week8/day3/Lab | random forest regressor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing cookies.csv\n",
    "df = pd.read_csv('cookies.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>sugar to flour ratio</th>\n",
       "      <th>sugar index</th>\n",
       "      <th>bake temp</th>\n",
       "      <th>chill time</th>\n",
       "      <th>calories</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>grams baking soda</th>\n",
       "      <th>bake time</th>\n",
       "      <th>quality</th>\n",
       "      <th>butter type</th>\n",
       "      <th>weight</th>\n",
       "      <th>diameter</th>\n",
       "      <th>mixins</th>\n",
       "      <th>crunch factor</th>\n",
       "      <th>aesthetic appeal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.25</td>\n",
       "      <td>9.5</td>\n",
       "      <td>300</td>\n",
       "      <td>15.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>0.99367</td>\n",
       "      <td>8.10</td>\n",
       "      <td>0.44</td>\n",
       "      <td>12.1</td>\n",
       "      <td>8</td>\n",
       "      <td>melted</td>\n",
       "      <td>15.2</td>\n",
       "      <td>7</td>\n",
       "      <td>raisins</td>\n",
       "      <td>1.30</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.23</td>\n",
       "      <td>3.3</td>\n",
       "      <td>520</td>\n",
       "      <td>34.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>0.99429</td>\n",
       "      <td>8.16</td>\n",
       "      <td>0.48</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7</td>\n",
       "      <td>melted</td>\n",
       "      <td>12.4</td>\n",
       "      <td>7</td>\n",
       "      <td>raisins</td>\n",
       "      <td>1.71</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.18</td>\n",
       "      <td>1.9</td>\n",
       "      <td>360</td>\n",
       "      <td>33.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>0.98746</td>\n",
       "      <td>8.21</td>\n",
       "      <td>0.83</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9</td>\n",
       "      <td>melted</td>\n",
       "      <td>9.4</td>\n",
       "      <td>7</td>\n",
       "      <td>nuts, chocolate</td>\n",
       "      <td>1.78</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.18</td>\n",
       "      <td>10.5</td>\n",
       "      <td>490</td>\n",
       "      <td>41.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>0.99630</td>\n",
       "      <td>8.14</td>\n",
       "      <td>0.35</td>\n",
       "      <td>10.5</td>\n",
       "      <td>7</td>\n",
       "      <td>melted</td>\n",
       "      <td>12.2</td>\n",
       "      <td>7</td>\n",
       "      <td>chocolate</td>\n",
       "      <td>1.59</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.24</td>\n",
       "      <td>2.4</td>\n",
       "      <td>770</td>\n",
       "      <td>6.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.99740</td>\n",
       "      <td>8.09</td>\n",
       "      <td>0.57</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "      <td>cubed</td>\n",
       "      <td>19.8</td>\n",
       "      <td>7</td>\n",
       "      <td>nuts, oats, chocolate</td>\n",
       "      <td>1.30</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sugar to flour ratio  sugar index  bake temp  chill time  calories  \\\n",
       "0                  0.25          9.5        300        15.0     136.0   \n",
       "1                  0.23          3.3        520        34.0     113.0   \n",
       "2                  0.18          1.9        360        33.0     106.0   \n",
       "3                  0.18         10.5        490        41.0     124.0   \n",
       "4                  0.24          2.4        770         6.0      33.0   \n",
       "\n",
       "   density    pH  grams baking soda  bake time  quality butter type  weight  \\\n",
       "0  0.99367  8.10               0.44       12.1        8      melted    15.2   \n",
       "1  0.99429  8.16               0.48        8.4        7      melted    12.4   \n",
       "2  0.98746  8.21               0.83       14.0        9      melted     9.4   \n",
       "3  0.99630  8.14               0.35       10.5        7      melted    12.2   \n",
       "4  0.99740  8.09               0.57        9.4        5       cubed    19.8   \n",
       "\n",
       "   diameter                 mixins  crunch factor  aesthetic appeal  \n",
       "0         7                raisins           1.30                 3  \n",
       "1         7                raisins           1.71                 3  \n",
       "2         7        nuts, chocolate           1.78                 3  \n",
       "3         7              chocolate           1.59                 3  \n",
       "4         7  nuts, oats, chocolate           1.30                 3  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# taking a look at the content of the dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis and Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5198 entries, 0 to 5197\n",
      "Data columns (total 16 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   sugar to flour ratio  5198 non-null   float64\n",
      " 1   sugar index           5193 non-null   float64\n",
      " 2   bake temp             5198 non-null   int64  \n",
      " 3   chill time            5198 non-null   float64\n",
      " 4   calories              5198 non-null   float64\n",
      " 5   density               5198 non-null   float64\n",
      " 6   pH                    5198 non-null   float64\n",
      " 7   grams baking soda     5198 non-null   float64\n",
      " 8   bake time             5188 non-null   float64\n",
      " 9   quality               5198 non-null   int64  \n",
      " 10  butter type           5198 non-null   object \n",
      " 11  weight                5198 non-null   float64\n",
      " 12  diameter              5198 non-null   int64  \n",
      " 13  mixins                5196 non-null   object \n",
      " 14  crunch factor         5198 non-null   float64\n",
      " 15  aesthetic appeal      5198 non-null   int64  \n",
      "dtypes: float64(10), int64(4), object(2)\n",
      "memory usage: 649.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# exploring data info\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sugar to flour ratio    0.000000\n",
       "sugar index             0.096191\n",
       "bake temp               0.000000\n",
       "chill time              0.000000\n",
       "calories                0.000000\n",
       "density                 0.000000\n",
       "pH                      0.000000\n",
       "grams baking soda       0.000000\n",
       "bake time               0.192382\n",
       "quality                 0.000000\n",
       "butter type             0.000000\n",
       "weight                  0.000000\n",
       "diameter                0.000000\n",
       "mixins                  0.038476\n",
       "crunch factor           0.000000\n",
       "aesthetic appeal        0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking null values\n",
    "df.isnull().sum()*100/len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As the percentage of nulls is lower than 1%, I'll delete the rows containing nulls.\n",
    "df= df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5181, 16)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bake time "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5181.000000\n",
       "mean       10.495561\n",
       "std         1.194958\n",
       "min         8.000000\n",
       "25%         9.500000\n",
       "50%        10.300000\n",
       "75%        11.300000\n",
       "max        14.900000\n",
       "Name: bake time, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['bake time'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df[['quality', 'bake time', 'bake temp']]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 6))\n",
    "mask = np.triu(np.ones_like(df.corr(), dtype=np.bool))\n",
