OKCupid data - correlation between users body type and users diet.ipynb

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    "### Codecademy Capstone Project: Date-A-Scientist\n",
    "\n",
    "This project is a final capstone project in Codecademy's Data Scientist online course. The task is to use python and machine learning algorithms to answer a specific question, e.g. Codecademy suggested to try to predict the Zodiac sign of a OKCupid user using his personal information. \n",
    "\n",
    "My interest lay in a correlation between the user's body_type and the following features: diet, drinks, drugs, smokes. "
   ]
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     "text": [
      "(59946, 31)\n",
      "                age        height          income\n",
      "count  59946.000000  59943.000000    59946.000000\n",
      "mean      32.340290     68.295281    20033.222534\n",
      "std        9.452779      3.994803    97346.192104\n",
      "min       18.000000      1.000000       -1.000000\n",
      "25%       26.000000     66.000000       -1.000000\n",
      "50%       30.000000     68.000000       -1.000000\n",
      "75%       37.000000     71.000000       -1.000000\n",
      "max      110.000000     95.000000  1000000.000000\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>body_type</th>\n",
       "      <th>diet</th>\n",
       "      <th>drinks</th>\n",
       "      <th>drugs</th>\n",
       "      <th>education</th>\n",
       "      <th>essay0</th>\n",
       "      <th>essay1</th>\n",
       "      <th>essay2</th>\n",
       "      <th>essay3</th>\n",
       "      <th>...</th>\n",
       "      <th>location</th>\n",
       "      <th>offspring</th>\n",
       "      <th>orientation</th>\n",
       "      <th>pets</th>\n",
       "      <th>religion</th>\n",
       "      <th>sex</th>\n",
       "      <th>sign</th>\n",
       "      <th>smokes</th>\n",
       "      <th>speaks</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>a little extra</td>\n",
       "      <td>strictly anything</td>\n",
       "      <td>socially</td>\n",
       "      <td>never</td>\n",
       "      <td>working on college/university</td>\n",
       "      <td>about me:&lt;br /&gt;\\n&lt;br /&gt;\\ni would love to think...</td>\n",
       "      <td>currently working as an international agent fo...</td>\n",
       "      <td>making people laugh.&lt;br /&gt;\\nranting about a go...</td>\n",
       "      <td>the way i look. i am a six foot half asian, ha...</td>\n",
       "      <td>...</td>\n",
       "      <td>south san francisco, california</td>\n",
       "      <td>doesn&amp;rsquo;t have kids, but might want them</td>\n",
       "      <td>straight</td>\n",
       "      <td>likes dogs and likes cats</td>\n",
       "      <td>agnosticism and very serious about it</td>\n",
       "      <td>m</td>\n",
       "      <td>gemini</td>\n",
       "      <td>sometimes</td>\n",
       "      <td>english</td>\n",
       "      <td>single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35</td>\n",
       "      <td>average</td>\n",
       "      <td>mostly other</td>\n",
       "      <td>often</td>\n",
       "      <td>sometimes</td>\n",
       "      <td>working on space camp</td>\n",
       "      <td>i am a chef: this is what that means.&lt;br /&gt;\\n1...</td>\n",
       "      <td>dedicating everyday to being an unbelievable b...</td>\n",
       "      <td>being silly. having ridiculous amonts of fun w...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>oakland, california</td>\n",
       "      <td>doesn&amp;rsquo;t have kids, but might want them</td>\n",
       "      <td>straight</td>\n",
       "      <td>likes dogs and likes cats</td>\n",
       "      <td>agnosticism but not too serious about it</td>\n",
       "      <td>m</td>\n",
       "      <td>cancer</td>\n",
       "      <td>no</td>\n",
       "      <td>english (fluently), spanish (poorly), french (...</td>\n",
       "      <td>single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>thin</td>\n",
       "      <td>anything</td>\n",
       "      <td>socially</td>\n",
       "      <td>NaN</td>\n",
       "      <td>graduated from masters program</td>\n",
       "      <td>i'm not ashamed of much, but writing public te...</td>\n",
       "      <td>i make nerdy software for musicians, artists, ...</td>\n",
       "      <td>improvising in different contexts. alternating...</td>\n",
       "      <td>my large jaw and large glasses are the physica...</td>\n",
       "      <td>...</td>\n",
       "      <td>san francisco, california</td>\n",
       "      <td>NaN</td>\n",
       "      <td>straight</td>\n",
       "      <td>has cats</td>\n",
       "      <td>NaN</td>\n",
       "      <td>m</td>\n",
       "      <td>pisces but it doesn&amp;rsquo;t matter</td>\n",
       "      <td>no</td>\n",
       "      <td>english, french, c++</td>\n",
       "      <td>available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23</td>\n",
       "      <td>thin</td>\n",
       "      <td>vegetarian</td>\n",
       "      <td>socially</td>\n",
       "      <td>NaN</td>\n",
       "      <td>working on college/university</td>\n",
       "      <td>i work in a library and go to school. . .</td>\n",
       "      <td>reading things written by old dead people</td>\n",
       "      <td>playing synthesizers and organizing books acco...</td>\n",
       "      <td>socially awkward but i do my best</td>\n",
       "      <td>...</td>\n",
       "      <td>berkeley, california</td>\n",
       "      <td>doesn&amp;rsquo;t want kids</td>\n",
       "      <td>straight</td>\n",
       "      <td>likes cats</td>\n",
       "      <td>NaN</td>\n",
       "      <td>m</td>\n",
       "      <td>pisces</td>\n",
       "      <td>no</td>\n",
       "      <td>english, german (poorly)</td>\n",
       "      <td>single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29</td>\n",
       "      <td>athletic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>socially</td>\n",
       "      <td>never</td>\n",
       "      <td>graduated from college/university</td>\n",
       "      <td>hey how's it going? currently vague on the pro...</td>\n",
       "      <td>work work work work + play</td>\n",
       "      <td>creating imagery to look at:&lt;br /&gt;\\nhttp://bag...</td>\n",
       "      <td>i smile a lot and my inquisitive nature</td>\n",
       "      <td>...</td>\n",
       "      <td>san francisco, california</td>\n",
       "      <td>NaN</td>\n",
       "      <td>straight</td>\n",
       "      <td>likes dogs and likes cats</td>\n",
       "      <td>NaN</td>\n",
       "      <td>m</td>\n",
       "      <td>aquarius</td>\n",
       "      <td>no</td>\n",
       "      <td>english</td>\n",
       "      <td>single</td>\n",
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       "<p>5 rows × 31 columns</p>\n",
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      "text/plain": [
       "   age       body_type               diet    drinks      drugs  \\\n",
       "0   22  a little extra  strictly anything  socially      never   \n",
       "1   35         average       mostly other     often  sometimes   \n",
       "2   38            thin           anything  socially        NaN   \n",
       "3   23            thin         vegetarian  socially        NaN   \n",
       "4   29        athletic                NaN  socially      never   \n",
       "\n",
       "                           education  \\\n",
       "0      working on college/university   \n",
       "1              working on space camp   \n",
       "2     graduated from masters program   \n",
       "3      working on college/university   \n",
       "4  graduated from college/university   \n",
       "\n",
       "                                              essay0  \\\n",
       "0  about me:<br />\\n<br />\\ni would love to think...   \n",
       "1  i am a chef: this is what that means.<br />\\n1...   \n",
       "2  i'm not ashamed of much, but writing public te...   \n",
       "3          i work in a library and go to school. . .   \n",
       "4  hey how's it going? currently vague on the pro...   \n",
       "\n",
       "                                              essay1  \\\n",
       "0  currently working as an international agent fo...   \n",
       "1  dedicating everyday to being an unbelievable b...   \n",
       "2  i make nerdy software for musicians, artists, ...   \n",
       "3          reading things written by old dead people   \n",
       "4                         work work work work + play   \n",
       "\n",
       "                                              essay2  \\\n",
       "0  making people laugh.<br />\\nranting about a go...   \n",
       "1  being silly. having ridiculous amonts of fun w...   \n",
       "2  improvising in different contexts. alternating...   \n",
       "3  playing synthesizers and organizing books acco...   \n",
       "4  creating imagery to look at:<br />\\nhttp://bag...   \n",
       "\n",
       "                                              essay3  ...  \\\n",
       "0  the way i look. i am a six foot half asian, ha...  ...   \n",
       "1                                                NaN  ...   \n",
       "2  my large jaw and large glasses are the physica...  ...   \n",
       "3                  socially awkward but i do my best  ...   \n",
       "4            i smile a lot and my inquisitive nature  ...   \n",
       "\n",
       "                          location  \\\n",
       "0  south san francisco, california   \n",
       "1              oakland, california   \n",
       "2        san francisco, california   \n",
       "3             berkeley, california   \n",
       "4        san francisco, california   \n",
       "\n",
       "                                      offspring orientation  \\\n",
       "0  doesn&rsquo;t have kids, but might want them    straight   \n",
       "1  doesn&rsquo;t have kids, but might want them    straight   \n",
       "2                                           NaN    straight   \n",
       "3                       doesn&rsquo;t want kids    straight   \n",
       "4                                           NaN    straight   \n",
       "\n",
       "                        pets                                  religion sex  \\\n",
       "0  likes dogs and likes cats     agnosticism and very serious about it   m   \n",
       "1  likes dogs and likes cats  agnosticism but not too serious about it   m   \n",
       "2                   has cats                                       NaN   m   \n",
       "3                 likes cats                                       NaN   m   \n",
       "4  likes dogs and likes cats                                       NaN   m   \n",
       "\n",
       "                                 sign     smokes  \\\n",
       "0                              gemini  sometimes   \n",
       "1                              cancer         no   \n",
       "2  pisces but it doesn&rsquo;t matter         no   \n",
       "3                              pisces         no   \n",
       "4                            aquarius         no   \n",
       "\n",
       "                                              speaks     status  \n",
       "0                                            english     single  \n",
       "1  english (fluently), spanish (poorly), french (...     single  \n",
       "2                               english, french, c++  available  \n",
       "3                           english, german (poorly)     single  \n",
       "4                                            english     single  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 44,
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     "data": {
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"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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "from sklearn import tree\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "from sklearn.model_selection import cross_val_score, GridSearchCV\n",
    "\n",
    "# Import data\n",
    "df = pd.read_csv('profiles.csv')\n",
    "\n",
    "# Take a first look at the data\n",
    "print(df.shape)\n",
    "print(df.describe())\n",
    "df.hist()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age            0.000000\n",
       "body_type      0.088346\n",
       "diet           0.406950\n",
       "drinks         0.049795\n",
       "drugs          0.234878\n",
       "education      0.110566\n",
       "essay0         0.091549\n",
       "essay1         0.126314\n",
       "essay2         0.160778\n",
       "essay3         0.191439\n",
       "essay4         0.175775\n",
       "essay5         0.180996\n",
       "essay6         0.229723\n",
       "essay7         0.207704\n",
