \n",
       "73       110        1    1     140    0.0   13.0      12      25        25   \n",
       "74       100        3    1     230    3.0   17.0       3     115        25   \n",
       "75       100        3    1     200    3.0   17.0       3     110        25   \n",
       "76       110        2    1     200    1.0   16.0       8      60        25   \n",
       "\n",
       "    shelf  weight  cups     rating  \n",
       "0       3     1.0  0.33  68.402973  \n",
       "1       3     1.0  1.00  33.983679  \n",
       "2       3     1.0  0.33  59.425505  \n",
       "3       3     1.0  0.50  93.704912  \n",
       "4       3     1.0  0.75  34.384843  \n",
       "..    ...     ...   ...        ...  \n",
       "72      3     1.0  0.75  39.106174  \n",
       "73      2     1.0  1.00  27.753301  \n",
       "74      1     1.0  0.67  49.787445  \n",
       "75      1     1.0  1.00  51.592193  \n",
       "76      1     1.0  0.75  36.187559  \n",
       "\n",
       "[77 rows x 13 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,3:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = df.to_numpy()\n",
    "\n",
    "dataset.shape\n",
    "\n",
    "x= dataset[:,:-1]\n",
    "\n",
    "y= dataset[:,:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(77, 16)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(77, 16)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(57, 16)\n",
      "(20, 16)\n"
     ]
    }
   ],
   "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=0.25,random_state=10)\n",
    "print (X_train.shape)\n",
    "print (X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cor_matrix = df.corr()\n",
    "plt.figure(figsize = (15,8))\n",
    "sns.heatmap(cor_matrix, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the Correlation Matrix, we can see that 'carbo' and 'shelf' has almost no correlation with 'rating' (there's no linear relationship) while 'sugars' and 'calories' has a negative correlation with 'rating' (a strong relationship). This supported the idea that the manufacturers tend to produce products containing less sugar and having less colories content to meet the consumers' taste. On the other hand, 'protein' and 'fiber' has positive correlation with rating and are likely to contribute a lot in increseanf the rating."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>rating</td>      <th>  R-squared:         </th> <td>   1.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   1.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>1.349e+16</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 09 Aug 2022</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>12:00:01</td>     <th>  Log-Likelihood:    </th> <td>  1053.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    77</td>      <th>  AIC:               </th> <td>  -2080.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    64</td>      <th>  BIC:               </th> <td>  -2050.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    12</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>   54.9272</td> <td> 3.63e-07</td> <td> 1.51e+08</td> <td> 0.000</td> <td>   54.927</td> <td>   54.927</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>calories</th>  <td>   -0.2227</td> <td> 5.66e-09</td> <td>-3.93e+07</td> <td> 0.000</td> <td>   -0.223</td> <td>   -0.223</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>protein</th>   <td>    3.2732</td> <td> 5.09e-08</td> <td> 6.43e+07</td> <td> 0.000</td> <td>    3.273</td> <td>    3.273</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>fat</th>       <td>   -1.6914</td> <td> 6.23e-08</td> <td>-2.72e+07</td> <td> 0.000</td> <td>   -1.691</td> <td>   -1.691</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sodium</th>    <td>   -0.0545</td> <td> 4.96e-10</td> <td> -1.1e+08</td> <td> 0.000</td> <td>   -0.054</td> <td>   -0.054</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>fiber</th>     <td>    3.4435</td> <td> 4.31e-08</td> <td> 7.99e+07</td> <td> 0.000</td> <td>    3.443</td> <td>    3.443</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>carbo</th>     <td>    1.0925</td> <td> 1.74e-08</td> <td> 6.27e+07</td> <td> 0.000</td> <td>    1.092</td> <td>    1.092</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sugars</th>    <td>   -0.7249</td> <td> 1.82e-08</td> <td>-3.99e+07</td> <td> 0.000</td> <td>   -0.725</td> <td>   -0.725</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>potass</th>    <td>   -0.0340</td> <td> 1.47e-09</td> <td>-2.31e+07</td> <td> 0.000</td> <td>   -0.034</td> <td>   -0.