    "heatmap = sns.heatmap(df.corr(), mask=mask, vmin=-1, vmax=1, annot=True, cmap='BrBG')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sugar to flour ratio --> 0.17992898765039828\n",
      "sugar index --> 0.21995856817996384\n",
      "bake temp --> -0.470895323707292\n",
      "chill time --> 0.3649248978298107\n",
      "calories --> 0.4400803834300938\n",
      "density --> -0.017523118224988726\n",
      "pH --> -0.11372471128992226\n",
      "grams baking soda --> -0.3062510398576623\n",
      "bake time --> 0.315445216360322\n",
      "quality --> 1.0\n",
      "weight --> -0.3285339469570104\n",
      "diameter --> nan\n",
      "crunch factor --> 0.008345525940834769\n",
      "aesthetic appeal --> 0.00014270685418785024\n"
     ]
    }
   ],
   "source": [
    "num_cols = df.select_dtypes(include=[\"int64\", \"float64\"]).columns\n",
    "\n",
    "for column in num_cols:\n",
    "    print(column, '-->', df['quality'].corr(df[column]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## butter type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "melted    3908\n",
       "cubed     1273\n",
       "Name: butter type, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['butter type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## aesthetic appeal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    5178\n",
       "4       3\n",
       "Name: aesthetic appeal, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['aesthetic appeal'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thoughts on aesthetics : \n",
    "    not many values - drop column "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5181.000000\n",
       "mean       14.383922\n",
       "std         3.027030\n",
       "min       -99.000000\n",
       "25%        12.800000\n",
       "50%        14.000000\n",
       "75%        15.400000\n",
       "max        31.800000\n",
       "Name: weight, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['weight'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 15.2,  12.4,   9.4,  12.2,  19.8,  13.2,  14.2,  13.8,  12.6,\n",
       "        13. ,  12. ,  14.6,  15.6,  14. ,  13.4,  16.8,  14.8,  13.6,\n",
       "        16. ,  11. ,  18.6,  10.6,  18.2,  15.8,  17.2,  14.3,  10.4,\n",
       "        11.8,  11.6,  15.4,  21.6,  12.8,  20.2,  14.4,  12.3,  23.8,\n",
       "        11.2,  11.4,  15. ,  19.6,  17.4,  16.4,  19.2,  10.8,  18. ,\n",
       "        26. ,  16.6,  16.2,  19. ,  10. ,  21.4,  20.4,  17. ,  31.2,\n",
       "        17.8,  22.6,   9.8,  23. ,  18.8,  26.6,  20.8,  26.4,  10.2,\n",
       "        22.2,  23.4,  18.4,  17.6,  28.4,  24.6,  20.6,  25. ,  24.2,\n",
       "        22.8,  24. ,  23.2,  24.4,  26.8,  22.4,  19.4,  23.6,  27.4,\n",
       "        28. ,  22. ,  21.8,  25.2,  25.8,  20. ,  21.2,  21. , -99. ,\n",
       "        25.6,  31. ,   9. ,  24.8,  25.4,   9.6,   8.8,  30. ,  12.9,\n",
       "         9.2,  31.8,   7.6,   7.8,   8.4,  28.6])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['weight'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thoughts on weight : \n",
    "\n",
    "    Remove -99 value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## diameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['diameter'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thoughts on diameter : \n",
    "\n",
    "    All equal to 7 - column can be dropped "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## bake time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5181.000000\n",
       "mean       10.495561\n",
       "std         1.194958\n",
       "min         8.000000\n",
       "25%         9.500000\n",
       "50%        10.300000\n",
       "75%        11.300000\n",
       "max        14.900000\n",
       "Name: bake time, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['bake time'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: quality, dtype: int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['bake time'].isnull()]['quality']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thoughts on Bake time \n",
    "\n",
    "    judging by the spread of nulls across quality bands, any missing values could be replaced by mean or rows dropped "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## crunch factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"ticks\")\n",
    "plt.hist(df['crunch factor'], bins=15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"dark\")\n",
    "m1 = sns.distplot(df['crunch factor'], bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5181 entries, 0 to 5197\n",
      "Data columns (total 16 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   sugar to flour ratio  5181 non-null   float64\n",
      " 1   sugar index           5181 non-null   float64\n",
      " 2   bake temp             5181 non-null   int64  \n",
      " 3   chill time            5181 non-null   float64\n",
      " 4   calories              5181 non-null   float64\n",
      " 5   density               5181 non-null   float64\n",
      " 6   pH                    5181 non-null   float64\n",
      " 7   grams baking soda     5181 non-null   float64\n",
      " 8   bake time             5181 non-null   float64\n",
      " 9   quality               5181 non-null   int64  \n",
      " 10  butter type           5181 non-null   object \n",
      " 11  weight                5181 non-null   float64\n",
      " 12  diameter              5181 non-null   int64  \n",
      " 13  mixins                5181 non-null   object \n",
      " 14  crunch factor         5181 non-null   float64\n",
      " 15  aesthetic appeal      5181 non-null   int64  \n",
      "dtypes: float64(10), int64(4), object(2)\n",
      "memory usage: 688.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## mixins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                     raisins\n",
       "1                     raisins\n",
       "2             nuts, chocolate\n",
       "3                   chocolate\n",
       "4       nuts, oats, chocolate\n",
       "                ...          \n",
       "5193            peanut butter\n",
       "5194                  raisins\n",
       "5195          chocolate, oats\n",
       "5196                chocolate\n",
       "5197                chocolate\n",
       "Name: mixins, Length: 5181, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['mixins']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['raisins', 'nuts, chocolate', 'chocolate', 'nuts, oats, chocolate',\n",
       "       'chocolate, oats', 'raisins, oats', 'nuts,raisins', 'nuts, oats',\n",
       "       'chocolate, peanut butter', 'chocolate, oats, peanut butter',\n",
       "       'peanut butter', 'peanut butter, raisins', 'oats'], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['mixins'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['mixins'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "chocolate                         1892\n",
       "raisins                           1196\n",
       "chocolate, oats                    745\n",
       "nuts, chocolate                    519\n",
       "nuts,raisins                       331\n",
       "nuts, oats, chocolate              293\n",
       "nuts, oats                         107\n",
       "chocolate, peanut butter            52\n",
       "raisins, oats                       24\n",
       "peanut butter                       15\n",
       "oats                                 4\n",
       "chocolate, oats, peanut butter       2\n",
       "peanut butter, raisins               1\n",
       "Name: mixins, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['mixins'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wrangling the data / making small changes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=[\"aesthetic appeal\", \"diameter\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove -99\n",