       "essay8         0.320705\n",
       "essay9         0.210239\n",
       "ethnicity      0.094752\n",
       "height         0.000050\n",
       "income         0.000000\n",
       "job            0.136756\n",
       "last_online    0.000000\n",
       "location       0.000000\n",
       "offspring      0.593217\n",
       "orientation    0.000000\n",
       "pets           0.332316\n",
       "religion       0.337404\n",
       "sex            0.000000\n",
       "sign           0.184433\n",
       "smokes         0.091949\n",
       "speaks         0.000834\n",
       "status         0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Which columns have NaNs? \n",
    "\n",
    "(np.sum(df.isna() == True))/df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "body_type    0.088346\n",
       "diet         0.406950\n",
       "drinks       0.049795\n",
       "drugs        0.234878\n",
       "smokes       0.091949\n",
       "dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I am interested in the columns 'body_type', 'diet', 'drinks', 'drugs' and 'smokes'.\n",
    "# How many NaNs are in these columns? \n",
    "\n",
    "(np.sum(df[['body_type', 'diet', 'drinks', 'drugs', 'smokes']].isna() == True))/df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "body_type    0.0\n",
       "diet         0.0\n",
       "drinks       0.0\n",
       "drugs        0.0\n",
       "smokes       0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Diet has most NaNs with over 40%!\n",
    "# There are some ways to take care of NaNs, like dropping the rows, fill the mean or fill the mode. \n",
    "# For now I will try with dropping the NaNs. In a later approach, I would like to see if I can improve my models by \n",
    "# filling in the mean or the mode in these columns. \n",
    "\n",
    "df_without_nan = df.dropna(subset=['body_type', 'diet', 'drinks','drugs', 'smokes'], axis = 0)\n",
    "\n",
    "# No NaNs left:\n",
    "(np.sum(df_without_nan[['body_type', 'diet', 'drinks', 'drugs', 'smokes']].isna() == True))/df_without_nan.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>body_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>average</td>\n",
       "      <td>6802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fit</td>\n",
       "      <td>5742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>athletic</td>\n",
       "      <td>5366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>thin</td>\n",
       "      <td>2191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>curvy</td>\n",
       "      <td>1843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>a little extra</td>\n",
       "      <td>1312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>skinny</td>\n",
       "      <td>804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>full figured</td>\n",
       "      <td>464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>overweight</td>\n",
       "      <td>227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>jacked</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>used up</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>rather not say</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             index  body_type\n",
       "0          average       6802\n",
       "1              fit       5742\n",
       "2         athletic       5366\n",
       "3             thin       2191\n",
       "4            curvy       1843\n",
       "5   a little extra       1312\n",
       "6           skinny        804\n",
       "7     full figured        464\n",
       "8       overweight        227\n",
       "9           jacked        191\n",
       "10         used up        182\n",
       "11  rather not say         78"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The label I would like to predict is 'body_type'. Lets have a first look a the body_type column: \n",
    "\n",
    "df_without_nan.body_type.value_counts().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>body_labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sporty</td>\n",
       "      <td>14103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>average</td>\n",
       "      <td>6802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>curvy</td>\n",
       "      <td>3619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bad</td>\n",
       "      <td>678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     index  body_labels\n",
       "0   sporty        14103\n",
       "1  average         6802\n",
       "2    curvy         3619\n",
       "3      bad          678"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We have 12 different body types. To improve the results of the machine learning algorithms, I aggregate the \n",
    "# body types to four different main categories: sporty, average, curvy and bad. \n",
    "\n",
    "# Mapping of the body types: \n",
    "\n",
    "sporty = ['fit', 'athletic', 'thin', 'skinny']\n",
    "average = ['average']\n",
    "curvy = ['curvy', 'a little extra', 'full figured']\n",
    "bad = ['overweight', 'jacked', 'used up', 'rather not say']\n",
    "\n",
    "body_mapping = {'fit': 'sporty', \n",
    "                'athletic': 'sporty', \n",
    "                'thin': 'sporty', \n",
    "                'skinny': 'sporty',\n",
    "               'average': 'average',\n",
    "               'curvy': 'curvy', \n",
    "                'a little extra': 'curvy', \n",
    "                'full figured': 'curvy',\n",
    "               'overweight': 'bad', \n",
    "                'jacked': 'bad', \n",
    "                'used up': 'bad', \n",
    "                'rather not say': 'bad'}\n",
    "\n",
    "df_body_labels = df_without_nan.assign(body_labels = df_without_nan.body_type.map(body_mapping))\n",
    "\n",
    "# Here are the new, mapped values, now with four different body types: \n",
    "df_body_labels.body_labels.value_counts().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "socially       17929\n",
      "rarely          2896\n",
      "often           2162\n",
      "not at all      1789\n",
      "very often       264\n",
      "desperately      162\n",
      "Name: drinks, dtype: int64\n",
      "never        20339\n",
      "sometimes     4600\n",
      "often          263\n",
      "Name: drugs, dtype: int64\n",
      "no                20402\n",
      "sometimes          1688\n",
      "when drinking      1429\n",
      "yes                1032\n",
      "trying to quit      651\n",
      "Name: smokes, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# The features, that I would like to use for prediction are: diet, drinks, drugs and smokes. \n",
    "# Drinks, drugs and smokes contains categorical data that can be mapped into continuous data.\n",
    "# E.g. \"drinks not at all\" = 0, \"drinks rarely\" = 1, \"drinks socially\" = 2 ... up to \"drinks desperately\" = 5. \n",
    "# Lets have a look at drinks, drugs and smokes: \n",
    "\n",
    "print(df_body_labels.drinks.value_counts())\n",
    "print(df_body_labels.drugs.value_counts())\n",
    "print(df_body_labels.smokes.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>age</th>\n",
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       "      <td>often</td>\n",
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       "      <td>working on space camp</td>\n",
       "      <td>i am a chef: this is what that means.&lt;br /&gt;\\n1...</td>\n",
       "      <td>dedicating everyday to being an unbelievable b...</td>\n",
       "      <td>being silly. having ridiculous amonts of fun w...</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>never</td>\n",
       "      <td>graduated from college/university</td>\n",
       "      <td>NaN</td>\n",
       "      <td>writing. meeting new people, spending time wit...</td>\n",
       "      <td>remembering people's birthdays, sending cards,...</td>\n",
       "      <td>i'm rather approachable (a byproduct of being ...</td>\n",
       "      <td>...</td>\n",
       "      <td>christianity</td>\n",
       "      <td>f</td>\n",
       "      <td>sagittarius</td>\n",
       "      <td>no</td>\n",
       "      <td>english, spanish (okay)</td>\n",
       "      <td>single</td>\n",
       "      <td>average</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>37</td>\n",
       "      <td>athletic</td>\n",
       "      <td>mostly anything</td>\n",
       "      <td>not at all</td>\n",
       "      <td>never</td>\n",
       "      <td>working on two-year college</td>\n",
       "      <td>my names jake.&lt;br /&gt;\\ni'm a creative guy and i...</td>\n",
       "      <td>i have an apartment. i like to explore and che...</td>\n",
       "      <td>i'm good at finding creative solutions to prob...</td>\n",
       "      <td>i'm short</td>\n",
       "      <td>...</td>\n",
       "      <td>atheism and laughing about it</td>\n",
       "      <td>m</td>\n",
       "      <td>cancer but it doesn&amp;rsquo;t matter</td>\n",
       "      <td>no</td>\n",
       "      <td>english (fluently)</td>\n",
       "      <td>single</td>\n",
       "      <td>sporty</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>28</td>\n",
       "      <td>average</td>\n",
       "      <td>mostly anything</td>\n",
       "      <td>socially</td>\n",
       "      <td>never</td>\n",
       "      <td>graduated from college/university</td>\n",
       "      <td>i was born in wisconsin, grew up in iowa, and ...</td>\n",
       "      <td>i'm currently the youngest member on an intern...</td>\n",
       "      <td>i'm really good at a little bit of everything....</td>\n",
       "      <td>the way i dress. some days it's hats, other da...</td>\n",
       "      <td>...</td>\n",
       "      <td>christianity and very serious about it</td>\n",
       "      <td>m</td>\n",
       "      <td>leo but it doesn&amp;rsquo;t matter</td>\n",
       "      <td>no</td>\n",
       "      <td>english (fluently), sign language (poorly)</td>\n",
       "      <td>seeing someone</td>\n",
       "      <td>average</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    age       body_type               diet      drinks      drugs  \\\n",
       "0    22  a little extra  strictly anything    socially      never   \n",
       "1    35         average       mostly other       often  sometimes   \n",
       "7    31         average    mostly anything    socially      never   \n",
       "9    37        athletic    mostly anything  not at all      never   \n",
       "11   28         average    mostly anything    socially      never   \n",
       "\n",
       "                            education  \\\n",
       "0       working on college/university   \n",
       "1               working on space camp   \n",
       "7   graduated from college/university   \n",
       "9         working on two-year college   \n",
       "11  graduated from college/university   \n",
       "\n",
       "                                               essay0  \\\n",
       "0   about me:<br />\\n<br />\\ni would love to think...   \n",
       "1   i am a chef: this is what that means.<br />\\n1...   \n",
       "7                                                 NaN   \n",
       "9   my names jake.<br />\\ni'm a creative guy and i...   \n",
       "11  i was born in wisconsin, grew up in iowa, and ...   \n",
       "\n",
       "                                               essay1  \\\n",
       "0   currently working as an international agent fo...   \n",
       "1   dedicating everyday to being an unbelievable b...   \n",
       "7   writing. meeting new people, spending time wit...   \n",
       "9   i have an apartment. i like to explore and che...   \n",
       "11  i'm currently the youngest member on an intern...   \n",
       "\n",
       "                                               essay2  \\\n",
       "0   making people laugh.<br />\\nranting about a go...   \n",
       "1   being silly. having ridiculous amonts of fun w...   \n",
       "7   remembering people's birthdays, sending cards,...   \n",
       "9   i'm good at finding creative solutions to prob...   \n",
       "11  i'm really good at a little bit of everything....   \n",
       "\n",
       "                                               essay3  ...  \\\n",
       "0   the way i look. i am a six foot half asian, ha...  ...   \n",
       "1                                                 NaN  ...   \n",
       "7   i'm rather approachable (a byproduct of being ...  ...   \n",
       "9                                           i'm short  ...   \n",
       "11  the way i dress. some days it's hats, other da...  ...   \n",
       "\n",
       "                                    religion sex  \\\n",
       "0      agnosticism and very serious about it   m   \n",
       "1   agnosticism but not too serious about it   m   \n",
