Linear_Regression_Activity.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3a00d5f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bfec4b52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>mfr</th>\n",
       "      <th>type</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>100% Bran</td>\n",
       "      <td>N</td>\n",
       "      <td>C</td>\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>100% Natural Bran</td>\n",
       "      <td>Q</td>\n",
       "      <td>C</td>\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>All-Bran</td>\n",
       "      <td>K</td>\n",
       "      <td>C</td>\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>All-Bran with Extra Fiber</td>\n",
       "      <td>K</td>\n",
       "      <td>C</td>\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>Almond Delight</td>\n",
       "      <td>R</td>\n",
       "      <td>C</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Triples</td>\n",
       "      <td>G</td>\n",
       "      <td>C</td>\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>Trix</td>\n",
       "      <td>G</td>\n",
       "      <td>C</td>\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>Wheat Chex</td>\n",
       "      <td>R</td>\n",
       "      <td>C</td>\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>Wheaties</td>\n",
       "      <td>G</td>\n",
       "      <td>C</td>\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>Wheaties Honey Gold</td>\n",
       "      <td>G</td>\n",
       "      <td>C</td>\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 × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         name mfr type  calories  protein  fat  sodium  fiber  \\\n",
       "0                   100% Bran   N    C        70        4    1     130   10.0   \n",
       "1           100% Natural Bran   Q    C       120        3    5      15    2.0   \n",
       "2                    All-Bran   K    C        70        4    1     260    9.0   \n",
       "3   All-Bran with Extra Fiber   K    C        50        4    0     140   14.0   \n",
       "4              Almond Delight   R    C       110        2    2     200    1.0   \n",
       "..                        ...  ..  ...       ...      ...  ...     ...    ...   \n",
       "72                    Triples   G    C       110        2    1     250    0.0   \n",
       "73                       Trix   G    C       110        1    1     140    0.0   \n",
       "74                 Wheat Chex   R    C       100        3    1     230    3.0   \n",
       "75                   Wheaties   G    C       100        3    1     200    3.0   \n",
       "76        Wheaties Honey Gold   G    C       110        2    1     200    1.0   \n",
       "\n",
       "    carbo  sugars  potass  vitamins  shelf  weight  cups     rating  \n",
       "0     5.0       6     280        25      3     1.0  0.33  68.402973  \n",
       "1     8.0       8     135         0      3     1.0  1.00  33.983679  \n",
       "2     7.0       5     320        25      3     1.0  0.33  59.425505  \n",
       "3     8.0       0     330        25      3     1.0  0.50  93.704912  \n",
       "4    14.0       8      -1        25      3     1.0  0.75  34.384843  \n",
       "..    ...     ...     ...       ...    ...     ...   ...        ...  \n",
       "72   21.0       3      60        25      3     1.0  0.75  39.106174  \n",
       "73   13.0      12      25        25      2     1.0  1.00  27.753301  \n",
       "74   17.0       3     115        25      1     1.0  0.67  49.787445  \n",
       "75   17.0       3     110        25      1     1.0  1.00  51.592193  \n",
       "76   16.0       8      60        25      1     1.0  0.75  36.187559  \n",
       "\n",
       "[77 rows x 16 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r'C:\\Users\\yunus\\Downloads\\cereal.csv')\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "38f80483",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name         0\n",
      "mfr          0\n",
      "type         0\n",
      "calories     0\n",
      "protein      0\n",
      "fat          0\n",
      "sodium       0\n",
      "fiber        0\n",
      "carbo        0\n",
      "sugars       0\n",
      "potass       0\n",