    "df = df.drop(df[df['weight'] == -99].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.dropna(subset=['mixins'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5180 entries, 0 to 5197\n",
      "Data columns (total 14 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   sugar to flour ratio  5180 non-null   float64\n",
      " 1   sugar index           5180 non-null   float64\n",
      " 2   bake temp             5180 non-null   int64  \n",
      " 3   chill time            5180 non-null   float64\n",
      " 4   calories              5180 non-null   float64\n",
      " 5   density               5180 non-null   float64\n",
      " 6   pH                    5180 non-null   float64\n",
      " 7   grams baking soda     5180 non-null   float64\n",
      " 8   bake time             5180 non-null   float64\n",
      " 9   quality               5180 non-null   int64  \n",
      " 10  butter type           5180 non-null   object \n",
      " 11  weight                5180 non-null   float64\n",
      " 12  mixins                5180 non-null   object \n",
      " 13  crunch factor         5180 non-null   float64\n",
      "dtypes: float64(10), int64(2), object(2)\n",
      "memory usage: 607.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['chocolate'] = 0\n",
    "df['raisins'] = 0\n",
    "df['oats'] = 0\n",
    "df['nuts'] = 0\n",
    "df['peanut butter'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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",
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       "    }\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>sugar to flour ratio</th>\n",
       "      <th>sugar index</th>\n",
       "      <th>bake temp</th>\n",
       "      <th>chill time</th>\n",
       "      <th>calories</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>grams baking soda</th>\n",
       "      <th>bake time</th>\n",
       "      <th>quality</th>\n",
       "      <th>butter type</th>\n",
       "      <th>weight</th>\n",
       "      <th>mixins</th>\n",
       "      <th>crunch factor</th>\n",
       "      <th>chocolate</th>\n",
       "      <th>raisins</th>\n",
       "      <th>oats</th>\n",
       "      <th>nuts</th>\n",
       "      <th>peanut butter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.25</td>\n",
       "      <td>9.5</td>\n",
       "      <td>300</td>\n",
       "      <td>15.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>0.99367</td>\n",
       "      <td>8.10</td>\n",
       "      <td>0.44</td>\n",
       "      <td>12.1</td>\n",
       "      <td>8</td>\n",
       "      <td>melted</td>\n",
       "      <td>15.2</td>\n",
       "      <td>raisins</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.23</td>\n",
       "      <td>3.3</td>\n",
       "      <td>520</td>\n",
       "      <td>34.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>0.99429</td>\n",
       "      <td>8.16</td>\n",
       "      <td>0.48</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7</td>\n",
       "      <td>melted</td>\n",
       "      <td>12.4</td>\n",
       "      <td>raisins</td>\n",
       "      <td>1.71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.18</td>\n",
       "      <td>1.9</td>\n",
       "      <td>360</td>\n",
       "      <td>33.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>0.98746</td>\n",
       "      <td>8.21</td>\n",
       "      <td>0.83</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9</td>\n",
       "      <td>melted</td>\n",
       "      <td>9.4</td>\n",
       "      <td>nuts, chocolate</td>\n",
       "      <td>1.78</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.18</td>\n",
       "      <td>10.5</td>\n",
       "      <td>490</td>\n",
       "      <td>41.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>0.99630</td>\n",
       "      <td>8.14</td>\n",
       "      <td>0.35</td>\n",
       "      <td>10.5</td>\n",
       "      <td>7</td>\n",
       "      <td>melted</td>\n",
       "      <td>12.2</td>\n",
       "      <td>chocolate</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.24</td>\n",
       "      <td>2.4</td>\n",
       "      <td>770</td>\n",
       "      <td>6.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.99740</td>\n",
       "      <td>8.09</td>\n",
       "      <td>0.57</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "      <td>cubed</td>\n",
       "      <td>19.8</td>\n",
       "      <td>nuts, oats, chocolate</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sugar to flour ratio  sugar index  bake temp  chill time  calories  \\\n",
       "0                  0.25          9.5        300        15.0     136.0   \n",
       "1                  0.23          3.3        520        34.0     113.0   \n",
       "2                  0.18          1.9        360        33.0     106.0   \n",
       "3                  0.18         10.5        490        41.0     124.0   \n",
       "4                  0.24          2.4        770         6.0      33.0   \n",
       "\n",
       "   density    pH  grams baking soda  bake time  quality butter type  weight  \\\n",
       "0  0.99367  8.10               0.44       12.1        8      melted    15.2   \n",
       "1  0.99429  8.16               0.48        8.4        7      melted    12.4   \n",
       "2  0.98746  8.21               0.83       14.0        9      melted     9.4   \n",
       "3  0.99630  8.14               0.35       10.5        7      melted    12.2   \n",
       "4  0.99740  8.09               0.57        9.4        5       cubed    19.8   \n",
       "\n",
       "                  mixins  crunch factor  chocolate  raisins  oats  nuts  \\\n",
       "0                raisins           1.30          0        0     0     0   \n",
       "1                raisins           1.71          0        0     0     0   \n",
       "2        nuts, chocolate           1.78          0        0     0     0   \n",
       "3              chocolate           1.59          0        0     0     0   \n",
       "4  nuts, oats, chocolate           1.30          0        0     0     0   \n",
       "\n",
       "   peanut butter  \n",
       "0              0  \n",
       "1              0  \n",
       "2              0  \n",
       "3              0  \n",
       "4              0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "mixins = ['chocolate', 'raisins', 'oats', 'nuts', 'peanut butter']\n",
    "for mix in mixins:\n",
    "    for i in range(len(df)):\n",
    "        if mix in [x.strip() for x in df.loc[i, 'mixins'].split(',')]:\n",
    "            df.loc[i, mix] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>sugar to flour ratio</th>\n",
       "      <th>sugar index</th>\n",
       "      <th>bake temp</th>\n",
       "      <th>chill time</th>\n",
       "      <th>calories</th>\n",
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       "      <th>mixins</th>\n",
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       "      <th>oats</th>\n",
       "      <th>nuts</th>\n",
       "      <th>peanut butter</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.25</td>\n",
       "      <td>9.5</td>\n",
       "      <td>300</td>\n",
       "      <td>15.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>0.99367</td>\n",
       "      <td>8.10</td>\n",
       "      <td>0.44</td>\n",
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       "      <td>15.2</td>\n",
       "      <td>raisins</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.23</td>\n",
       "      <td>3.3</td>\n",
       "      <td>520</td>\n",
       "      <td>34.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>0.99429</td>\n",
       "      <td>8.16</td>\n",
       "      <td>0.48</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7</td>\n",
       "      <td>melted</td>\n",
       "      <td>12.4</td>\n",
       "      <td>raisins</td>\n",
       "      <td>1.71</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.18</td>\n",
       "      <td>1.9</td>\n",
       "      <td>360</td>\n",