       "7                               christianity   f   \n",
       "9              atheism and laughing about it   m   \n",
       "11    christianity and very serious about it   m   \n",
       "\n",
       "                                  sign     smokes  \\\n",
       "0                               gemini  sometimes   \n",
       "1                               cancer         no   \n",
       "7                          sagittarius         no   \n",
       "9   cancer but it doesn&rsquo;t matter         no   \n",
       "11     leo but it doesn&rsquo;t matter         no   \n",
       "\n",
       "                                               speaks          status  \\\n",
       "0                                             english          single   \n",
       "1   english (fluently), spanish (poorly), french (...          single   \n",
       "7                             english, spanish (okay)          single   \n",
       "9                                  english (fluently)          single   \n",
       "11         english (fluently), sign language (poorly)  seeing someone   \n",
       "\n",
       "   body_labels  drinks_code  drugs_code smokes_code  \n",
       "0        curvy            2           0           1  \n",
       "1      average            3           1           0  \n",
       "7      average            2           0           0  \n",
       "9       sporty            0           0           0  \n",
       "11     average            2           0           0  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mapping the categorical values to continuous values: \n",
    "\n",
    "drink_mapping = {\"not at all\": 0, \"rarely\": 1, \"socially\": 2, \"often\": 3, \"very often\": 4, \"desperately\": 5}\n",
    "df_body_labels[\"drinks_code\"] = df_body_labels.drinks.map(drink_mapping)\n",
    "\n",
    "drugs_mapping = {\"never\": 0, \"sometimes\": 1, \"often\": 2}\n",
    "df_body_labels[\"drugs_code\"] = df_body_labels.drugs.map(drugs_mapping)\n",
    "\n",
    "smokes_mapping = {\"no\": 0, \"sometimes\": 1, \"when drinking\": 2, \"yes\": 3, \"trying to quit\": 4}\n",
    "df_body_labels[\"smokes_code\"] = df_body_labels.smokes.map(smokes_mapping)\n",
    "\n",
    "df_body_labels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mostly anything        12290\n",
       "anything                4357\n",
       "strictly anything       3341\n",
       "mostly vegetarian       2322\n",
       "mostly other             741\n",
       "strictly vegetarian      632\n",
       "vegetarian               366\n",
       "strictly other           310\n",
       "other                    233\n",
       "mostly vegan             222\n",
       "strictly vegan           160\n",
       "vegan                     86\n",
       "mostly kosher             64\n",
       "mostly halal              36\n",
       "strictly halal            15\n",
       "strictly kosher           14\n",
       "halal                      7\n",
       "kosher                     6\n",
       "Name: diet, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The column diet can not be mapped into continuous value. Therefore I apply the one-hot approach to generate\n",
    "# new columns with 1 and 0 for the different diet types. \n",
    "\n",
    "df_body_labels.diet.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25202, 53)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First: Prepare a list of values in diet column: \n",
    "\n",
    "diet_values = df_body_labels.diet.unique().tolist()\n",
    "#print(diet_values)\n",
    "\n",
    "# Second: Iterate through the list items and add a new column named after this item with the value 1 or 0: \n",
    "for item in diet_values:\n",
    "    df_body_labels[item] = np.where(df_body_labels.diet.str.match(item), 1, 0)\n",
    "\n",
    "# Third: Have a look at the new shape of the dataframe, now with considerably more columns: \n",
    "df_body_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "   drinks_code  drugs_code  smokes_code  strictly anything  mostly other  \\\n",
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       "\n",
       "   mostly anything  mostly vegetarian  strictly vegan  anything  vegetarian  \\\n",
       "0              0.0                0.0             0.0       0.0         0.0   \n",
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       "4              1.0                0.0             0.0       0.0         0.0   \n",
       "\n",
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       "\n",
       "   mostly kosher  strictly halal  halal  strictly kosher  kosher  \n",
       "0            0.0             0.0    0.0              0.0     0.0  \n",
       "1            0.0             0.0    0.0              0.0     0.0  \n",
       "2            0.0             0.0    0.0              0.0     0.0  \n",
       "3            0.0             0.0    0.0              0.0     0.0  \n",
       "4            0.0             0.0    0.0              0.0     0.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prepare features and labels for prediction. \n",
    "# Features: Idea of trying out different combinations (e.g. only food variables to predict body type or\n",
    "# all no food variables to predict body type.)\n",
    "\n",
    "df_features_all = df_body_labels[['drinks_code','drugs_code', 'smokes_code'] + diet_values]\n",
    "x = df_features_all.values\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "x_scaled = min_max_scaler.fit_transform(x)\n",
    "\n",
    "df_features_all = pd.DataFrame(x_scaled, columns=df_features_all.columns)\n",
    "df_labels = df_body_labels['body_labels']\n",
    "\n",
    "df_features_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
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       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow18_col13 {\n",
       "            background-color:  #e4eff9;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow18_col14 {\n",
       "            background-color:  #edf4fc;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow18_col15 {\n",
       "            background-color:  #e7f0fa;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow18_col16 {\n",
       "            background-color:  #eef5fc;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow18_col17 {\n",
       "            background-color:  #f3f8fe;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow18_col18 {\n",
       "            background-color:  #08306b;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow18_col19 {\n",
       "            background-color:  #f3f8fe;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow18_col20 {\n",
       "            background-color:  #f5f9fe;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col0 {\n",
       "            background-color:  #eaf2fb;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col1 {\n",
       "            background-color:  #e4eff9;\n",
       "            color:  #000000;\n",
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       "            background-color:  #eef5fc;\n",
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       "            background-color:  #c1d9ed;\n",
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       "            background-color:  #dbe9f6;\n",
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       "            background-color:  #b9d6ea;\n",
       "            color:  #000000;\n",
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       "            background-color:  #caddf0;\n",
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       "            background-color:  #e9f2fa;\n",
       "            color:  #000000;\n",
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       "            background-color:  #b5d4e9;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col9 {\n",
       "            background-color:  #e3eef8;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col10 {\n",
       "            background-color:  #f1f7fd;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col11 {\n",
       "            background-color:  #ddeaf7;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col12 {\n",
       "            background-color:  #e7f0fa;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col13 {\n",
       "            background-color:  #e4eff9;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col14 {\n",
       "            background-color:  #edf4fc;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col15 {\n",
       "            background-color:  #e7f0fa;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col16 {\n",
       "            background-color:  #eef5fc;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col17 {\n",
       "            background-color:  #f3f8fe;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col18 {\n",
       "            background-color:  #f5f9fe;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col19 {\n",
       "            background-color:  #08306b;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col20 {\n",
       "            background-color:  #f5f9fe;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col0 {\n",
       "            background-color:  #f0f6fd;\n",
       "            color:  #000000;\n",
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       "            background-color:  #eaf3fb;\n",
       "            color:  #000000;\n",
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       "            background-color:  #f1f7fd;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col3 {\n",
       "            background-color:  #bfd8ed;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col4 {\n",
       "            background-color:  #dbe9f6;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col5 {\n",
       "            background-color:  #b7d4ea;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col6 {\n",
       "            background-color:  #caddf0;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col7 {\n",
       "            background-color:  #e9f2fa;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col8 {\n",
       "            background-color:  #b5d4e9;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col9 {\n",
       "            background-color:  #e3eef8;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col10 {\n",
       "            background-color:  #f1f7fd;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col11 {\n",
       "            background-color:  #dceaf6;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col12 {\n",
       "            background-color:  #e7f0fa;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col13 {\n",
       "            background-color:  #e4eff9;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col14 {\n",
       "            background-color:  #edf4fc;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col15 {\n",
       "            background-color:  #e7f0fa;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col16 {\n",
       "            background-color:  #eef5fc;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col17 {\n",
       "            background-color:  #f3f8fe;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col18 {\n",
       "            background-color:  #f4f9fe;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col19 {\n",
       "            background-color:  #f3f8fe;\n",
       "            color:  #000000;\n",
       "        }    #T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col20 {\n",