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>vitamins</th>  <td>   -0.0512</td> <td> 1.93e-09</td> <td>-2.66e+07</td> <td> 0.000</td> <td>   -0.051</td> <td>   -0.051</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>shelf</th>     <td>-3.721e-08</td> <td> 5.28e-08</td> <td>   -0.704</td> <td> 0.484</td> <td>-1.43e-07</td> <td> 6.84e-08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>weight</th>    <td>-4.298e-07</td> <td> 5.21e-07</td> <td>   -0.826</td> <td> 0.412</td> <td>-1.47e-06</td> <td>  6.1e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cups</th>      <td> 1.379e-07</td> <td> 1.92e-07</td> <td>    0.717</td> <td> 0.476</td> <td>-2.46e-07</td> <td> 5.22e-07</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>14.739</td> <th>  Durbin-Watson:     </th> <td>   2.158</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.001</td> <th>  Jarque-Bera (JB):  </th> <td>   3.923</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.068</td> <th>  Prob(JB):          </th> <td>   0.141</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 1.903</td> <th>  Cond. No.          </th> <td>3.69e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 3.69e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 rating   R-squared:                       1.000\n",
       "Model:                            OLS   Adj. R-squared:                  1.000\n",
       "Method:                 Least Squares   F-statistic:                 1.349e+16\n",
       "Date:                Tue, 09 Aug 2022   Prob (F-statistic):               0.00\n",
       "Time:                        12:00:01   Log-Likelihood:                 1053.2\n",
       "No. Observations:                  77   AIC:                            -2080.\n",
       "Df Residuals:                      64   BIC:                            -2050.\n",
       "Df Model:                          12                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     54.9272   3.63e-07   1.51e+08      0.000      54.927      54.927\n",
       "calories      -0.2227   5.66e-09  -3.93e+07      0.000      -0.223      -0.223\n",
       "protein        3.2732   5.09e-08   6.43e+07      0.000       3.273       3.273\n",
       "fat           -1.6914   6.23e-08  -2.72e+07      0.000      -1.691      -1.691\n",
       "sodium        -0.0545   4.96e-10   -1.1e+08      0.000      -0.054      -0.054\n",
       "fiber          3.4435   4.31e-08   7.99e+07      0.000       3.443       3.443\n",
       "carbo          1.0925   1.74e-08   6.27e+07      0.000       1.092       1.092\n",
       "sugars        -0.7249   1.82e-08  -3.99e+07      0.000      -0.725      -0.725\n",
       "potass        -0.0340   1.47e-09  -2.31e+07      0.000      -0.034      -0.034\n",
       "vitamins      -0.0512   1.93e-09  -2.66e+07      0.000      -0.051      -0.051\n",
       "shelf      -3.721e-08   5.28e-08     -0.704      0.484   -1.43e-07    6.84e-08\n",
       "weight     -4.298e-07   5.21e-07     -0.826      0.412   -1.47e-06     6.1e-07\n",
       "cups        1.379e-07   1.92e-07      0.717      0.476   -2.46e-07    5.22e-07\n",
       "==============================================================================\n",
       "Omnibus:                       14.739   Durbin-Watson:                   2.158\n",
       "Prob(Omnibus):                  0.001   Jarque-Bera (JB):                3.923\n",
       "Skew:                          -0.068   Prob(JB):                        0.141\n",
       "Kurtosis:                       1.903   Cond. No.                     3.69e+03\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 3.69e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fit the linear regression module\n",
    "\n",
    "rating_model_1 = sm.OLS.from_formula(\n",
    "\"rating ~ calories + protein + fat + sodium + fiber + carbo + sugars + potass + vitamins + shelf + weight + cups\", data = df).fit()\n",
    "rating_model_1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    97.527094\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Testing the rating of a new product\n",
    "\n",
    "nutri = {\"calories\":50,\"protein\":6,\"fat\":1,\"sodium\":130,\"fiber\":14,\"carbo\":5,\"sugars\":0,\"potass\":280,\"vitamins\":25,\"shelf\":3,\"weight\":1,\"cups\":0.33}\n",
    "df= pd.DataFrame(nutri,index=[\"0\"])\n",
    "print(rating_model_1.predict(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'copy_X': True,\n",
       " 'fit_intercept': True,\n",
       " 'n_jobs': None,\n",
       " 'normalize': 'deprecated',\n",
       " 'positive': False}"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.get_params()"
   ]
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}