       "      <td>33.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>0.98746</td>\n",
       "      <td>8.21</td>\n",
       "      <td>0.83</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9</td>\n",
       "      <td>melted</td>\n",
       "      <td>9.4</td>\n",
       "      <td>nuts, chocolate</td>\n",
       "      <td>1.78</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.18</td>\n",
       "      <td>10.5</td>\n",
       "      <td>490</td>\n",
       "      <td>41.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>0.99630</td>\n",
       "      <td>8.14</td>\n",
       "      <td>0.35</td>\n",
       "      <td>10.5</td>\n",
       "      <td>7</td>\n",
       "      <td>melted</td>\n",
       "      <td>12.2</td>\n",
       "      <td>chocolate</td>\n",
       "      <td>1.59</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.24</td>\n",
       "      <td>2.4</td>\n",
       "      <td>770</td>\n",
       "      <td>6.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.99740</td>\n",
       "      <td>8.09</td>\n",
       "      <td>0.57</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "      <td>cubed</td>\n",
       "      <td>19.8</td>\n",
       "      <td>nuts, oats, chocolate</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sugar to flour ratio  sugar index  bake temp  chill time  calories  \\\n",
       "0                  0.25          9.5        300        15.0     136.0   \n",
       "1                  0.23          3.3        520        34.0     113.0   \n",
       "2                  0.18          1.9        360        33.0     106.0   \n",
       "3                  0.18         10.5        490        41.0     124.0   \n",
       "4                  0.24          2.4        770         6.0      33.0   \n",
       "\n",
       "   density    pH  grams baking soda  bake time  quality butter type  weight  \\\n",
       "0  0.99367  8.10               0.44       12.1        8      melted    15.2   \n",
       "1  0.99429  8.16               0.48        8.4        7      melted    12.4   \n",
       "2  0.98746  8.21               0.83       14.0        9      melted     9.4   \n",
       "3  0.99630  8.14               0.35       10.5        7      melted    12.2   \n",
       "4  0.99740  8.09               0.57        9.4        5       cubed    19.8   \n",
       "\n",
       "                  mixins  crunch factor  chocolate  raisins  oats  nuts  \\\n",
       "0                raisins           1.30          0        1     0     0   \n",
       "1                raisins           1.71          0        1     0     0   \n",
       "2        nuts, chocolate           1.78          1        0     0     1   \n",
       "3              chocolate           1.59          1        0     0     0   \n",
       "4  nuts, oats, chocolate           1.30          1        0     1     1   \n",
       "\n",
       "   peanut butter  \n",
       "0              0  \n",
       "1              0  \n",
       "2              0  \n",
       "3              0  \n",
       "4              0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=[\"mixins\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 6))\n",
    "mask = np.triu(np.ones_like(df.corr(), dtype=np.bool))\n",
    "heatmap = sns.heatmap(df.corr(), mask=mask, vmin=-1, vmax=1, annot=True, cmap='BrBG')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pre processing- drop NANs and deal with non numerical data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sugar to flour ratio    float64\n",
       "sugar index             float64\n",
       "bake temp                 int64\n",
       "chill time              float64\n",
       "calories                float64\n",
       "density                 float64\n",
       "pH                      float64\n",
       "grams baking soda       float64\n",
       "bake time               float64\n",
       "quality                   int64\n",
       "butter type              object\n",
       "weight                  float64\n",
       "crunch factor           float64\n",
       "chocolate                 int64\n",
       "raisins                   int64\n",
       "oats                      int64\n",
       "nuts                      int64\n",
       "peanut butter             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.get_dummies(df, columns=['butter type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5180 entries, 0 to 5179\n",
      "Data columns (total 19 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   sugar to flour ratio  5180 non-null   float64\n",
      " 1   sugar index           5180 non-null   float64\n",
      " 2   bake temp             5180 non-null   int64  \n",
      " 3   chill time            5180 non-null   float64\n",
      " 4   calories              5180 non-null   float64\n",
      " 5   density               5180 non-null   float64\n",
      " 6   pH                    5180 non-null   float64\n",
      " 7   grams baking soda     5180 non-null   float64\n",
      " 8   bake time             5180 non-null   float64\n",
      " 9   quality               5180 non-null   int64  \n",
      " 10  weight                5180 non-null   float64\n",
      " 11  crunch factor         5180 non-null   float64\n",
      " 12  chocolate             5180 non-null   int64  \n",
      " 13  raisins               5180 non-null   int64  \n",
      " 14  oats                  5180 non-null   int64  \n",
      " 15  nuts                  5180 non-null   int64  \n",
      " 16  peanut butter         5180 non-null   int64  \n",
      " 17  butter type_cubed     5180 non-null   uint8  \n",
      " 18  butter type_melted    5180 non-null   uint8  \n",
      "dtypes: float64(10), int64(7), uint8(2)\n",
      "memory usage: 698.2 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split data into dependent and independent variables "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "X= df.drop(columns=\"quality\")\n",
    "y=df.quality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing - scaling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "X_scaled = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split( X_scaled, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get model, fit, make predictions  AND evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "lm = linear_model.LinearRegression()\n",
    "model = lm.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_preds  = lm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.66013667, 7.82612177, 8.6221728 , ..., 8.4027209 , 7.25690791,\n",
       "       8.2478747 ])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regression metrics on the test set\n",
      "R2 score: 0.6458913148801371\n",
      "MAE: 0.5793221761789332\n",
      "MSE: 0.5671998985426202\n"
     ]
    }
   ],
   "source": [
    "print(\"Regression metrics on the test set\")\n",
    "print(f\"R2 score: {r2_score(y_test, y_preds)}\")\n",
    "print(f\"MAE: {mean_absolute_error(y_test, y_preds)}\")\n",
    "print(f\"MSE: {mean_squared_error(y_test, y_preds)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.753126747993072\n"
     ]
    }
   ],
   "source": [
    "import math \n",
    "rmse = math.sqrt(mean_squared_error(y_test, y_preds))\n",
    "print(rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make predictions on the cookies_validate dataset (without labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "cookies_val = pd.read_csv('cookies_validate.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 779 entries, 0 to 778\n",
      "Data columns (total 17 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   id                    779 non-null    int64  \n",
      " 1   sugar to flour ratio  779 non-null    float64\n",
      " 2   sugar index           779 non-null    float64\n",
      " 3   bake temp             779 non-null    float64\n",
      " 4   chill time            779 non-null    float64\n",
      " 5   calories              779 non-null    float64\n",