      "vitamins     0\n",
      "shelf        0\n",
      "weight       0\n",
      "cups         0\n",
      "rating       0\n",
      "full name    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "76d7a57a",
   "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>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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "05841bfa",
   "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[['sugars','vitamins']].plot(kind='bar',color=['green','orange'])\n",
    "plt.xlabel('cereals')\n",
    "plt.ylabel('bins')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e246e945",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         name mfr type  calories  protein  fat  sodium  fiber  \\\n",
      "0                   100% Bran   N    C        70        4    1     130   10.0   \n",
      "1           100% Natural Bran   Q    C       120        3    5      15    2.0   \n",
      "2                    All-Bran   K    C        70        4    1     260    9.0   \n",
      "3   All-Bran with Extra Fiber   K    C        50        4    0     140   14.0   \n",
      "4              Almond Delight   R    C       110        2    2     200    1.0   \n",
      "..                        ...  ..  ...       ...      ...  ...     ...    ...   \n",
      "72                    Triples   G    C       110        2    1     250    0.0   \n",
      "73                       Trix   G    C       110        1    1     140    0.0   \n",
      "74                 Wheat Chex   R    C       100        3    1     230    3.0   \n",
      "75                   Wheaties   G    C       100        3    1     200    3.0   \n",
      "76        Wheaties Honey Gold   G    C       110        2    1     200    1.0   \n",
      "\n",
      "    carbo  sugars  potass  vitamins  shelf  weight  cups     rating  \\\n",
      "0     5.0       6     280        25      3     1.0  0.33  68.402973   \n",
      "1     8.0       8     135         0      3     1.0  1.00  33.983679   \n",
      "2     7.0       5     320        25      3     1.0  0.33  59.425505   \n",
      "3     8.0       0     330        25      3     1.0  0.50  93.704912   \n",
      "4    14.0       8      -1        25      3     1.0  0.75  34.384843   \n",
      "..    ...     ...     ...       ...    ...     ...   ...        ...   \n",
      "72   21.0       3      60        25      3     1.0  0.75  39.106174   \n",
      "73   13.0      12      25        25      2     1.0  1.00  27.753301   \n",
      "74   17.0       3     115        25      1     1.0  0.67  49.787445   \n",
      "75   17.0       3     110        25      1     1.0  1.00  51.592193   \n",
      "76   16.0       8      60        25      1     1.0  0.75  36.187559   \n",
      "\n",
      "          full name  \n",
      "0           Nabisco  \n",
      "1       Qualer Oats  \n",
      "2           Kellogs  \n",
      "3           Kellogs  \n",
      "4   Raslston Purina  \n",
      "..              ...  \n",
      "72    General Mills  \n",
      "73    General Mills  \n",
      "74  Raslston Purina  \n",
      "75    General Mills  \n",
      "76    General Mills  \n",
      "\n",
      "[77 rows x 17 columns]\n"
     ]
    }
   ],
   "source": [
    "conditions = [(df['mfr'] == 'N'),\n",
    "    (df['mfr'] == 'Q'), (df['mfr'] == 'K'),\n",
    "    (df['mfr'] == 'R'), (df['mfr'] == 'G'), (df['mfr'] == 'P'),\n",
    "    (df['mfr'] == 'A')]\n",
    "choices = ['Nabisco','Qualer Oats','Kellogs','Raslston Purina','General Mills','Post','American Home Foods Products']\n",
    "df['full name'] = np.select(conditions,choices, default=0)\n",
    "print(df)       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "25a7a46b",
   "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": [
    "sns.countplot(y='full name', data = df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "68f8923c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((77, 12), (77,))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['rating']\n",
    "x = df[['calories', 'protein', 'fat', 'sodium', 'fiber', 'carbo', 'sugars', 'potass', 'vitamins', 'shelf', 'weight', 'cups']]\n",