       "            background-color:  #08306b;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3c\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >drinks_code</th>        <th class=\"col_heading level0 col1\" >drugs_code</th>        <th class=\"col_heading level0 col2\" >smokes_code</th>        <th class=\"col_heading level0 col3\" >strictly anything</th>        <th class=\"col_heading level0 col4\" >mostly other</th>        <th class=\"col_heading level0 col5\" >mostly anything</th>        <th class=\"col_heading level0 col6\" >mostly vegetarian</th>        <th class=\"col_heading level0 col7\" >strictly vegan</th>        <th class=\"col_heading level0 col8\" >anything</th>        <th class=\"col_heading level0 col9\" >vegetarian</th>        <th class=\"col_heading level0 col10\" >mostly halal</th>        <th class=\"col_heading level0 col11\" >strictly vegetarian</th>        <th class=\"col_heading level0 col12\" >other</th>        <th class=\"col_heading level0 col13\" >strictly other</th>        <th class=\"col_heading level0 col14\" >vegan</th>        <th class=\"col_heading level0 col15\" >mostly vegan</th>        <th class=\"col_heading level0 col16\" >mostly kosher</th>        <th class=\"col_heading level0 col17\" >strictly halal</th>        <th class=\"col_heading level0 col18\" >halal</th>        <th class=\"col_heading level0 col19\" >strictly kosher</th>        <th class=\"col_heading level0 col20\" >kosher</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3clevel0_row0\" class=\"row_heading level0 row0\" >drinks_code</th>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow0_col0\" class=\"data row0 col0\" >1.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow0_col1\" class=\"data row0 col1\" >0.22</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow0_col2\" class=\"data row0 col2\" >0.14</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow0_col3\" class=\"data row0 col3\" >0.08</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow0_col4\" class=\"data row0 col4\" >-0.05</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow0_col5\" class=\"data row0 col5\" >0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow0_col6\" class=\"data row0 col6\" >-0.04</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow0_col7\" class=\"data row0 col7\" >-0.04</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow0_col8\" class=\"data row0 col8\" >0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow0_col9\" class=\"data row0 col9\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow0_col10\" class=\"data row0 col10\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow0_col11\" class=\"data row0 col11\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow0_col12\" class=\"data row0 col12\" >-0.03</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow0_col13\" class=\"data row0 col13\" >-0.03</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow0_col14\" class=\"data row0 col14\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow0_col15\" class=\"data row0 col15\" >-0.03</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow0_col16\" class=\"data row0 col16\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow0_col17\" class=\"data row0 col17\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow0_col18\" class=\"data row0 col18\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow0_col19\" class=\"data row0 col19\" >0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow0_col20\" class=\"data row0 col20\" >-0.01</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3clevel0_row1\" class=\"row_heading level0 row1\" >drugs_code</th>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow1_col0\" class=\"data row1 col0\" >0.22</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow1_col1\" class=\"data row1 col1\" >1.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow1_col2\" class=\"data row1 col2\" >0.32</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow1_col3\" class=\"data row1 col3\" >0.10</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow1_col4\" class=\"data row1 col4\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow1_col5\" class=\"data row1 col5\" >-0.08</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow1_col6\" class=\"data row1 col6\" >0.03</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow1_col7\" class=\"data row1 col7\" >0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow1_col8\" class=\"data row1 col8\" >-0.04</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow1_col9\" class=\"data row1 col9\" >0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow1_col10\" class=\"data row1 col10\" >0.03</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow1_col11\" class=\"data row1 col11\" >0.03</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow1_col12\" class=\"data row1 col12\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow1_col13\" class=\"data row1 col13\" >0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow1_col14\" class=\"data row1 col14\" >0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow1_col15\" class=\"data row1 col15\" >0.03</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow1_col16\" class=\"data row1 col16\" >0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow1_col17\" class=\"data row1 col17\" >0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow1_col18\" class=\"data row1 col18\" >0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow1_col19\" class=\"data row1 col19\" >0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow1_col20\" class=\"data row1 col20\" >-0.01</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3clevel0_row2\" class=\"row_heading level0 row2\" >smokes_code</th>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow2_col0\" class=\"data row2 col0\" >0.14</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow2_col1\" class=\"data row2 col1\" >0.32</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow2_col2\" class=\"data row2 col2\" >1.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow2_col3\" class=\"data row2 col3\" >0.04</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow2_col4\" class=\"data row2 col4\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow2_col5\" class=\"data row2 col5\" >-0.03</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow2_col6\" class=\"data row2 col6\" >-0.03</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow2_col7\" class=\"data row2 col7\" >0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow2_col8\" class=\"data row2 col8\" >0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow2_col9\" class=\"data row2 col9\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow2_col10\" class=\"data row2 col10\" >0.03</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow2_col11\" class=\"data row2 col11\" >0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow2_col12\" class=\"data row2 col12\" >0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow2_col13\" class=\"data row2 col13\" >0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow2_col14\" class=\"data row2 col14\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow2_col15\" class=\"data row2 col15\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow2_col16\" class=\"data row2 col16\" >0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow2_col17\" class=\"data row2 col17\" >0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow2_col18\" class=\"data row2 col18\" >0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow2_col19\" class=\"data row2 col19\" >0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow2_col20\" class=\"data row2 col20\" >0.01</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3clevel0_row3\" class=\"row_heading level0 row3\" >strictly anything</th>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow3_col0\" class=\"data row3 col0\" >0.08</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow3_col1\" class=\"data row3 col1\" >0.10</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow3_col2\" class=\"data row3 col2\" >0.04</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow3_col3\" class=\"data row3 col3\" >1.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow3_col4\" class=\"data row3 col4\" >-0.07</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow3_col5\" class=\"data row3 col5\" >-0.38</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow3_col6\" class=\"data row3 col6\" >-0.12</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow3_col7\" class=\"data row3 col7\" >-0.03</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow3_col8\" class=\"data row3 col8\" >-0.18</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow3_col9\" class=\"data row3 col9\" >-0.05</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow3_col10\" class=\"data row3 col10\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow3_col11\" class=\"data row3 col11\" >-0.06</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow3_col12\" class=\"data row3 col12\" >-0.04</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow3_col13\" class=\"data row3 col13\" >-0.04</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow3_col14\" class=\"data row3 col14\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow3_col15\" class=\"data row3 col15\" >-0.04</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow3_col16\" class=\"data row3 col16\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow3_col17\" class=\"data row3 col17\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow3_col18\" class=\"data row3 col18\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow3_col19\" class=\"data row3 col19\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow3_col20\" class=\"data row3 col20\" >-0.01</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3clevel0_row4\" class=\"row_heading level0 row4\" >mostly other</th>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow4_col0\" class=\"data row4 col0\" >-0.05</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow4_col1\" class=\"data row4 col1\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow4_col2\" class=\"data row4 col2\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow4_col3\" class=\"data row4 col3\" >-0.07</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow4_col4\" class=\"data row4 col4\" >1.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow4_col5\" class=\"data row4 col5\" >-0.17</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow4_col6\" class=\"data row4 col6\" >-0.06</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow4_col7\" class=\"data row4 col7\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow4_col8\" class=\"data row4 col8\" >-0.08</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow4_col9\" class=\"data row4 col9\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow4_col10\" class=\"data row4 col10\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow4_col11\" class=\"data row4 col11\" >-0.03</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow4_col12\" class=\"data row4 col12\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow4_col13\" class=\"data row4 col13\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow4_col14\" class=\"data row4 col14\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow4_col15\" class=\"data row4 col15\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow4_col16\" class=\"data row4 col16\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow4_col17\" class=\"data row4 col17\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow4_col18\" class=\"data row4 col18\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow4_col19\" class=\"data row4 col19\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow4_col20\" class=\"data row4 col20\" >-0.00</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3clevel0_row5\" class=\"row_heading level0 row5\" >mostly anything</th>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow5_col0\" class=\"data row5 col0\" >0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow5_col1\" class=\"data row5 col1\" >-0.08</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow5_col2\" class=\"data row5 col2\" >-0.03</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow5_col3\" class=\"data row5 col3\" >-0.38</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow5_col4\" class=\"data row5 col4\" >-0.17</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow5_col5\" class=\"data row5 col5\" >1.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow5_col6\" class=\"data row5 col6\" >-0.31</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow5_col7\" class=\"data row5 col7\" >-0.08</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow5_col8\" class=\"data row5 col8\" >-0.45</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow5_col9\" class=\"data row5 col9\" >-0.12</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow5_col10\" class=\"data row5 col10\" >-0.04</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow5_col11\" class=\"data row5 col11\" >-0.16</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow5_col12\" class=\"data row5 col12\" >-0.09</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow5_col13\" class=\"data row5 col13\" >-0.11</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow5_col14\" class=\"data row5 col14\" >-0.06</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow5_col15\" class=\"data row5 col15\" >-0.09</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow5_col16\" class=\"data row5 col16\" >-0.05</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow5_col17\" class=\"data row5 col17\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow5_col18\" class=\"data row5 col18\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow5_col19\" class=\"data row5 col19\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow5_col20\" class=\"data row5 col20\" >-0.02</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3clevel0_row6\" class=\"row_heading level0 row6\" >mostly vegetarian</th>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow6_col0\" class=\"data row6 col0\" >-0.04</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow6_col1\" class=\"data row6 col1\" >0.03</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow6_col2\" class=\"data row6 col2\" >-0.03</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow6_col3\" class=\"data row6 col3\" >-0.12</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow6_col4\" class=\"data row6 col4\" >-0.06</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow6_col5\" class=\"data row6 col5\" >-0.31</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow6_col6\" class=\"data row6 col6\" >1.