      " 6   density               779 non-null    float64\n",
      " 7   pH                    779 non-null    float64\n",
      " 8   grams baking soda     779 non-null    float64\n",
      " 9   bake time             779 non-null    float64\n",
      " 10  quality               779 non-null    int64  \n",
      " 11  butter type           779 non-null    object \n",
      " 12  weight                779 non-null    float64\n",
      " 13  diameter              779 non-null    int64  \n",
      " 14  mixins                779 non-null    object \n",
      " 15  crunch factor         779 non-null    float64\n",
      " 16  aesthetic appeal      779 non-null    int64  \n",
      "dtypes: float64(11), int64(4), object(2)\n",
      "memory usage: 103.6+ KB\n"
     ]
    }
   ],
   "source": [
    "cookies_val.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "cookies_val.drop(columns=[\"aesthetic appeal\", \"diameter\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "cookies_val['chocolate'] = 0\n",
    "cookies_val['raisins'] = 0\n",
    "cookies_val['oats'] = 0\n",
    "cookies_val['nuts'] = 0\n",
    "cookies_val['peanut butter'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "mixins = ['chocolate', 'raisins', 'oats', 'nuts', 'peanut butter']\n",
    "for mix in mixins:\n",
    "    for i in range(len(cookies_val)):\n",
    "        if mix in [x.strip() for x in cookies_val.loc[i, 'mixins'].split(',')]:\n",
    "            cookies_val.loc[i, mix] = 1\n",
    "            \n",
    "cookies_val.drop(columns=[\"mixins\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "cookies_val.drop(columns= 'id', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "cookies_val=pd.get_dummies(cookies_val, columns=['butter type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 779 entries, 0 to 778\n",
      "Data columns (total 19 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   sugar to flour ratio  779 non-null    float64\n",
      " 1   sugar index           779 non-null    float64\n",
      " 2   bake temp             779 non-null    float64\n",
      " 3   chill time            779 non-null    float64\n",
      " 4   calories              779 non-null    float64\n",
      " 5   density               779 non-null    float64\n",
      " 6   pH                    779 non-null    float64\n",
      " 7   grams baking soda     779 non-null    float64\n",
      " 8   bake time             779 non-null    float64\n",
      " 9   quality               779 non-null    int64  \n",
      " 10  weight                779 non-null    float64\n",
      " 11  crunch factor         779 non-null    float64\n",
      " 12  chocolate             779 non-null    int64  \n",
      " 13  raisins               779 non-null    int64  \n",
      " 14  oats                  779 non-null    int64  \n",
      " 15  nuts                  779 non-null    int64  \n",
      " 16  peanut butter         779 non-null    int64  \n",
      " 17  butter type_cubed     779 non-null    uint8  \n",
      " 18  butter type_melted    779 non-null    uint8  \n",
      "dtypes: float64(11), int64(6), uint8(2)\n",
      "memory usage: 105.1 KB\n"
     ]
    }
   ],
   "source": [
    "cookies_val.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_val = cookies_val.drop(columns=\"quality\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_val_scaled = scaler.fit_transform(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_val=lm.predict(X_val_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.4384112 , 7.30083796, 7.48278376, 7.72783991, 7.21337458,\n",
       "       7.16881892, 7.48746821, 7.10062739, 7.69597956, 7.3107104 ,\n",
       "       7.43253657, 7.35233637, 7.4369311 , 7.63616511, 7.90853449,\n",
       "       7.15529963, 7.52462336, 7.11580988, 8.24450251, 7.94005915,\n",
       "       7.58190486, 7.59453913, 8.01586482, 7.67744013, 6.91346308,\n",
       "       6.92553278, 7.00936457, 7.44593379, 6.9064593 , 7.26232478,\n",
       "       7.61481806, 7.87470576, 7.40798518, 7.47079035, 7.76732966,\n",
       "       7.85308405, 7.8544726 , 7.72332331, 7.10650202, 7.42254206,\n",
       "       7.92519709, 7.63369319, 7.47867915, 7.12482783, 7.43468806,\n",
       "       7.31180903, 7.15769526, 7.85657832, 7.53422114, 7.13781306,\n",
       "       7.0888476 , 7.04542109, 7.36048457, 7.16183039, 7.03318354,\n",
       "       7.9411883 , 7.27985713, 7.33674189, 7.91248652, 8.02175471,\n",
       "       7.13987299, 7.47837397, 6.76980158, 7.59763667, 7.48496577,\n",
       "       7.34002253, 7.4533343 , 7.27607295, 7.01275202, 8.17301508,\n",
       "       7.86846491, 8.18694636, 7.13892695, 6.83790156, 7.73374506,\n",
       "       7.7925677 , 7.59284541, 7.45960566, 7.02848383, 7.58790156,\n",
       "       7.26267573, 7.86703059, 7.40752741, 7.14800593, 7.64573237,\n",
       "       7.88804194, 7.88616511, 7.52012202, 7.5583758 , 8.08019587,\n",
       "       7.74235102, 6.85163447, 7.60174128, 7.28437373, 7.46477839,\n",
       "       7.3910174 , 7.41421076, 7.93862483, 7.40630671, 6.99709651,\n",
       "       7.89428278, 7.49956843, 8.02166316, 8.10639521, 7.87185236,\n",
       "       7.46709773, 7.09279963, 7.04003474, 7.35970637, 7.95580622,\n",
       "       7.48868891, 6.85161921, 7.17599055, 7.25411555, 7.53547236,\n",
       "       7.23609492, 7.44028803, 7.45084712, 8.21628901, 7.10157343,\n",
       "       7.62778803, 7.43308589, 7.36638972, 7.63894221, 7.61371943,\n",
       "       7.43525263, 6.92313715, 7.50867793, 6.64884516, 7.599956  ,\n",
       "       6.357326  , 7.89599177, 7.44649836, 7.38694331, 7.01543757,\n",
       "       7.92559382, 7.17832514, 7.15894648, 7.40017268, 7.39149043,\n",
       "       7.5538592 , 7.54072138, 7.44831416, 7.29067561, 7.11693903,\n",
       "       7.05660578, 7.21207758, 8.11352107, 7.60969111, 7.78791377,\n",
       "       6.76842829, 7.49419734, 7.47777888, 7.20969721, 7.2549853 ,\n",
       "       7.26180598, 7.47848078, 7.23742243, 7.93963191, 8.1499438 ,\n",
       "       7.99474665, 7.49383112, 7.35631892, 8.1884875 , 7.46264216,\n",
       "       7.51011225, 7.50080439, 7.54770991, 7.48083064, 8.27636286,\n",
       "       7.26093623, 7.66993281, 7.15940424, 7.32285639, 7.8660998 ,\n",
       "       8.01197382, 7.70766779, 7.45600459, 7.50478694, 7.96186396,\n",
       "       7.11680171, 6.94532343, 6.83201167, 7.13119074, 7.27796504,\n",
       "       7.04566523, 7.48885676, 7.07856318, 7.09220454, 7.67455622,\n",
       "       6.63711115, 7.17243525, 7.66727778, 6.41353938, 7.6178393 ,\n",
       "       7.6493487 , 7.48551508, 7.49583003, 8.08134028, 7.74993464,\n",
       "       7.50278803, 8.02358576, 8.05703303, 6.4938769 , 7.99987361,\n",
       "       7.27741572, 7.23607966, 7.26031062, 7.45223567, 7.68500849,\n",
       "       7.38448664, 6.69851252, 7.60637995, 7.25016352, 7.11086604,\n",
       "       7.53356501, 7.53708979, 7.6471667 , 7.33985468, 6.73257014,\n",
       "       6.71779963, 7.29513117, 7.28782221, 7.25643488, 7.11925837,\n",
       "       7.19416377, 7.47835871, 6.87833735, 7.57216975, 7.64747187,\n",
       "       7.59327265, 7.09604975, 7.22033259, 7.30535456, 7.22938105,\n",
       "       8.07551142, 8.05189082, 6.98241755, 8.08867976, 7.56331965,\n",
       "       7.27479121, 7.71334406, 7.28918025, 7.02337214, 7.36402461,\n",
       "       7.00702998, 7.36626765, 7.64171931, 7.48031184, 7.43427607,\n",
       "       7.61500117, 7.15482661, 6.82062861, 7.74752375, 7.47291132,\n",
       "       8.11594721, 7.2547259 , 7.11268183, 7.45975825, 7.91024348,\n",
       "       8.4468188 , 6.77474543, 6.98134944, 8.40836665, 7.45649287,\n",
       "       7.07627436, 7.30736872, 7.54339167, 7.48197505, 7.85305354,\n",
       "       8.02202937, 7.90241572, 7.56536433, 6.77599665, 8.09073969,\n",
       "       7.36411616, 7.67913386, 8.64730402, 8.32410761, 7.86474177,\n",
       "       7.24552485, 7.55161616, 6.93069026, 7.62801692, 7.82836482,\n",