    "x.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a96e62ad",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 OLS Regression Results                                \n",
      "=======================================================================================\n",
      "Dep. Variable:                 rating   R-squared (uncentered):                   0.986\n",
      "Model:                            OLS   Adj. R-squared (uncentered):              0.984\n",
      "Method:                 Least Squares   F-statistic:                              390.8\n",
      "Date:                Fri, 05 Aug 2022   Prob (F-statistic):                    1.13e-55\n",
      "Time:                        13:57:24   Log-Likelihood:                         -236.92\n",
      "No. Observations:                  77   AIC:                                      497.8\n",
      "Df Residuals:                      65   BIC:                                      526.0\n",
      "Df Model:                          12                                                  \n",
      "Covariance Type:            nonrobust                                                  \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "calories      -0.2063      0.106     -1.942      0.056      -0.419       0.006\n",
      "protein        4.2137      0.948      4.443      0.000       2.319       6.108\n",
      "fat           -1.7604      1.168     -1.507      0.137      -4.094       0.573\n",
      "sodium        -0.0548      0.009     -5.890      0.000      -0.073      -0.036\n",
      "fiber          5.0508      0.784      6.446      0.000       3.486       6.616\n",
      "carbo          1.1342      0.327      3.468      0.001       0.481       1.787\n",
      "sugars        -0.8346      0.341     -2.448      0.017      -1.516      -0.154\n",
      "potass        -0.0945      0.027     -3.549      0.001      -0.148      -0.041\n",
      "vitamins      -0.1470      0.034     -4.303      0.000      -0.215      -0.079\n",
      "shelf          3.2104      0.908      3.535      0.001       1.396       5.024\n",
      "weight        33.6329      8.834      3.807      0.000      15.990      51.275\n",
      "cups          16.9256      2.936      5.764      0.000      11.061      22.790\n",
      "==============================================================================\n",
      "Omnibus:                        4.723   Durbin-Watson:                   1.886\n",
      "Prob(Omnibus):                  0.094   Jarque-Bera (JB):                4.651\n",
      "Skew:                          -0.596   Prob(JB):                       0.0977\n",
      "Kurtosis:                       2.828   Cond. No.                     3.12e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
      "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[3] The condition number is large, 3.12e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "mod = sm.OLS(y, x)\n",
    "res = mod.fit()\n",
    "\n",
    "print(res.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "25a65208",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1e7c8ed0550>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='rating', y='sugars', data = df)\n",
    "sns.lmplot(x='rating', y='vitamins', data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3f6f8d7e",
   "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>calories</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.019066</td>\n",
       "      <td>0.498610</td>\n",
       "      <td>0.300649</td>\n",
       "      <td>-0.293413</td>\n",
       "      <td>0.250681</td>\n",
       "      <td>0.562340</td>\n",
       "      <td>-0.066609</td>\n",
       "      <td>0.265356</td>\n",
       "      <td>0.097234</td>\n",
       "      <td>0.696091</td>\n",
       "      <td>0.087200</td>\n",
       "      <td>-0.689376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>protein</th>\n",
       "      <td>0.019066</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.208431</td>\n",
       "      <td>-0.054674</td>\n",
       "      <td>0.500330</td>\n",
       "      <td>-0.130864</td>\n",
       "      <td>-0.329142</td>\n",
       "      <td>0.549407</td>\n",
       "      <td>0.007335</td>\n",
       "      <td>0.133865</td>\n",
       "      <td>0.216158</td>\n",
       "      <td>-0.244469</td>\n",