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow6_col7\" class=\"data row6 col7\" >-0.03</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow6_col8\" class=\"data row6 col8\" >-0.15</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow6_col9\" class=\"data row6 col9\" >-0.04</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow6_col10\" class=\"data row6 col10\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow6_col11\" class=\"data row6 col11\" >-0.05</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow6_col12\" class=\"data row6 col12\" >-0.03</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow6_col13\" class=\"data row6 col13\" >-0.04</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow6_col14\" class=\"data row6 col14\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow6_col15\" class=\"data row6 col15\" >-0.03</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow6_col16\" class=\"data row6 col16\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow6_col17\" class=\"data row6 col17\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow6_col18\" class=\"data row6 col18\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow6_col19\" class=\"data row6 col19\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow6_col20\" class=\"data row6 col20\" >-0.00</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3clevel0_row7\" class=\"row_heading level0 row7\" >strictly vegan</th>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow7_col0\" class=\"data row7 col0\" >-0.04</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow7_col1\" class=\"data row7 col1\" >0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow7_col2\" class=\"data row7 col2\" >0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow7_col3\" class=\"data row7 col3\" >-0.03</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow7_col4\" class=\"data row7 col4\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow7_col5\" class=\"data row7 col5\" >-0.08</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow7_col6\" class=\"data row7 col6\" >-0.03</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow7_col7\" class=\"data row7 col7\" >1.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow7_col8\" class=\"data row7 col8\" >-0.04</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow7_col9\" class=\"data row7 col9\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow7_col10\" class=\"data row7 col10\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow7_col11\" class=\"data row7 col11\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow7_col12\" class=\"data row7 col12\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow7_col13\" class=\"data row7 col13\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow7_col14\" class=\"data row7 col14\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow7_col15\" class=\"data row7 col15\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow7_col16\" class=\"data row7 col16\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow7_col17\" class=\"data row7 col17\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow7_col18\" class=\"data row7 col18\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow7_col19\" class=\"data row7 col19\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow7_col20\" class=\"data row7 col20\" >-0.00</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3clevel0_row8\" class=\"row_heading level0 row8\" >anything</th>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow8_col0\" class=\"data row8 col0\" >0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow8_col1\" class=\"data row8 col1\" >-0.04</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow8_col2\" class=\"data row8 col2\" >0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow8_col3\" class=\"data row8 col3\" >-0.18</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow8_col4\" class=\"data row8 col4\" >-0.08</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow8_col5\" class=\"data row8 col5\" >-0.45</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow8_col6\" class=\"data row8 col6\" >-0.15</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow8_col7\" class=\"data row8 col7\" >-0.04</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow8_col8\" class=\"data row8 col8\" >1.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow8_col9\" class=\"data row8 col9\" >-0.06</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow8_col10\" class=\"data row8 col10\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow8_col11\" class=\"data row8 col11\" >-0.07</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow8_col12\" class=\"data row8 col12\" >-0.04</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow8_col13\" class=\"data row8 col13\" >-0.05</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow8_col14\" class=\"data row8 col14\" >-0.03</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow8_col15\" class=\"data row8 col15\" >-0.04</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow8_col16\" class=\"data row8 col16\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow8_col17\" class=\"data row8 col17\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow8_col18\" class=\"data row8 col18\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow8_col19\" class=\"data row8 col19\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow8_col20\" class=\"data row8 col20\" >-0.01</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3clevel0_row9\" class=\"row_heading level0 row9\" >vegetarian</th>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow9_col0\" class=\"data row9 col0\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow9_col1\" class=\"data row9 col1\" >0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow9_col2\" class=\"data row9 col2\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow9_col3\" class=\"data row9 col3\" >-0.05</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow9_col4\" class=\"data row9 col4\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow9_col5\" class=\"data row9 col5\" >-0.12</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow9_col6\" class=\"data row9 col6\" >-0.04</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow9_col7\" class=\"data row9 col7\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow9_col8\" class=\"data row9 col8\" >-0.06</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow9_col9\" class=\"data row9 col9\" >1.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow9_col10\" class=\"data row9 col10\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow9_col11\" class=\"data row9 col11\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow9_col12\" class=\"data row9 col12\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow9_col13\" class=\"data row9 col13\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow9_col14\" class=\"data row9 col14\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow9_col15\" class=\"data row9 col15\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow9_col16\" class=\"data row9 col16\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow9_col17\" class=\"data row9 col17\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow9_col18\" class=\"data row9 col18\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow9_col19\" class=\"data row9 col19\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow9_col20\" class=\"data row9 col20\" >-0.00</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3clevel0_row10\" class=\"row_heading level0 row10\" >mostly halal</th>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow10_col0\" class=\"data row10 col0\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow10_col1\" class=\"data row10 col1\" >0.03</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow10_col2\" class=\"data row10 col2\" >0.03</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow10_col3\" class=\"data row10 col3\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow10_col4\" class=\"data row10 col4\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow10_col5\" class=\"data row10 col5\" >-0.04</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow10_col6\" class=\"data row10 col6\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow10_col7\" class=\"data row10 col7\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow10_col8\" class=\"data row10 col8\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow10_col9\" class=\"data row10 col9\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow10_col10\" class=\"data row10 col10\" >1.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow10_col11\" class=\"data row10 col11\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow10_col12\" class=\"data row10 col12\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow10_col13\" class=\"data row10 col13\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow10_col14\" class=\"data row10 col14\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow10_col15\" class=\"data row10 col15\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow10_col16\" class=\"data row10 col16\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow10_col17\" class=\"data row10 col17\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow10_col18\" class=\"data row10 col18\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow10_col19\" class=\"data row10 col19\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow10_col20\" class=\"data row10 col20\" >-0.00</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3clevel0_row11\" class=\"row_heading level0 row11\" >strictly vegetarian</th>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow11_col0\" class=\"data row11 col0\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow11_col1\" class=\"data row11 col1\" >0.03</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow11_col2\" class=\"data row11 col2\" >0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow11_col3\" class=\"data row11 col3\" >-0.06</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow11_col4\" class=\"data row11 col4\" >-0.03</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow11_col5\" class=\"data row11 col5\" >-0.16</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow11_col6\" class=\"data row11 col6\" >-0.05</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow11_col7\" class=\"data row11 col7\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow11_col8\" class=\"data row11 col8\" >-0.07</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow11_col9\" class=\"data row11 col9\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow11_col10\" class=\"data row11 col10\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow11_col11\" class=\"data row11 col11\" >1.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow11_col12\" class=\"data row11 col12\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow11_col13\" class=\"data row11 col13\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow11_col14\" class=\"data row11 col14\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow11_col15\" class=\"data row11 col15\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow11_col16\" class=\"data row11 col16\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow11_col17\" class=\"data row11 col17\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow11_col18\" class=\"data row11 col18\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow11_col19\" class=\"data row11 col19\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow11_col20\" class=\"data row11 col20\" >-0.00</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3clevel0_row12\" class=\"row_heading level0 row12\" >other</th>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow12_col0\" class=\"data row12 col0\" >-0.03</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow12_col1\" class=\"data row12 col1\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow12_col2\" class=\"data row12 col2\" >0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow12_col3\" class=\"data row12 col3\" >-0.04</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow12_col4\" class=\"data row12 col4\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow12_col5\" class=\"data row12 col5\" >-0.09</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow12_col6\" class=\"data row12 col6\" >-0.03</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow12_col7\" class=\"data row12 col7\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow12_col8\" class=\"data row12 col8\" >-0.04</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow12_col9\" class=\"data row12 col9\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow12_col10\" class=\"data row12 col10\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow12_col11\" class=\"data row12 col11\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow12_col12\" class=\"data row12 col12\" >1.