       "       7.83274409, 9.16143366, 7.28426692, 8.51838252, 7.12568232,\n",
       "       8.13105341, 7.63064143, 7.44445368, 7.41579768, 7.35253474,\n",
       "       8.0377917 , 8.08753535, 7.47812983, 7.02885005, 7.88700434,\n",
       "       7.357326  , 7.73452326, 7.37414118, 6.54133173, 7.89574763,\n",
       "       7.75075861, 8.25663325, 8.24991938, 7.91759821, 7.99731013,\n",
       "       8.55660578, 7.78141352, 8.25468012, 7.41059443, 8.09087702,\n",
       "       6.87858149, 7.7814593 , 7.02865168, 7.92243525, 8.2018542 ,\n",
       "       7.09112116, 7.9770312 , 7.97117182, 7.86828181, 7.80128047,\n",
       "       7.59635493, 8.49160334, 7.61849543, 7.63515803, 8.1947436 ,\n",
       "       6.97060725, 8.04996821, 7.70858332, 8.20270869, 7.9717364 ,\n",
       "       7.66715571, 7.82075068, 7.88950678, 8.25002619, 7.77375361,\n",
       "       7.93081233, 7.55010554, 7.57415339, 7.90003535, 7.90236994,\n",
       "       7.4205279 , 7.99911067, 7.13593623, 7.52204463, 6.95156428,\n",
       "       7.85924861, 7.84308955, 7.80248591, 8.19684931, 8.08907649,\n",
       "       7.85738703, 7.70797297, 7.41811701, 7.92579218, 8.22393366,\n",
       "       7.81437251, 8.0722613 , 7.54641291, 7.81202265, 8.07764765,\n",
       "       7.66912409, 7.3053393 , 7.60067317, 8.20721003, 7.6065478 ,\n",
       "       8.03660151, 8.01496455, 7.9603686 , 7.78550288, 7.00687739,\n",
       "       8.68316218, 7.32036921, 8.05492732, 8.29822871, 8.0738177 ,\n",
       "       7.98800227, 7.94071528, 7.32267329, 7.67647883, 8.02270075,\n",
       "       7.83201167, 7.40021845, 8.22906062, 7.99778315, 7.23636958,\n",
       "       7.74290034, 7.38288447, 7.36481806, 8.48177668, 8.17295405,\n",
       "       7.79667231, 6.94498774, 7.08721491, 7.39086482, 8.08097407,\n",
       "       7.93334528, 7.69353816, 8.06376215, 8.08901545, 8.16007563,\n",
       "       8.1798968 , 7.33573481, 7.8762469 , 8.29824397, 7.9401507 ,\n",
       "       8.10090205, 7.93638178, 7.31629511, 7.77325007, 7.32683894,\n",
       "       7.26780268, 8.15403315, 7.15319392, 8.06490656, 8.43044611,\n",
       "       8.06977412, 7.85930964, 8.57680842, 7.73968073, 8.49348017,\n",
       "       7.04806086, 8.47649714, 7.31701228, 6.99633357, 7.76494929,\n",
       "       8.44449946, 7.85050532, 7.57030818, 7.91077754, 8.21485468,\n",
       "       7.26743647, 8.09125849, 7.53722712, 8.39364192, 7.64953181,\n",
       "       7.92287776, 8.55987116, 7.88047358, 7.88705012, 7.44793269,\n",
       "       8.16091486, 8.24537226, 7.24566218, 8.19698664, 8.34060236,\n",
       "       7.66195246, 7.4661822 , 7.41965815, 7.82184931, 7.95887324,\n",
       "       7.79941889, 7.96097895, 7.81460139, 7.64773127, 7.64304682,\n",
       "       7.67676875, 8.26560542, 8.31251093, 7.76539179, 8.00266596,\n",
       "       7.6833758 , 8.21021601, 7.68572566, 7.77579829, 6.96682307,\n",
       "       8.49920222, 7.95531794, 7.98719355, 7.7746844 , 7.12916132,\n",
       "       7.57134577, 8.15537592, 6.91392085, 7.95919367, 8.19071528,\n",
       "       7.80349299, 8.13888117, 7.09585139, 7.08089777, 7.21215388,\n",
       "       7.75954768, 7.85891291, 7.24007746, 7.23734614, 7.99392268,\n",
       "       7.32619807, 7.9996905 , 7.83980891, 8.35335871, 7.73374506,\n",
       "       8.09759089, 8.15055415, 7.76859614, 7.33820673, 7.28460261,\n",
       "       7.62644526, 6.95920893, 7.55028864, 7.5886645 , 7.97112605,\n",
       "       7.17444941, 7.41036555, 8.30269953, 8.6337237 , 8.29058405,\n",
       "       7.65804621, 8.47985407, 7.60418269, 7.21430537, 8.05778071,\n",
       "       7.37966486, 7.807857  , 7.69594904, 7.59693476, 7.74398371,\n",
       "       7.6367602 , 7.92655512, 8.32223078, 7.64980647, 8.61901423,\n",
       "       8.40818354, 8.5654101 , 8.20690486, 7.56785151, 8.14275691,\n",
       "       6.71749445, 7.11162898, 8.074779  , 7.6510882 , 7.64123103,\n",
       "       7.86645075, 8.07767817, 7.63492915, 7.59984919, 7.64359614,\n",
       "       7.95051142, 8.22304865, 7.74663874, 7.92167231, 7.41608759,\n",
       "       7.04436823, 7.04226252, 7.4778094 , 8.02389094, 7.32876154,\n",
       "       7.45267817, 7.46238276, 7.70022151, 7.72349116, 7.78533503,\n",
       "       6.61475703, 7.48902461, 7.75120112, 7.35523554, 7.63460871,\n",
       "       8.30147883, 8.06364008, 7.47561213, 7.3293719 , 7.49886652,\n",
       "       6.94607111, 6.89240595, 6.90600153, 7.78004023, 7.35978266,\n",
       "       7.37643   , 7.12212702, 7.10547968, 7.71950861, 7.73124262,\n",
       "       7.9203448 , 7.43549677, 7.30852839, 7.31154963, 8.04384944,\n",
       "       7.06908747, 7.45072505, 8.33991572, 8.07305476, 7.74381587,\n",
       "       8.46479365, 8.04357478, 7.32722041, 7.87179133, 7.4235644 ,\n",
       "       7.67534968, 7.54088923, 7.67206904, 8.14876887, 7.65626093,\n",
       "       6.96860835, 7.6803393 , 7.8140063 , 7.81862971, 8.24140498,\n",
       "       8.11704585, 8.0541186 , 7.89789912, 7.215404  , 7.56005427,\n",
       "       7.79429194, 7.25793025, 7.10103938, 7.14989802, 7.73531672,\n",
       "       7.71886775, 7.66540095, 7.83526179, 8.01101252, 7.67235896,\n",
       "       7.36486384, 7.58399531, 7.59478327, 7.49035212, 7.30366084,\n",
       "       7.3003802 , 7.45179316, 7.60786006, 8.30219599, 6.80372187,\n",
       "       7.84975764, 7.1262469 , 7.13515803, 7.30059382, 7.82404658,\n",
       "       8.03373286, 8.31527277, 7.10531184, 6.53019282, 8.19633051,\n",
       "       7.09121272, 8.14095637, 6.46253535, 7.97785517, 7.78495356,\n",
       "       7.93328425, 7.54491755, 8.49731013, 7.78358027, 7.74854609,\n",
       "       8.27659174, 8.05280634, 7.83101985, 7.96207758, 7.94335505,\n",
       "       8.15537592, 7.28599116, 8.11747309, 7.98388239, 8.16070124,\n",
       "       8.19710871, 7.20983454, 8.16862055, 8.47237727, 7.72820612,\n",
       "       7.73820063, 7.43600031, 7.72457453, 7.62610957, 7.12444636,\n",
       "       7.1284289 , 7.16212031, 7.84910151, 7.76514765, 7.61977717,\n",
       "       8.12115046, 7.5818896 , 7.81873652, 7.69010493, 8.56881281,\n",
       "       8.33092829, 8.26989314, 8.49645564, 7.62916132, 8.2512469 ,\n",
       "       7.17650935, 7.95402094, 7.84629389, 7.43339106, 8.29821345,\n",
       "       7.83707758, 8.00577876, 6.97146174, 7.58680293, 7.38735529,\n",
       "       7.39603755, 7.74259516, 7.86043879, 7.27410456, 8.04807612,\n",
       "       8.02736994, 7.47895381, 8.18502375, 7.92525813, 6.36373469,\n",
       "       7.36277339, 7.560451  , 7.36187312, 8.69854304, 7.81965205,\n",
       "       8.22429987, 3.89557978, 3.92731806, 4.09748408, 3.83649775,\n",
       "       4.24518916, 4.03181025, 3.82941767, 3.6384997 , 4.39435908,\n",
       "       4.3469958 , 4.31281611, 4.31208369, 4.7634997 , 4.03425166,\n",
       "       3.69465205, 4.54767939, 5.01374384, 4.75373408, 3.72590205,\n",
       "       4.35187861, 4.6072497 , 4.38483759, 4.43928095, 3.73420283,\n",
       "       4.52839228, 4.84528681, 5.04865595, 4.05964228, 4.70246455,\n",
       "       4.29255244, 4.51691767, 3.88874384, 4.63825556, 3.80890986,\n",
       "       4.06061884, 3.77570673, 5.0669665 , 4.93928095, 4.55475947,\n",
       "       4.24836298, 4.00104853, 5.11725947, 4.28620478, 4.98737666,\n",
       "       5.32380244, 4.23981806, 5.11896845, 4.83478877, 3.72370478,\n",
       "       5.08552119, 3.98542353, 4.63215205, 3.78400752, 4.59138056,\n",
       "       5.39045283, 4.66901728, 4.92560908, 4.62678095, 3.98371455,\n",
       "       4.11140009, 4.53205439, 4.35798213, 4.07087275, 3.75812861,\n",
       "       5.20563838, 4.36579463, 3.7923083 , 4.02326533, 4.26594111,\n",
       "       3.68464228, 4.0134997 , 3.86701533, 3.73395869])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions_val"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### assess predictions against the test data (with labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "cookies_test = pd.read_csv('cookies_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_real = cookies_test[\"quality\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    779.000000\n",