       "      <td>0.470618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fat</th>\n",
       "      <td>0.498610</td>\n",
       "      <td>0.208431</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005407</td>\n",
       "      <td>0.016719</td>\n",
       "      <td>-0.318043</td>\n",
       "      <td>0.270819</td>\n",
       "      <td>0.193279</td>\n",
       "      <td>-0.031156</td>\n",
       "      <td>0.263691</td>\n",
       "      <td>0.214625</td>\n",
       "      <td>-0.175892</td>\n",
       "      <td>-0.409284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sodium</th>\n",
       "      <td>0.300649</td>\n",
       "      <td>-0.054674</td>\n",
       "      <td>-0.005407</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.070675</td>\n",
       "      <td>0.355983</td>\n",
       "      <td>0.101451</td>\n",
       "      <td>-0.032603</td>\n",
       "      <td>0.361477</td>\n",
       "      <td>-0.069719</td>\n",
       "      <td>0.308576</td>\n",
       "      <td>0.119665</td>\n",
       "      <td>-0.401295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiber</th>\n",
       "      <td>-0.293413</td>\n",
       "      <td>0.500330</td>\n",
       "      <td>0.016719</td>\n",
       "      <td>-0.070675</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.356083</td>\n",
       "      <td>-0.141205</td>\n",
       "      <td>0.903374</td>\n",
       "      <td>-0.032243</td>\n",
       "      <td>0.297539</td>\n",
       "      <td>0.247226</td>\n",
       "      <td>-0.513061</td>\n",
       "      <td>0.584160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>carbo</th>\n",
       "      <td>0.250681</td>\n",
       "      <td>-0.130864</td>\n",
       "      <td>-0.318043</td>\n",
       "      <td>0.355983</td>\n",
       "      <td>-0.356083</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.331665</td>\n",
       "      <td>-0.349685</td>\n",
       "      <td>0.258148</td>\n",
       "      <td>-0.101790</td>\n",
       "      <td>0.135136</td>\n",
       "      <td>0.363932</td>\n",
       "      <td>0.052055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sugars</th>\n",
       "      <td>0.562340</td>\n",
       "      <td>-0.329142</td>\n",
       "      <td>0.270819</td>\n",
       "      <td>0.101451</td>\n",
       "      <td>-0.141205</td>\n",
       "      <td>-0.331665</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.021696</td>\n",
       "      <td>0.125137</td>\n",
       "      <td>0.100438</td>\n",
       "      <td>0.450648</td>\n",
       "      <td>-0.032358</td>\n",
       "      <td>-0.759675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>potass</th>\n",
       "      <td>-0.066609</td>\n",
       "      <td>0.549407</td>\n",
       "      <td>0.193279</td>\n",
       "      <td>-0.032603</td>\n",
       "      <td>0.903374</td>\n",
       "      <td>-0.349685</td>\n",
       "      <td>0.021696</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.020699</td>\n",
       "      <td>0.360663</td>\n",
       "      <td>0.416303</td>\n",
       "      <td>-0.495195</td>\n",
       "      <td>0.380165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vitamins</th>\n",
       "      <td>0.265356</td>\n",
       "      <td>0.007335</td>\n",
       "      <td>-0.031156</td>\n",
       "      <td>0.361477</td>\n",
       "      <td>-0.032243</td>\n",
       "      <td>0.258148</td>\n",
       "      <td>0.125137</td>\n",
       "      <td>0.020699</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.299262</td>\n",
       "      <td>0.320324</td>\n",
       "      <td>0.128405</td>\n",
       "      <td>-0.240544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shelf</th>\n",
       "      <td>0.097234</td>\n",
       "      <td>0.133865</td>\n",
       "      <td>0.263691</td>\n",
       "      <td>-0.069719</td>\n",
       "      <td>0.297539</td>\n",
       "      <td>-0.101790</td>\n",
       "      <td>0.100438</td>\n",
       "      <td>0.360663</td>\n",
       "      <td>0.299262</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.190762</td>\n",
       "      <td>-0.335269</td>\n",
       "      <td>0.025159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weight</th>\n",