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow12_col13\" class=\"data row12 col13\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow12_col14\" class=\"data row12 col14\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow12_col15\" class=\"data row12 col15\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow12_col16\" class=\"data row12 col16\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow12_col17\" class=\"data row12 col17\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow12_col18\" class=\"data row12 col18\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow12_col19\" class=\"data row12 col19\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow12_col20\" class=\"data row12 col20\" >-0.00</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3clevel0_row13\" class=\"row_heading level0 row13\" >strictly other</th>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow13_col0\" class=\"data row13 col0\" >-0.03</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow13_col1\" class=\"data row13 col1\" >0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow13_col2\" class=\"data row13 col2\" >0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow13_col3\" class=\"data row13 col3\" >-0.04</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow13_col4\" class=\"data row13 col4\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow13_col5\" class=\"data row13 col5\" >-0.11</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow13_col6\" class=\"data row13 col6\" >-0.04</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow13_col7\" class=\"data row13 col7\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow13_col8\" class=\"data row13 col8\" >-0.05</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow13_col9\" class=\"data row13 col9\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow13_col10\" class=\"data row13 col10\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow13_col11\" class=\"data row13 col11\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow13_col12\" class=\"data row13 col12\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow13_col13\" class=\"data row13 col13\" >1.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow13_col14\" class=\"data row13 col14\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow13_col15\" class=\"data row13 col15\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow13_col16\" class=\"data row13 col16\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow13_col17\" class=\"data row13 col17\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow13_col18\" class=\"data row13 col18\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow13_col19\" class=\"data row13 col19\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow13_col20\" class=\"data row13 col20\" >-0.00</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3clevel0_row14\" class=\"row_heading level0 row14\" >vegan</th>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow14_col0\" class=\"data row14 col0\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow14_col1\" class=\"data row14 col1\" >0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow14_col2\" class=\"data row14 col2\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow14_col3\" class=\"data row14 col3\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow14_col4\" class=\"data row14 col4\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow14_col5\" class=\"data row14 col5\" >-0.06</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow14_col6\" class=\"data row14 col6\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow14_col7\" class=\"data row14 col7\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow14_col8\" class=\"data row14 col8\" >-0.03</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow14_col9\" class=\"data row14 col9\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow14_col10\" class=\"data row14 col10\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow14_col11\" class=\"data row14 col11\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow14_col12\" class=\"data row14 col12\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow14_col13\" class=\"data row14 col13\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow14_col14\" class=\"data row14 col14\" >1.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow14_col15\" class=\"data row14 col15\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow14_col16\" class=\"data row14 col16\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow14_col17\" class=\"data row14 col17\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow14_col18\" class=\"data row14 col18\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow14_col19\" class=\"data row14 col19\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow14_col20\" class=\"data row14 col20\" >-0.00</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3clevel0_row15\" class=\"row_heading level0 row15\" >mostly vegan</th>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow15_col0\" class=\"data row15 col0\" >-0.03</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow15_col1\" class=\"data row15 col1\" >0.03</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow15_col2\" class=\"data row15 col2\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow15_col3\" class=\"data row15 col3\" >-0.04</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow15_col4\" class=\"data row15 col4\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow15_col5\" class=\"data row15 col5\" >-0.09</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow15_col6\" class=\"data row15 col6\" >-0.03</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow15_col7\" class=\"data row15 col7\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow15_col8\" class=\"data row15 col8\" >-0.04</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow15_col9\" class=\"data row15 col9\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow15_col10\" class=\"data row15 col10\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow15_col11\" class=\"data row15 col11\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow15_col12\" class=\"data row15 col12\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow15_col13\" class=\"data row15 col13\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow15_col14\" class=\"data row15 col14\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow15_col15\" class=\"data row15 col15\" >1.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow15_col16\" class=\"data row15 col16\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow15_col17\" class=\"data row15 col17\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow15_col18\" class=\"data row15 col18\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow15_col19\" class=\"data row15 col19\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow15_col20\" class=\"data row15 col20\" >-0.00</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3clevel0_row16\" class=\"row_heading level0 row16\" >mostly kosher</th>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow16_col0\" class=\"data row16 col0\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow16_col1\" class=\"data row16 col1\" >0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow16_col2\" class=\"data row16 col2\" >0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow16_col3\" class=\"data row16 col3\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow16_col4\" class=\"data row16 col4\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow16_col5\" class=\"data row16 col5\" >-0.05</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow16_col6\" class=\"data row16 col6\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow16_col7\" class=\"data row16 col7\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow16_col8\" class=\"data row16 col8\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow16_col9\" class=\"data row16 col9\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow16_col10\" class=\"data row16 col10\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow16_col11\" class=\"data row16 col11\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow16_col12\" class=\"data row16 col12\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow16_col13\" class=\"data row16 col13\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow16_col14\" class=\"data row16 col14\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow16_col15\" class=\"data row16 col15\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow16_col16\" class=\"data row16 col16\" >1.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow16_col17\" class=\"data row16 col17\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow16_col18\" class=\"data row16 col18\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow16_col19\" class=\"data row16 col19\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow16_col20\" class=\"data row16 col20\" >-0.00</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3clevel0_row17\" class=\"row_heading level0 row17\" >strictly halal</th>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow17_col0\" class=\"data row17 col0\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow17_col1\" class=\"data row17 col1\" >0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow17_col2\" class=\"data row17 col2\" >0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow17_col3\" class=\"data row17 col3\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow17_col4\" class=\"data row17 col4\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow17_col5\" class=\"data row17 col5\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow17_col6\" class=\"data row17 col6\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow17_col7\" class=\"data row17 col7\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow17_col8\" class=\"data row17 col8\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow17_col9\" class=\"data row17 col9\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow17_col10\" class=\"data row17 col10\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow17_col11\" class=\"data row17 col11\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow17_col12\" class=\"data row17 col12\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow17_col13\" class=\"data row17 col13\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow17_col14\" class=\"data row17 col14\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow17_col15\" class=\"data row17 col15\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow17_col16\" class=\"data row17 col16\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow17_col17\" class=\"data row17 col17\" >1.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow17_col18\" class=\"data row17 col18\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow17_col19\" class=\"data row17 col19\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow17_col20\" class=\"data row17 col20\" >-0.00</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3clevel0_row18\" class=\"row_heading level0 row18\" >halal</th>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow18_col0\" class=\"data row18 col0\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow18_col1\" class=\"data row18 col1\" >0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow18_col2\" class=\"data row18 col2\" >0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow18_col3\" class=\"data row18 col3\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow18_col4\" class=\"data row18 col4\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow18_col5\" class=\"data row18 col5\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow18_col6\" class=\"data row18 col6\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow18_col7\" class=\"data row18 col7\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow18_col8\" class=\"data row18 col8\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow18_col9\" class=\"data row18 col9\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow18_col10\" class=\"data row18 col10\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow18_col11\" class=\"data row18 col11\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow18_col12\" class=\"data row18 col12\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow18_col13\" class=\"data row18 col13\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow18_col14\" class=\"data row18 col14\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow18_col15\" class=\"data row18 col15\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow18_col16\" class=\"data row18 col16\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow18_col17\" class=\"data row18 col17\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow18_col18\" class=\"data row18 col18\" >1.