       "mean       7.661104\n",
       "std        1.093643\n",
       "min        4.000000\n",
       "25%        7.000000\n",
       "50%        8.000000\n",
       "75%        8.000000\n",
       "max       10.000000\n",
       "Name: quality, dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_real.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9064320276234387"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_real, predictions_val, squared = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Can we get more accuracy with an ensemble method?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Task 4 : Add a random forest regressor model to try to get a more accurate score. \n",
    "\n",
    "You can follow https://www.geeksforgeeks.org/random-forest-regression-in-python/\n",
    "or the sklearn_documentation https://scikit-learn.org/stable/search.html?q=random+forest\n",
    "to import the regressor and fit it to your data, before running the cells below which will calculate your new RMSE accuracy score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### <span style=\"color:#306998\">Random Forest</span>\n",
    "<span style=\"color:#306998\">\n",
    "A random forest is an ensemble machine learning technique — a random forest contains many decision trees that all work together to classify new points. When a random forest is asked to classify a new point, the random forest gives that point to each of the decision trees. Each of those trees reports their classification and the random forest returns the most popular classification. It’s like every tree gets a vote, and the most popular classification wins.<br>\n",
    "Some of the trees in the random forest may be overfit, but by making the prediction based on a large number of trees, overfitting will have less of an impact.\n",
    "</span>  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Task 4 your code: \n",
    "# Fitting Random Forest Regression to the dataset\n",
    "# import the regressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "# Setup random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# Create the data\n",
    "X= df.drop(columns=\"quality\")\n",
    "y=df.quality\n",
    "\n",
    "# Split the data\n",
    "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2)\n",
    "\n",
    "# Instatiate Random Forest Regressor\n",
    "# create regressor object\n",
    "regressor = RandomForestRegressor(n_estimators=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit the regressor with X_train and y_train data\n",
    "regressor.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_new = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7400163799877966"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate de Random Forest Regressor,\n",
    "# The default score() evaluation metric is r_squared for regression algorithms\n",
    "# Highest = 1.0, lowest = 0.0\n",
    "\n",
    "regressor.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regression metrics on the test set\n",
      "R2 score: 0.7400163799877966\n",
      "MAE: 0.47749034749034747\n",
      "MSE: 0.4164333976833977\n"
     ]
    }
   ],
   "source": [
    "print(\"Regression metrics on the test set\")\n",
    "print(f\"R2 score: {r2_score(y_test, predictions_new)}\")\n",
    "print(f\"MAE: {mean_absolute_error(y_test, predictions_new)}\")\n",
    "print(f\"MSE: {mean_squared_error(y_test, predictions_new)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4159</th>\n",
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       "      <th>4546</th>\n",
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       "      <td>8.087902</td>\n",
       "      <td>-0.912098</td>\n",
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       "    <tr>\n",
       "      <th>2787</th>\n",
       "      <td>4</td>\n",
       "      <td>5.594921</td>\n",
       "      <td>1.594921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744</th>\n",
       "      <td>5</td>\n",
       "      <td>5.003246</td>\n",
       "      <td>0.003246</td>\n",
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       "    <tr>\n",
       "      <th>4481</th>\n",
       "      <td>7</td>\n",
       "      <td>7.434581</td>\n",
       "      <td>0.434581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3801</th>\n",
       "      <td>8</td>\n",
       "      <td>7.712932</td>\n",
       "      <td>-0.287068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4666</th>\n",
       "      <td>8</td>\n",
       "      <td>7.403118</td>\n",
       "      <td>-0.596882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2373</th>\n",
       "      <td>8</td>\n",
       "      <td>8.141551</td>\n",
       "      <td>0.141551</td>\n",
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       "    <tr>\n",
       "      <th>5007</th>\n",
       "      <td>6</td>\n",
       "      <td>6.202831</td>\n",
       "      <td>0.202831</td>\n",
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       "    <tr>\n",
       "      <th>4847</th>\n",
       "      <td>8</td>\n",
       "      <td>8.066295</td>\n",
       "      <td>0.066295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4536</th>\n",
       "      <td>5</td>\n",
       "      <td>5.048534</td>\n",
       "      <td>0.048534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4525</th>\n",
       "      <td>7</td>\n",
       "      <td>7.355571</td>\n",
       "      <td>0.355571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>8</td>\n",
       "      <td>8.146923</td>\n",
       "      <td>0.146923</td>\n",
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       "    <tr>\n",
       "      <th>2771</th>\n",
       "      <td>7</td>\n",
       "      <td>7.860912</td>\n",
       "      <td>0.860912</td>\n",
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       "    <tr>\n",
       "      <th>952</th>\n",
       "      <td>8</td>\n",
       "      <td>7.396953</td>\n",
       "      <td>-0.603047</td>\n",
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       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>8</td>\n",
       "      <td>7.815868</td>\n",
       "      <td>-0.184132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      actual values  predicted values  differences\n",
       "584               7          7.660137     0.660137\n",
       "1215              7          7.826122     0.826122\n",
       "877               9          8.622173    -0.377827\n",
       "4230              9          9.087627     0.087627\n",
       "4159              5          5.790599     0.790599\n",
       "4546              9          8.087902    -0.912098\n",
       "2787              4          5.594921     1.594921\n",
       "1744              5          5.003246     0.003246\n",
       "4481              7          7.434581     0.434581\n",
       "3801              8          7.712932    -0.287068\n",
       "4666              8          7.403118    -0.596882\n",
       "2373              8          8.141551     0.141551\n",
       "5007              6          6.202831     0.202831\n",
       "4847              8          8.066295     0.066295\n",
       "4536              5          5.048534     0.048534\n",
       "4525              7          7.355571     0.355571\n",
       "561               8          8.146923     0.146923\n",
       "2771              7          7.860912     0.860912\n",
       "952               8          7.396953    -0.603047\n",
       "915               8          7.815868    -0.184132"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Mean absolute error (MAE)\n",
    "\n",
    "#MAE is the average of the absolute differences between predictions and actual values.\n",
    "\n",
    "#It gives you an idea of how wrong your models predictions are.\n",