       "      <td>0.696091</td>\n",
       "      <td>0.216158</td>\n",
       "      <td>0.214625</td>\n",
       "      <td>0.308576</td>\n",
       "      <td>0.247226</td>\n",
       "      <td>0.135136</td>\n",
       "      <td>0.450648</td>\n",
       "      <td>0.416303</td>\n",
       "      <td>0.320324</td>\n",
       "      <td>0.190762</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.199583</td>\n",
       "      <td>-0.298124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cups</th>\n",
       "      <td>0.087200</td>\n",
       "      <td>-0.244469</td>\n",
       "      <td>-0.175892</td>\n",
       "      <td>0.119665</td>\n",
       "      <td>-0.513061</td>\n",
       "      <td>0.363932</td>\n",
       "      <td>-0.032358</td>\n",
       "      <td>-0.495195</td>\n",
       "      <td>0.128405</td>\n",
       "      <td>-0.335269</td>\n",
       "      <td>-0.199583</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.203160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating</th>\n",
       "      <td>-0.689376</td>\n",
       "      <td>0.470618</td>\n",
       "      <td>-0.409284</td>\n",
       "      <td>-0.401295</td>\n",
       "      <td>0.584160</td>\n",
       "      <td>0.052055</td>\n",
       "      <td>-0.759675</td>\n",
       "      <td>0.380165</td>\n",
       "      <td>-0.240544</td>\n",
       "      <td>0.025159</td>\n",
       "      <td>-0.298124</td>\n",
       "      <td>-0.203160</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          calories   protein       fat    sodium     fiber     carbo  \\\n",
       "calories  1.000000  0.019066  0.498610  0.300649 -0.293413  0.250681   \n",
       "protein   0.019066  1.000000  0.208431 -0.054674  0.500330 -0.130864   \n",
       "fat       0.498610  0.208431  1.000000 -0.005407  0.016719 -0.318043   \n",
       "sodium    0.300649 -0.054674 -0.005407  1.000000 -0.070675  0.355983   \n",
       "fiber    -0.293413  0.500330  0.016719 -0.070675  1.000000 -0.356083   \n",
       "carbo     0.250681 -0.130864 -0.318043  0.355983 -0.356083  1.000000   \n",
       "sugars    0.562340 -0.329142  0.270819  0.101451 -0.141205 -0.331665   \n",
       "potass   -0.066609  0.549407  0.193279 -0.032603  0.903374 -0.349685   \n",
       "vitamins  0.265356  0.007335 -0.031156  0.361477 -0.032243  0.258148   \n",
       "shelf     0.097234  0.133865  0.263691 -0.069719  0.297539 -0.101790   \n",
       "weight    0.696091  0.216158  0.214625  0.308576  0.247226  0.135136   \n",
       "cups      0.087200 -0.244469 -0.175892  0.119665 -0.513061  0.363932   \n",
       "rating   -0.689376  0.470618 -0.409284 -0.401295  0.584160  0.052055   \n",
       "\n",
       "            sugars    potass  vitamins     shelf    weight      cups    rating  \n",
       "calories  0.562340 -0.066609  0.265356  0.097234  0.696091  0.087200 -0.689376  \n",
       "protein  -0.329142  0.549407  0.007335  0.133865  0.216158 -0.244469  0.470618  \n",
       "fat       0.270819  0.193279 -0.031156  0.263691  0.214625 -0.175892 -0.409284  \n",
       "sodium    0.101451 -0.032603  0.361477 -0.069719  0.308576  0.119665 -0.401295  \n",
       "fiber    -0.141205  0.903374 -0.032243  0.297539  0.247226 -0.513061  0.584160  \n",
       "carbo    -0.331665 -0.349685  0.258148 -0.101790  0.135136  0.363932  0.052055  \n",
       "sugars    1.000000  0.021696  0.125137  0.100438  0.450648 -0.032358 -0.759675  \n",
       "potass    0.021696  1.000000  0.020699  0.360663  0.416303 -0.495195  0.380165  \n",
       "vitamins  0.125137  0.020699  1.000000  0.299262  0.320324  0.128405 -0.240544  \n",
       "shelf     0.100438  0.360663  0.299262  1.000000  0.190762 -0.335269  0.025159  \n",
       "weight    0.450648  0.416303  0.320324  0.190762  1.000000 -0.199583 -0.298124  \n",
       "cups     -0.032358 -0.495195  0.128405 -0.335269 -0.199583  1.000000 -0.203160  \n",
       "rating   -0.759675  0.380165 -0.240544  0.025159 -0.298124 -0.203160  1.000000  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = df.corr(method='pearson')\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6e9d339",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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