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow18_col19\" class=\"data row18 col19\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow18_col20\" class=\"data row18 col20\" >-0.00</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3clevel0_row19\" class=\"row_heading level0 row19\" >strictly kosher</th>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col0\" class=\"data row19 col0\" >0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col1\" class=\"data row19 col1\" >0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col2\" class=\"data row19 col2\" >0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col3\" class=\"data row19 col3\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col4\" class=\"data row19 col4\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col5\" class=\"data row19 col5\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col6\" class=\"data row19 col6\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col7\" class=\"data row19 col7\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col8\" class=\"data row19 col8\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col9\" class=\"data row19 col9\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col10\" class=\"data row19 col10\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col11\" class=\"data row19 col11\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col12\" class=\"data row19 col12\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col13\" class=\"data row19 col13\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col14\" class=\"data row19 col14\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col15\" class=\"data row19 col15\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col16\" class=\"data row19 col16\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col17\" class=\"data row19 col17\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col18\" class=\"data row19 col18\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col19\" class=\"data row19 col19\" >1.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow19_col20\" class=\"data row19 col20\" >-0.00</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3clevel0_row20\" class=\"row_heading level0 row20\" >kosher</th>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col0\" class=\"data row20 col0\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col1\" class=\"data row20 col1\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col2\" class=\"data row20 col2\" >0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col3\" class=\"data row20 col3\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col4\" class=\"data row20 col4\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col5\" class=\"data row20 col5\" >-0.02</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col6\" class=\"data row20 col6\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col7\" class=\"data row20 col7\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col8\" class=\"data row20 col8\" >-0.01</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col9\" class=\"data row20 col9\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col10\" class=\"data row20 col10\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col11\" class=\"data row20 col11\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col12\" class=\"data row20 col12\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col13\" class=\"data row20 col13\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col14\" class=\"data row20 col14\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col15\" class=\"data row20 col15\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col16\" class=\"data row20 col16\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col17\" class=\"data row20 col17\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col18\" class=\"data row20 col18\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col19\" class=\"data row20 col19\" >-0.00</td>\n",
       "                        <td id=\"T_54338874_90a1_11ea_9a75_206a8aa20d3crow20_col20\" class=\"data row20 col20\" >1.00</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2e16a707a88>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Before with start, lets have a look at the correlations. \n",
    "# First, the correlations of the features: drinks, drugs, smokes and all diet alternatives. \n",
    "\n",
    "corr = df_features_all.corr()\n",
    "table = corr.style.background_gradient(cmap='Blues').format('{0:,.2f}')\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Next, we have a loook at the correlation between the features and the label. However, I do not use the \n",
    "# aggregated body type but the 12 original body types.\n",
    "\n",
    "\n",
    "f, ax = plt.subplots(figsize = (12,6))\n",
    "plt.scatter(df_body_labels.body_type, df_body_labels.drinks_code, alpha = 0.01)\n",
    "plt.xlabel('Body Type')\n",
    "plt.ylabel('Drinks Code')\n",
    "plt.title('Correlation between Drinks Code and Body Type')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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611nq75euuK9PfX197S6ttIGBAS1engN1Z6cGnlSaHxhod2ljYv369Vq6KgfqqVO1fqPS/Pr17S6ttHXr1mnJyhyop03Tug1K8+vWtbu00gYHB7VmIAfqjg4NSml+cLDdpQ2rpaHa9l6SXi3pG8MscqykcyK5TtJM23u0sqZtdfYvN2iGUqBWns7I7VVXH6hHa6+S+kBdDNYTwc/uWqxdJqVALaXpLpNSe9Vdesf92nlSCtRSmu48KbVXXX2gLgbrieCSWx7UjEkpUEtpOmNSaq+6xcv7tMukFKilNN1lUmqvur41A+rs0OaR6c7OTnV2pPaJoP+J9Zraoc0j01OnTtXUjtRedY+tXadpHdo8Mj1t2jRN60jtVffkk4Pq6NDmkemOjg51dKT28arVI9VflPQBSZuGuX1PSQ8V5pfmti3YPsH2QtsLly9fPvZVjqA2Ql1UG7HG+FYL0sPNV1lthLqoNmJddbUR6qLaiPVEcODUkeerrDZCXVQbsa662gh1UW3EuupqI9RFtRHriaA2Ql1UG7GuutoIdVFtxLrqaiPURbUR6/GqZaHa9msk9UbETSMt1qAttmqIODsieiKip7u7e8xqbMZcSbfeveXWeevdGzR3h1aB7XHb2pHnq6y7S/rtg1uOSv/2wcXq7mpTQWNo9y7pviVbjkrft+R+7T4B+iZJd60feb7K5nZJ9y7eclT63sUPau4EeO26uqT+VVuOSvev6lPXBOhb507a6lCPgYEBde7UpoLG2NTJ2upQj/Xr12tqy39V1nrTpmirQz3WrVunaVOGuUOFdEhbHeoxODio8XtEdWtHql8k6U9tL5H0A0mvsP29umWWStq7ML+XpN+3sKZtdsIrpmiNhoL1rXdv0JrcXnW7bGN7lXzkgDStBenatNZedX9y4P5avWkoWP/2wcVavSm1V93RB++nxzcNBev7ltyvxzel9qr74L5pWgvStWmtvepee8g8rdk0FKzvXfyg1mxK7VW3f/csrd40FKz7V/Vp9abUXnWzZnRqYHAoWA8MDGhgMLVPBF1Pm6r1g0PBev369Vo/mNqrbub0aVo3OBSs161bp3WDqb3qdtqpQ4ODQ8F6cHBQg4OpfbxyxFYDw2P/IPaRkt7f4Owfr5b0D0pn/zhc0pcj4rCR1tXT0xMLFy5sVakNcfaPauLsH9XF2T+qi7N/VBNn/6guzv7RWrZvioieppbd0aHa9omSFBFn5lPqnSHpKKVT6r0zIkZMzO0I1QAAAHjq2ZZQvUOOKIqIKyVdma+fWWgPSX+/I2oAAAAAWoX/qAgAAACURKgGAAAASiJUAwAAACURqgEAAICSCNUAAABASYRqAAAAoCRCNQAAAFASoRoAAAAoiVANAAAAlESoBgAAAEoiVAMAAAAlEaoBAACAkgjVAAAAQEmEagAAAKAkQjUAAABQEqEaAAAAKIlQDQAAAJREqAYAAABKIlQDAAAAJRGqAQAAgJII1QAAAEBJhGoAAACgJEI1AAAAUBKhGgAAACiJUA0AAACURKgGAAAASiJUAwAAACURqgEAAICSWhaqbU+zfYPtW23faftfGixzpO3Vtm/Jl4+2qh4AAACgVSa3cN3rJb0iItbYniLpWtuXRsR1dctdExGvaWEdAAAAQEu1LFRHREhak2en5Eu06vEAAACAdmnpMdW2O2zfIqlX0s8j4voGix2RDxG51PZBrawHAAAAaIWWhuqIGIyIQyTtJekw2wfXLXKzpH0i4rmSviLpokbrsX2C7YW2Fy5fvryVJQMAAADbbIec/SMiHpN0paSj6tr7I2JNvv5TSVNsz25w/7Mjoicierq7u3dEyQAAAEDTWnn2j27bM/P1TkmvlHR33TJzbTtfPyzXs7JVNQEAAACt0Mqzf+wh6Tu2O5TC8vkR8RPbJ0pSRJwp6Y2S3m17o6QBScflHzgCAAAAldHKs3/cJul5DdrPLFw/Q9IZraoBAAAA2BH4j4oAAABASYRqAAAAoCRCNQAAAFASoRoAAAAoiVANAAAAlESoBgAAAEoiVAMAAAAlEaoBAACAkgjVAAAAQEmEagAAAKAkQjUAAABQEqEaAAAAKIlQDQAAAJREqAYAAABKIlQDAAAAJRGqAQAAgJII1QAAAEBJhGoAAACgJEI1AAAAUBKhGgAAACiJUA0AAACURKgGAAAASiJUAwAAACURqgEAAICSCNUAAABASYRqAAAAoCRCNQAAAFASoRoAAAAoqWWh2vY02zfYvtX2nbb/pcEytv1l24tt32b7+a2qBwAAAGiVyS1c93pJr4iINbanSLrW9qURcV1hmaMlHZAvh0v6ep6OK+edt0Cfvk1aI2mGpNOeI731ra9ud1ljYv6pC7ZqW3L6xOjbl760QF94eGj+fXtI73nPxOibJF144QJ9/gbpEUlzJZ18mPRnfzYx+nfKqQt0QWH+DZL+Y4Jsl6efvkBnPjY0f+JM6dRTJ0bfJOnccxfoU7dLT0h6mqQPPVv6i7+YGP1bsWKF7n50hfr7pa4u6Vm7z9bs2bPbXdaYWLt2rXr712rtOmn6NGlO13RNnz693WWNmb6+Pi1e3rf5tdu/e5ZmzZrV7rLGxLJly3TDg8s29+2weXtqzz33bHdZY2JgYEB9awY08KTUuZM0a0anOjs7213WsFo2Uh3Jmjw7JV+ibrFjJZ2Tl71O0kzbe7Sqpu1x3nkLdFoO1IfPTtPTbkvtVdcoUI/UXiXFQL1fbvvCw6l9IrjwwgU6OQfqF+2VpiffkNqrrhioa3/1X5Dbq64YqGs7ujMfS+0TwbnnLtCHcqA+bLc0/dDtqb3qVqxYoZ/dkwL1zF1nq79f+tk9K7RixYp2l1ba2rVrdcfDOVBPn66165Tm165td2ljoq+vT1fclwP1rrPU3y9dcV+f+vr62l1aacuWLdMPb0uBevdZe6q/X/rhbcu0bNmydpdW2sDAgBYvz4G6s1MDTyrNDwy0u7RhtfSYatsdtm+R1Cvp5xFxfd0ie0p6qDC/NLeNG5++LU0Pn73ltNaO8ak+UO9X1151n78hBc4X7ZXmX7RXmv/8De2samzUB+rJde1VVh+o96hrr7pP3Z6mh+225bTWXmV3P7pCu05KgVpK010npfaq6+1fq+kd2jwyPX36dE3vSO0TweLlfdplUgrUUpruMim1V90NDy7TbpNSoJbSdLdJqb3q+tYMqLNDm0emOzs71dmR2serlobqiBiMiEMk7SXpMNsH1y3iRnerb7B9gu2FthcuX768FaUOqzZCXVQbscb4tt8o81VWG6Euqo1YTwT1x6W18ji1Ha3+q7hx9dVcSbUR6qLaiHXV1Uaoi2oj1lVXG6Euqo1YTwS1Eeqi2oh11dVGqItqI9ZVVxuhLqqNWI9XO+TsHxHxmKQrJR1Vd9NSSXsX5veS9PsG9z87Inoioqe7u7tldTYyQ9L1dQMR169I7Rjf7h9lvsrmSvrV0i3bfrU0tU8EG0eZr7L6L0smyJcnktIx1Des3LLthpWpveq6uqTHVm35YfDYqhXq6mpTQWNo+jRtdajH2rVrNX1amwoaY11dUv+qLUel+1f1TYjXrqtLerRvy1HpR/uWTYi+de6krQ71GBgYUOdObSqoCa08+0e37Zn5eqekV0q6u26xiyW9I58F5IWSVkfEuPqMOe05aVoL1rVprR3j0/vy8F8tSN9f1151Jx+WgmYtWP9qaZo/+bB2VjU23pCnG+umb2iwbNWcODNNazu5h+vaq+5Dz07TWrCuTWvtVfas3Wdr1aahYP3YqhVatSm1V92crulaOzgUrNeuXau1g6l9Iti/e5ZWbxoK1v2r+rR6U2qvusPm7amVm4aC9aN9y7RyU2qvulkzOjUwOBSsBwYGNDCY2scrR2x1tMXYrNh+jqTvSOpQCu/nR8QnbJ8oSRFxpm1LOkNpBPsJSe+MiIUjrbenpycWLhxxkTHH2T+qibN/VBdn/6guzv5RTZz9o7o4+0dr2b4pInqaWrZVobpV2hGqAQAA8NSzLaGa/6gIAAAAlESoBgAAAEoiVAMAAAAlEaoBAACAkgjVAAAAQEmEagAAAKAkQjUAAABQEqEaAAAAKIlQDQAAAJREqAYAAABKIlQDAAAAJRGqAQAAgJII1QAAAEBJhGoAAACgJEI1AAAAUBKhGgAAACiJUA0AAACURKgGAAAASiJUAwAAACURqgEAAICSCNUAAABASYRqAAAAoCRCNQAAAFASoRoAAAAoiVANAAAAlESoBgAAAEoiVAMAAAAlEaoBAACAkloWqm3vbfsK24ts32n7PQ2WOdL2atu35MtHW1UPAAAA0CqTW7jujZJOiYibbe8s6SbbP4+Iu+qWuyYiXtPCOgAAAICWatlIdUQ8HBE35+uPS1okac9WPR4AAADQLjvkmGrb8yU9T9L1DW4+wvatti+1fdCOqAcAAAAYS608/EOSZHuGpAskvTci+utuvlnSPhGxxvYxki6SdECDdZwg6QRJmjdvXosrBgAAALZNS0eqbU9RCtTnRsSF9bdHRH9ErMnXfyppiu3ZDZY7OyJ6IqKnu7u7lSUDAAAA26yVZ/+wpG9KWhQRnx9mmbl5Odk+LNezslU1AQAAAK3QysM/XiTp7ZJut31LbjtN0jxJiogzJb1R0rttb5Q0IOm4iIgW1gQAAACMuZaF6oi4VpJHWeYMSWe0qgYAAABgRxj18A8nb6v9Yxbb8/KhGgAAAADU3DHVX5N0hKS35PnHJX21ZRUBAAAAFdPM4R+HR8Tzbf9GkiJile2dWlwXAAAAUBnNjFRvsN0hKSTJdrekTS2tCgAAAKiQZkL1lyX9WNIc25+SdK2kT7e0KgAAAKBCRj38IyLOtX2TpD9SOpvH6yJiUcsrAwAAACpi2FBte1ZhtlfS94u3RURfKwsDAAAAqmKkkeqblI6jttI/bFmVr8+U9KCkfVteHQAAAFABwx5THRH7RsR+kn4m6bURMTsidpP0GkkX7qgCAQAAgPGumR8qHhoRP63NRMSlkl7WupIAAACAamnmPNUrbH9Y0veUDgd5m6SVLa0KAAAAqJBmRqrfIqlb6bR6F0mao6H/rggAAAA85TVzSr0+Se+x3SVpU0SsaX1ZAAAAQHWMOlJt+9n5X5TfLulO2zfZPrj1pQEAAADV0MzhH2dJOjki9omIfSSdIuns1pYFAAAAVEczoXp6RFxRm4mIKyVNb1lFAAAAQMU0c/aP+21/RNJ38/zbJP2udSUBAAAA1dLMSPVfKZ3948J8mS3pna0sCgAAAKiSYUeqbU+TtHNELJd0UqF9d0kDO6A2AAAAoBJGGqn+sqSXNGh/paQvtKYcAAAAoHpGCtUvjogL6xsj4lxJL21dSQAAAEC1jBSqvZ33AwAAAJ5SRgrHvbYPq2+0faik5a0rCQAAAKiWkU6p90+Szrf9bUk35bYeSe+QdFyL6wIAAAAqY9iR6oi4QdJhSoeBHJ8vlnR4RFy/I4oDAAAAqmDEf/4SEb2SPraDagEAAAAqiR8cAgAAACURqgEAAICStilU255ku6vJZfe2fYXtRbbvtP2eBsvY9pdtL7Z9m+3nb0s9AAAAwHgw4jHVkmT7PEknShpUOgvILrY/HxGfG+WuGyWdEhE3295Z0k22fx4RdxWWOVrSAflyuKSv5+m4ct55C/Tp26Q1kmZIOu050lvf+up2lzUm5p+6YKu2JafTtyq45ppr9N0r+/XIWmnudOntR3bpJS9p9E9Qq+cLX1igLz06NP+e3aX3vW9ivHYTfbt836kL9OPC/OslfWGC9O+BBx7Q1fc/oP5+qatLeul++2ifffZpd1ljYuXKlbq3d+Xmvj1jzm7abbfd2l3WmNmwYYOeWLdBGzZJUyZJT5s2RVOmTGl3WWOit7dXdzzcu/m1O3iPOZozZ067yxoTGzdu1Lr1G7UxpMmWpk2drMmTR42ubdPMSPWBEdEv6XWSfippnqS3j3aniHg4Im7O1x+XtEjSnnWLHSvpnEiukzTT9h7b0oFWO++8BTotB+rDZ6fpabel9qpr9OE+UnuVTOS+SSlQf2xBCtTPeF6XHlkrfWxBv6655pp2l1ZaMVDPz21fejS1V91E3y6Lgbr24fLj3F51DzzwgL5zc+XJbIYAACAASURBVArUe87ZR/390ndufkAPPPBAu0srbeXKlbr83hSod9l1N/X3S5ffu1IrV65sd2ljYsOGDVrxeA7UU6Zowyal+Q0b2l1aab29vbpkUQrUs3ado/5+6ZJFvert7W13aaVt3LhRj63NgXryZG0MpfmNG9td2rCaCdVTbE9RCtX/ExEbJMW2PIjt+ZKeJ6n+VHx7SnqoML9UWwfvtvr0bWl6+Owtp7V2oB2+e2W/ZigFauXpjNxedfWBen5dO8av+kA9qa69yq6+/wHtNikFailNd5uU2qvu3t6VmjkpBWopTWdOSu0TwRPrNmhKhzaPTE+ZMkVTOlJ71d3xcK92m5QCtZSmu01K7VW3bv1GTe7Q5pHpyZMna3JHah+vmgnVZ0laImm6pKtt7yOp6U9u2zMkXSDpvXnEe4ubG9xlq8Bu+wTbC20vXL58x/4zx9oIdVFtxBpol9oIdVFtxHoimD/KPMav+g+VifJr+NoIdVFtxLrqaiPURbUR64mgNkJdVBuxrrraCHVRbcS66moj1EW1EevxatT9XUR8OSL2jIhj8mEaD0h6eTMrzyPcF0g6NyIubLDIUkl7F+b3kvT7BjWcHRE9EdHT3d3dzEOPmRmSrl+xZdv1K1I70C5zp0v3/mbLvea9v+nX3OltKmiMLRllHuNXfU6ZALlFUjpWdVnvlqPSy3ofUFdTP90f37q6pNWrthyVXr1q5YTom5SOoa4/1GPDhg2aMgH+4uvqkvpWbTkq3beqd0K8dpOtrQ712LhxoyY3Go4dJ0bdpGzvbvubti/N8wdK+ssm7mdJ35S0KCI+P8xiF0t6Rz4LyAslrY6Ih5svv/VOe06a1oJ1bVprB9rh7Ud2aY2GgvW9v+nXmtxede/ZPU2XaMtprR3j1+vzdFPd9PUNlq2al+63