    "\n",
    "\n",
    "\n",
    "df = pd.DataFrame(data={\"actual values\": y_test,\n",
    "                        \"predicted values\": y_preds})\n",
    "df[\"differences\"] = df[\"predicted values\"] - df[\"actual values\"]\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5793221761789332"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MAE using formulas and differences\n",
    "np.abs(df[\"differences\"]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Mean squared error (MSE)\n",
    "\n",
    "#MSE is the mean of the square of the errors between actual and predicted values.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      actual values  predicted values  differences  squared_differences\n",
       "584               7          7.660137     0.660137             0.435780\n",
       "1215              7          7.826122     0.826122             0.682477\n",
       "877               9          8.622173    -0.377827             0.142753\n",
       "4230              9          9.087627     0.087627             0.007678\n",
       "4159              5          5.790599     0.790599             0.625047"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"squared_differences\"] = np.square(df[\"differences\"])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5671998985426202"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate MSE by hand\n",
    "squared = np.square(df[\"differences\"])\n",
    "squared.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_large_error = df.copy()\n",
    "df_large_error.iloc[0][\"squared_differences\"] = 16 # increase \"squared_differences\" for 1 sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
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       "      <th>4230</th>\n",
       "      <td>9</td>\n",
       "      <td>9.087627</td>\n",
       "      <td>0.087627</td>\n",
       "      <td>0.007678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4159</th>\n",
       "      <td>5</td>\n",
       "      <td>5.790599</td>\n",
       "      <td>0.790599</td>\n",
       "      <td>0.625047</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      actual values  predicted values  differences  squared_differences\n",
       "584               7          7.660137     0.660137             0.435780\n",
       "1215              7          7.826122     0.826122             0.682477\n",
       "877               9          8.622173    -0.377827             0.142753\n",
       "4230              9          9.087627     0.087627             0.007678\n",
       "4159              5          5.790599     0.790599             0.625047"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_large_error.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5671998985426202"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate MSE with large error\n",
    "df_large_error[\"squared_differences\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\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>actual values</th>\n",
       "      <th>predicted values</th>\n",
       "      <th>differences</th>\n",
       "      <th>squared_differences</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>7</td>\n",
       "      <td>7.660137</td>\n",
       "      <td>0.660137</td>\n",
       "      <td>0.435780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1215</th>\n",
       "      <td>7</td>\n",
       "      <td>7.826122</td>\n",
       "      <td>0.826122</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>9</td>\n",
       "      <td>8.622173</td>\n",
       "      <td>-0.377827</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4230</th>\n",
       "      <td>9</td>\n",
       "      <td>9.087627</td>\n",
       "      <td>0.087627</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4159</th>\n",
       "      <td>5</td>\n",
       "      <td>5.790599</td>\n",
       "      <td>0.790599</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>7</td>\n",
       "      <td>7.036663</td>\n",
       "      <td>0.036663</td>\n",
       "      <td>0.001344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1740</th>\n",
       "      <td>8</td>\n",
       "      <td>8.585857</td>\n",
       "      <td>0.585857</td>\n",
       "      <td>0.343228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1717</th>\n",
       "      <td>7</td>\n",
       "      <td>8.402721</td>\n",
       "      <td>1.402721</td>\n",
       "      <td>1.967626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>8</td>\n",
       "      <td>7.256908</td>\n",
       "      <td>-0.743092</td>\n",
       "      <td>0.552186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1909</th>\n",
       "      <td>7</td>\n",
       "      <td>8.247875</td>\n",
       "      <td>1.247875</td>\n",
       "      <td>1.557191</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1036 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      actual values  predicted values  differences  squared_differences\n",
       "584               7          7.660137     0.660137             0.435780\n",
       "1215              7          7.826122     0.826122            20.000000\n",
       "877               9          8.622173    -0.377827            20.000000\n",
       "4230              9          9.087627     0.087627            20.000000\n",
       "4159              5          5.790599     0.790599            20.000000\n",
       "...             ...               ...          ...                  ...\n",
       "283               7          7.036663     0.036663             0.001344\n",
       "1740              8          8.585857     0.585857             0.343228\n",
       "1717              7          8.402721     1.402721             1.967626\n",
       "1454              8          7.256908    -0.743092             0.552186\n",
       "1909              7          8.247875     1.247875             1.557191\n",
       "\n",
       "[1036 rows x 4 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Artificially increase error in \"squared_differences\" column for ~100 samples\n",
    "df_large_error.iloc[1:100, 3] = 20\n",
    "df_large_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.435884341803385"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate MSE with large error(s)\n",
    "df_large_error[\"squared_differences\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate random forest accuracy "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RMSE accuracy Score - depending on what you named your model and predictions this code may need to be adapted\n",
    "\n",
    "ie if your model is called 'new model' and you have created a prediction from this model 'predictions_new' then edit the code block below as: \n",
    "    \n",
    "    mean_squared_error(y_real, predictions_new, squared = False)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6453165096938073"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#EDIT as appropriate to get your RMSE \n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "mean_squared_error(y_test, predictions_new, squared = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Evaluating a model using the scoring parameter**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.78359239, 0.76041171, 0.73674938, 0.75922537, 0.76777694])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "cross_val_score(regressor, X, y, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7400163799877966, 0.7628822166889472)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "# Single training and test split score\n",
    "regressor_single_score = regressor.score(X_test, y_test)\n",
    "\n",
    "# Take the mean of 5-fold cross-validation score\n",
    "regressor_cross_val_score = np.mean(cross_val_score(regressor, X, y, cv=5))\n",
    "\n",
    "# Compare the two\n",
    "regressor_single_score, regressor_cross_val_score"
   ]
  }
 ],
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