j1ZuGgrWy3of0MpNqb3qnjFnNz22aShYr161Uo9tSu0TwdOmTdGGwaFgvWHDBm0YTO1Vd/Aec7Ry01Cw7lvVq5WbUnvVTZs6WRsHh4L1xo0btXEwtY9Xjhh5HD2H6f+S9KGIeK7tyZJ+ExHPHuV+L5Z0jaTbNbRvPU3ph46KiDNz8D5D0lGSnpD0zohYONJ6e3p6YuHCERcZc5z9o5omct8kzv5RVRN9u+TsH9XE2T+qi7N/tJbtmyKip6llmwjVN0bEobZ/ExHPy223RMQhY1DrNmtHqAYAAMBTz7aE6maOKFprezflHxDWDtMoUR8AAAAwoTQzhn6y0rHPT7f9K0ndkt7Y0qoAAACACmkmVPdJepmkZyqdAu8eSW059AMAAAAYj5o5/OMCSbtHxJ0RcYekIyR9q7VlAQAAANXRTKg+UdJFtufaPkbSlyUd09qyAAAAgOoY9fCPiLjR9kmSLpO0TtIfR8SO/beGAAAAwDg2bKi2fYm2/JfhT1M668c3bSsi/rTVxQEAAABVMNJI9b/vsCoAAACAChs2VEfEVbXrtneXdGievSEiehvfCwAAAHjqGfWHirbfJOkGSX8u6U2SrrfNeaoBAACArJnzVH9I0qG10Wnb3ZIul/SjVhYGAAAAVEUzp9SbVHe4x8om7wcAAAA8JTQzUv2/tn8m6ft5/s2SLm1dSQAAAEC1NHOe6n+y/QZJL1L6N+VnR8SPW14ZAAAAUBHNjFQrIi6w/fPa8rZnRURfSysDAAAAKmLUUG37XZI+IWlA0ial0eqQtF9rSwMAAACqoZmR6vdLOigiVrS6GAAAAKCKmjmLx28lPdHqQgAAAICqamak+oOS/s/29ZLW1xoj4qSWVQUAAABUSDOh+ixJv5R0u9Ix1QAAAAAKmgnVGyPi5JZXAgAAAFRUM8dUX2H7BNt72J5Vu7S8MgAAAKAimhmpfmuefrDQxin1AAAAgKyZ/6i4744oBAAAAKiqYQ//sH2o7bmF+XfY/h/bX+bwDwAAAGDISMdUnyXpSUmy/VJJp0s6R9JqSWe3vjQAAACgGkY6/KMjIvry9TdLOjsiLpB0ge1bWl8aAAAAUA0jjVR32K6F7j9SOld1TTM/cAQAAACeEkYK1d+XdJXt/5E0IOkaSbK9v9IhICOy/S3bvbbvGOb2I22vtn1Lvnx0O+oHAAAA2m7YEeeI+JTtX0jaQ9JlERH5pkmS/rGJdX9b0hlKx2EP55qIeE2TtQIAAADj0oiHcUTEdQ3a7m1mxRFxte3521cWAAAAUB3N/EfFVjrC9q22L7V9UJtrAQAAALZLO39weLOkfSJije1jJF0k6YBGC9o+QdIJkjRv3rwdVyEAAADQhLaNVEdEf0Ssydd/KmmK7dnDLHt2RPRERE93d/cOrRMAAAAYTdtCte25tp2vH5ZrWdmuegAAAIDt1bLDP2x/X9KRkmbbXirpY5KmSFJEnCnpjZLebXuj0in7jiucYQQAAACojJaF6oh4yyi3n6F0yj0AAACg0tp99g8AAACg8gjVAAAAQEmEagAAAKAkQjUAAABQEqEaAAAAKIlQDQAAAJREqAYAAABKIlQDAAAAJRGqAQAAgJII1QAAAEBJhGoAAACgJEI1AAAAUBKhGgAAACiJUA0AAACURKgGAAAASiJUAwAAACURqgEAAICSCNUAAABASYRqAAAAoCRCNQAAAFASoRoAAAAoiVANAAAAlESoBgAAAEoiVAMAAAAlEaoBAACAkgjVAAAAQEmEagAAAKAkQjUAAABQUstCte1v2e61fccwt9v2l20vtn2b7ee3qhYAAACglSa3cN3flnSGpHOGuf1oSQfky+GSvp6n487XvrZAn31waP4D86S/+7tXt6+gMTT/1AVbtS05nb5VwaWXXqqvX7VJj0iaK+ndL5uko48+ut1ljYl3nrpAVxTmXy7pvybIazfRt8tTTl2gCwrzb5D0HxOkf4sXL9bl9yxWX780q0t65TP31/7779/ussZEf3+/lq7qV/9aqWu6tNeuXerq6mp3WWNm+fLluuuR5ervl7q6pAPndqu7u7vdZY2J3t5e3fFw7+a+HbzHHM2ZM6fdZY2JgYEB9a0Z0MCTUudO0qwZners7Gx3WcNq2Uh1RFwtqW+ERY6VdE4k10maaXuPVtWzvYqB+pn5T5DPPpjaq67Rh/tI7VUykfsmpUB9Sg7Uzz9gkh6RdMpVm3TppZe2u7TS6gO1JF2R26tuom+XxUBdG7G5ILdX3eLFi3Xm9SlQz99zf/X1S2dev1iLFy9ud2ml9ff368YHcqDu6lL/WqX5/v52lzYmli9frp/enQL1rrt2q79f+undy7V8+fJ2l1Zab2+vLlmUAvWsXeeov1+6ZFGvent7211aaQMDA1q8PAfqzk4NPKk0PzDQ7tKG1c5jqveU9FBhfmluG1fqA3UxWAPt8vWrNmmGUqBWns7I7VVXH6hHa8f4UR+oJ9e1V9nl9yzWrEkpUEtpOmtSaq+6pav6Nb1Dm0emu7q6NL0jtU8Edz2yXLMmpUAtpemsSam96u54uFe7TUqBWkrT3Sal9qrrWzOgzg5tHpnu7OxUZ0dqH6/aGardoC0aLmifYHuh7YXt+MvymZNHngd2tNoIdVFtxBpop/rd40TZXdZGqItqI9ZVVxuhLqqNWE8EtRHqotqIddXVRqiLaiPWVVcboS6qjViPV+0M1Usl7V2Y30vS7xstGBFnR0RPRPS04xioezaOPA/saHMl3XzflqPSN9+3SXPbUw6wWf3ucaLsLmd1SUuWbTkqvWTZYs2aAIcdd03XVod69Pf3q2t6mwoaY11d0qpVWw7IrVq1XBPhkPGuLqlv1Zaj0n2reidE3zp30laHegwMDKhzpzYV1IR2huqLJb0jnwXkhZJWR8TDbaynoQ/MS9NakK5Na+1AO7z7ZZO0RkPB+ub7NmlNbq+6l29jO8aPN+TpxrrpGxosWzWvfOb+6ts0FKyXLFusvk2pver22rVLaweHgnV/f7/WDqb2ieDAud3q2zQUrFetWq6+Tam96g7eY45WbhoK1n2rerVyU2qvulkzOjUwOBSsBwYGNDCY2scrRzQ84qL8iu3vSzpS0mxJj0r6mKQpkhQRZ9q20tlBjpL0hKR3RsTC0dbb09MTCxeOutiY4uwf1TSR+yZx9o+qmujbJWf/qCbO/lFdnP2jtWzfFBE9TS3bqlDdKu0I1QAAAHjq2ZZQXf3vigEAAIA2I1QDAAAAJRGqAQAAgJII1QAAAEBJhGoAAACgJEI1AAAAUBKhGgAAACiJUA0AAACURKgGAAAASiJUAwAAACURqgEAAICSCNUAAABASYRqAAAAoCRCNQAAAFASoRoAAAAoiVANAAAAlESoBgAAAEoiVAMAAAAlEaoBAACAkgjVAAAAQEmEagAAAKAkQjUAAABQEqEaAAAAKIlQDQAAAJREqAYAAABKIlQDAAAAJRGqAQAAgJII1QAAAEBJLQ3Vto+yfY/txbZPbXD7kbZX274lXz7aynoAAACAVpjcqhXb7pD0VUl/LGmppBttXxwRd9Utek1EvKZVdQAAAACt1sqR6sMkLY6I+yPiSUk/kHRsCx8PAAAAaItWhuo9JT1UmF+a2+odYftW25faPqiF9QAAAAAt0bLDPyS5QVvUzd8saZ+IWGP7GEkXSTpgqxXZJ0g6QZLmzZs31nUCAAAApbRypHqppL0L83tJ+n1xgYjoj4g1+fpPJU2xPbt+RRFxdkT0RERPd3d3C0sGAAAAtl0rQ/WNkg6wva/tnSQdJ+ni4gK259p2vn5YrmdlC2sCAAAAxlzLDv+IiI22/0HSzyR1SPpWRNxp+8R8+5mS3ijp3bY3ShqQdFxE1B8iAgAAAIxrrlqG7enpiYULF7a7DAAAAExwtm+KiJ5mluU/KgIAAAAlEaoBAACAkgjVAAAAQEmEagAAAKAkQjUAAABQEqEaAAAAKIlQDQAAAJREqAYAAABKIlQDAAAAJRGqAQAAgJII1QAAAEBJhGoAAACgJEI1AAAAUBKhGgAAACiJUA0AAACURKgGAAAASiJUAwAAACURqgEAAICSCNUAAABASYRqAAAAoCRCNQAAAFASoRoAAAAoiVANAAAAlESoBgAAAEoiVAMAAAAlEaoBAACAkgjVAAAAQEmEagAAAKAkQjUAAABQ0uRWrtz2UZK+JKlD0jci4vS6251vP0bSE5KOj4ibW1nT9ph/6oKt2pac/uo2VDL26Ft1TeT+0bfqmsj9m8h9+8CpC3R+Yf5Nkj47QfomSa87dYFuKcwfIumiCdK/s85aoM/8bmj+g/tK73rXxOjbjTfeqP++rleP9Etzu6Q/f+EcHXrooe0ua1gtG6m23SHpq5KOlnSgpLfYPrBusaMlHZAvJ0j6eqvq2V6NdqIjtVcJfauuidw/+lZdE7l/E7lvxUA9NU/Pz+0TQX2glqRbcnvVFQP1gfnF+8zvUnvV3XjjjfrM/6ZA/YyD5+iRfukz/9urG2+8sd2lDauVh38cJmlxRNwfEU9K+oGkY+uWOVbSOZFcJ2mm7T1aWBMAACioD9RT69qrrj5Qj9ZeJfWBuhisq+6/r+vVjEkpUEtpOmNSah+vWhmq95T0UGF+aW7b1mVk+wTbC20vXL58+ZgXCgDAU9nUUeYxfh04deT5qqqNUBfVRqzHq1aGajdoi+1YRhFxdkT0RERPd3f3mBQHAACS9aPMY/y6a/3I81U1t0u6944tR6XvvaNXc7vaVFATWhmql0rauzC/l6Tfb8cyAACgRd6Up+vrpm9qsGwVHbKN7VXywX3TtBaka9Nae5X9+QvnaM2moWB97x29WrMptY9XrQzVN0o6wPa+tneSdJyki+uWuVjSO5y8UNLqiHi4hTVts+F+2T0RfvFN36prIvePvlXXRO7fRO7bZ09/9VbBeiKd/eOi01+9VYCeKGf/eNe7Xr1VsJ4oZ/849NBD9cGj5mwesZ7bJX3wqPF99g9HbHW0xdit3D5G0heVTqn3rYj4lO0TJSkizsyn1DtD0lFKp9R7Z0QsHGmdPT09sXDhiIsAAAAApdm+KSJ6mlm2peepjoifSvppXduZhesh6e9bWQMAAADQavxHRQAAAKAkQjUAAABQEqEaAAAAKIlQDQAAAJREqAYAAABKIlQDAAAAJRGqAQAAgJII1QAAAEBJhGoAAACgpJb+m/JWsL1c0gNtevjZkla06bFbjb5V10TuH32rroncP/pWXRO5f/StNfaJiO5mFqxcqG4n2wub/f/vVUPfqmsi94++VddE7h99q66J3D/61n4c/gEAAACURKgGAAAASiJUb5uz211AC9G36prI/aNv1TWR+0ffqmsi94++tRnHVAMAAAAlMVINAAAAlDRhQ7XtK2335Os/tT0zX/6usMx8228tzB9p+yctrOkQ28e0av1PFbZPK1yfb/uOYZb7tu03jrKu423/QWH+G7YPHLtqt4/tJbZnN2j/v3bUM9aK78WR3nfj5fUYC7ZPsr3I9irbp+a2102U/o0Hhef43FGWW5OnI+0/Pmf7zjw90fY7WlHzCDUeb/uMEvff5n1FM/vMEe77cdvv3577jie2P2H7laMs07Cv9RmjXYr5Zwc+5nttP60wv2ZHPv5o9ewoEzZUF0XEMRHxmKSZkoob/HxJb214p9Y4RFLDUG178g6soyHbHe2uoUmnjb5I046XtDlUR8TfRMRdY7j+MRUR/6/dNYyR+vdiQ+P99dhGfyfpmIjYNSJOz22vkzQhQ3Wb9ie15/gvxmBd75L0/Ij4p4g4MyLOGYN17rDnZQLtK7aJk+3ONhHx0Yi4fDvv3tR+rYqaeF7fK2lMQuwY5aExq2dbVDJU277I9k15FOGEJpavjfqdLunptm+x/bk8/5I8/766+0y3/S3bN9r+je1jh1n3P+VlbrP9L7nt9bYvzxvhHrbvtT1P0ickvTk/3pvzX7tn275M0jl51OQa2zfny4g7xfrnwfa7bX+2cPvxtr+Sr7/N9g35sc+q7dhtr8l/mV8v6QjbH839uSPX5rzcobmPv84jN3fk9o48X3sO3jXa67EtGvTxdEmduR+10agO2/+Zl7nMdmeD9bzA9lV5XT/Lr8sbJfVIOjevr9NbfsNxVH4dbrX9i7HsV11t020vyI9zh+03F27rtP2/tv82z9dG2I7Mtf7I9t22zy28Vkts/0uu/Xbbz7I9yfZ9trvzMpNsL3aD0fAdZPN7UdLnJM0Ypi/F12ON7U/l5+k627u3qfZtZvtMSftJutj2+2yfkd/ffyrpc3n7e3p7qxye7Xfk9/ettr/ruhHNuu3yCtvnSbrd9r95y28HP277lLyOYwvt59r+05I11j/HW4wm5vfW/CbXdbGk6ZKu99C++v35tuH2hVuMLNv+ie0j8/X6/exw++N3On1eXCXpRSWfjzW2Z9j+RWFfUHzOt3hNG9z/k/l1nuQGn3N5mQ/Zvsf25ZKeWaLWk/Prc4fTCGPD7SZfb/SZO9/pG4qvSbpZ0tttfz7f9h7b9+frT7d9bb6+1WdCbt+8bds+Ju+TrrX9ZW/5jdqBef90v+2Tclt9xhit31t8U2L7/bY/nq+fZPuu3M8f5LaGucTpc+IHedkfStrqMzAvt/kbUNs9tq8sPL/ftf1Lp8+J2udN/fO6t+2v217o9Hlbe/5PUhqcusL2FYXH22p/bbvb9gW5DzfaflGhhs15qK7ukT7v/ig/F7fn52bqcPUU1nd64bn999z2WtvX53Vdbnt3b8/nZkRU7iJpVp52SrpD0m4NlrlSUk++vkTpv/HMl3RHYZkjJf2k0bykT0t6W74+U9K9kqbXPcarlH6RaqU/UH4i6aX5tu9J+ofc9pbcdrykMwr3/7ikmyR15vmnSZqWrx8gaeE2Pg+7S1pcuP1SSS+W9IeSLpE0Jbd/TdI78vWQ9Kb6debr35X02nz9Dkn/L18/vfY8SjpB0ofz9amSFkrat5WvtaQ1hdvnS9oo6ZA8f37hdfu2pDdKmiLp/yR15/Y3S/pW/XZSnJfULemhWl+Kz0sLtuc3SPrPwvwueZudL+ny2muVb1tT2FZXS9orb3u/lvTiwvb+j/n630n6Rr7+MUnvLWy7F+zo927d61bbhkbqy+bXJ2+rte3xs7XtrioXDe2HjlfeD9S20XbXNkrdB0m6R9LsPD+rvu667XJt4X3zPElXFZa7S9I8SS+TdFFu20XS7yRNHqvnOF//uKT3F267Q9L8uno3b4cN1lXcz2xel4bfF25+XfP8TyQdWdh235SvN9wfS9pD0oNK+56dJP2quL7teC7WSJosqSvPz5a0WOnzaqvXtLg95vfXWXnZhp9zkl4g6Xalz62uvO73b0edtfVMlzRD0p0jbDfD1TJf0iZJL8zLz5V0Y77+I0k3StpT0l9K+oxG/kyoPQfTtOVnwPc1lA8+nu8/NT+vK/M6h92ehun7FstLer+kj+frv5c0NV+fmacNc4mkkwv1P0fpM7GnweMtKbzmPZKuLPTnVqXP2dm5339Q/7zWbSsdSvvn59Svu7DNb7W/lnSehvbv8yQtKtSwOQ/V1X2kGnxGFF6jZ+TlztHQZ9wW9RTrV9r2Xffc7lpo+xtJ/5Gvb9PnZtsPOdhOJ9l+fb6+t1IAXTnGj/EqSX/qoZGOacobQN0yr5L0mzw/I9dytaR/VNr5XhcR3x/hcS6OiIF8fYqkM2wfImlQ0jNGqbH+edhX0v22XyjpPqWRg19J+nulHdeN+Y+7Tkm9+X6Dki4orPPltj+gtKOcJelO29dI2jkiasfonSfpNYXn4DkeGrXaJT8Hvxul9mY1eq3r/S4ibsnXb1LaERQ9U9LBkn6e+98h6eFRHveFkq6OiN9JUkT0bXvpTbtd0r/b/jelnfY1uc7/kfTZiBju+NAbImKpJDmN+M6XdG2+7cI8vUnSn+Xr38rr/KKkv5L0X2PcjzJG6kvNk0ofolLq1x/vsOqe2l4h6UcRsUJK74W8fQ7nhsL75je25zj9bqFb0qqIeFDSg7a/anuO0vZ5QURsbG03yrM9U8PvC0dS3M/+kRrvjw9XCjnL82P9UKN/BoxasqRP236pUjjaU2nwZavXtHCfj0i6PiJOyHUM9zm3s6QfR8QTebmLt7PGF+f1rM3ruVDSSyRttd3kEchGtTwo6YGIuC735xGnUfqdlT43zlMK3y9R2jc285nwLEn317ZlpVBd/GZ8QUSsl7Tedq/S8zqWblP6FvUiSRfltuFyyUslfVmSIuI227dtx+P9T84iA3l09zBJt6jwvGZvcjpCYLLSH4IH5lrrDbe/fqXSKH9tua78Oklb5qF6jT4jHlf6/L83L/MdpbzzxRH62S9pnaRv2F5QqHEvST90+sZiJw1lmG363KxcqHb6Ou2Vko6IiCfy1xfTWvFQkt4QEfeMssxnIuKsBrftqbQT2932pIjYNMw61hauv0/So5Keq/TX2LphH3j45+GHkt4k6W6lHVXkr0m+ExEfbLCqdRExmNc5TWnUpCciHspfQ03L/Ry2FKVR0Z+NsMx22YbXen3h+qC2/urLku6MiCO25eGV/tJuuYi41/YLlI63/0z++ktKfxAdbfu8yH8m16nv9+QGt21uz6/po7ZfofQBPhbHnY6VkfpSs6HwPAy3DMZeo/fCRuXDB/P+ZafCbWvrlv2R0sjfXEk/KLR/V2kbPE7pw2qsba4xG4vPiZH2hSM93ub9bF7HVvtj26/T2O9z/kIplL4gIjbYXqKhffpwj3WjpBfYnpXDdsPPOdvvHaN6h3tOG203w9UyX1tvd7+W9E6lUclrlLaxIySdohRER/tMGPEvRzW3zxrNSNvMq5XC8p9K+ojtgzRMLskBtZnXovh49e+H+vvX5jc/r7b3VRpNPzQiVtn+doP11Ay3v56k9Jm+RXjOfah/DYsaPd+jvUZbiYiNtg9T+uP2OKUjCl4h6SuSPh8RF+fs8fG8/DZ9blbxmOpdlP5qfcL2s5RGFJv1uNJf18PNF/1M0j8Wjtt53jDL/JXtGXmZPfOozGSlv2beqjSyfXITjyelvj2cA/jblf56HmnZRs/DhUo/fnqLUsCWpF9IemMeFZLtWbb3abDO2ptjRe7TGyUpIlZJejyPgEtpQyw+B++2PSWv+xm2p49Q97YYro8bao/XpHskdds+Itc4Je+gpOFfk19Lelneicj2rO3qQRPyaMwTEfE9Sf8u6fn5po8qfQPztTF8uG8oHZp0fuFDvh1Gey88VVThefiF0ujUbtLm98ISpdFWSTpW6Vu24fxAaZ/xRqWgVPNtpR8TKSLuHNOKkyXK7yXbz1f6Jq+UUfaFSyQdko+73FtppK+R4fbH10s60vZuef/252XrVdqH9uZA/XJJtf1+o9e05n+VDmtZkEcQG37OKX0j+3qn43l3lvTa7azxakmvs/20/NnxeqUQ3Gi7Ga6W4db7/jz9jaSXS1ofEas18mdCzd2S9vPQcfhv1ui29f38qNKI/G62pyp/6+H0g8C9I+IKSR9QOtRjhobPJVcrhz3bBysdAtLIEg29b99Qd9uxtqflbeJIpT+u6nUpBd/VTsdIH124rdm+X6YUZJXrPaSJ+wznbknzbe+f598u6aqR6snbzi4R8VOl/U/t8XeRtCxf/8u6uzX9uVnFUP2/kibnrzc+Kem6UZbfLCJWSvqV048hPqf0lcX/b+/eQqyq4jiOf39IpJQRlUUF9VQZdPGhIKPSii5QEPVQPUh2hXrpAhVBPRgWEkXSvcxKulAxpGVJpZHpIJNjzjgzOUj1Fll2NySlsn8P/3XwOJ0z50xHmzPj7wOCrLPX2WvvPXvv/1l7rf3/SzmQ/o4hi88lbxT9yokEc2t833LysVKXpAHyxJ9Mvp2iMyI6yYD6RkknAivJxx4bVDUZrcrTwGxJn5KP/Yb71VZzP5SL/iBwbER0l7JB4D5geVl+BfnYZuj2/Ao8Tw5HeJvdT6obgAWSushfh1tL+cKyvp6yn55jz/Ug1jvWC8jjMuxrsyoi881bhwAABPpJREFU4g/ywvyQpD7ykVZlEugi4NlyTCZV1fmBfNS3uNR5k73nZKC7PNK6F3ig6rPbgYmqmoDaoqXkxXlUh35Un4vkRMV91RvAXcrJMW05UbEEvA8Cq8q58Ch5nZghqZvsval7rSr1JwPfRMS3VeVbyE6HvfW3+BZwSDmvbiHHn+4J9a6Fa8hHxgPkj+OeWpXrXY/LvplD/qD/qF79EQjgNeA0SZ+RQdem0oZax7S6jR3kMV5KBrj/us9FRA95XdxA7uvO/9TI/J5FQDf5w2JhRPTW+rsZ5p5bSyc59GN1CYS+pgwpa3BPqLRrOzkn5QPl5MYt7DrW9bZlaIzRaNv/JF9gsJYchrCpfDQBeLVsYy8wv9yf68Ulz5CTvfvJILy7zirvBx5TDukcGhx2A8vI++zciNhco719pT0byWERa6o+XgC8rxoTA4e4lfyb7Jc0CNzcYPm6ImIH+TSio+yrv4FnG7RnMvBe2VeryBECkOdeR9k3Pw6p0/R90xkVrSmSDoyIygz/e8ibwG2j3CwbIeWbNOZHxNmj3RbbtynfITtAvrZu2GClnYyFa2HpbeyJiFpPJK1JlWNdeoafAr6MiPmj3a49TTnUc1tEPDLabWlHI7lvjsWeahsdl5Te3M/JyR4PNKpg7aUEAG8BtcbWm/1vlMk1NgFPjKWAumjra2EZTtZF9pZba24qTzo2ksMDas2fsnFspPdN91SbmZmZmbXIPdVmZmZmZi1yUG1mZmZm1iIH1WZmZmZmLXJQbWbWBiTtLBPg+iT1SDqzca3d6i/SrsymjZZdUtb1laSt5f8bRrpOMzPbxRnJzMzaw/aImAYg6SJgHjBjb6woIi4v65kJ3BkRzaTaNjOzYbin2sys/RwE/AKZBlzSwyWhxEAlcVQpf1LSoKRlQCVD3/mSllS+SNIFkhY3WqGkzursZpLWSDpF0hxJr0j6WNKXkm6qWuYuSetKIof799zmm5mNPe6pNjNrD5PKO3EnkhlPzyvlV5CpdE8FDgPWSVoNTAdOIDNyHkFmNn0R+Bh4StKUkhn0OprLWrgQuBa4XdLxwP4R0S/pCjLt8RnAAUBvCeJPAo4j03ELWCrpnIhY3dpuMDMbm9xTbWbWHrZHxLSImApcDLxcMrmdBbweETtLau9VwOnAOVXlm8lgmsjkA68AsyQdTAbf7zex/g7gUkn7AdeTqaMr3omI7RHxI7CSDKQvLP96yZTaU8kg28xsn+SeajOzNhMRXZIOA6aQvcB1F61T/hLwLrAD6IiIv5pY5++SVgCXAVcCpw2znijtmhcRzjJnZoZ7qs3M2o6kqcAE4CdgNXCVpAmSppA91N2l/OpSfiRwbqV+6bneDNzH7j3OjSwEHgfWRcTPVeWXSZoo6VBgJrAO+BC4XtKBpc1HSzr8v2yvmdl44J5qM7P2UBlTDdkLPDsidpZJh9OBPrKH+O6I+K6UnwcMAF+Qw0KqvQZMiYjBZhsQEesl/ca/x2B3A8uAY4C5laBd0olAV45SYRswC/i+6S02MxtHlMPvzMxsPJH0JNAbES+MoM5RwCfA1Ij4u5TNAbZFxCN7o51mZuOFh3+YmY0zktaTb+x4dQR1rgHWAvdWAmozM2uee6rNzMzMzFrknmozMzMzsxY5qDYzMzMza5GDajMzMzOzFjmoNjMzMzNrkYNqMzMzM7MWOag2MzMzM2vRP0z8objTgETmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize = (12,6))\n",
    "plt.scatter(df_body_labels.body_type, df_body_labels.smokes_code, alpha = 0.01)\n",
    "plt.xlabel('Body Type')\n",
    "plt.ylabel('Smokes Code')\n",
    "plt.title('Correlation between Smokes Code and Body Type')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize = (12,6))\n",
    "plt.scatter(df_body_labels.body_type, df_body_labels.drugs_code, alpha = 0.01)\n",
    "plt.xlabel('Body Type')\n",
    "plt.ylabel('Drugs Code')\n",
    "plt.title('Correlation between Drugs Code and Body Type')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To have a look at the correlation between body_type and diet we take just the two columns of the dataframe\n",
    "\n",
    "df_body_diet = df_body_labels[['body_type', 'diet']]\n",
    "\n",
    "f, ax = plt.subplots(figsize = (12, 6))\n",
    "plt.scatter(df_body_labels.body_type, df_body_labels.diet, alpha = 0.1)\n",
    "plt.xlabel('Body Type')\n",
    "plt.ylabel('Diet')\n",
    "plt.title('Correlation between Diet and Body Type')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5609998016266614\n"
     ]
    }
   ],
   "source": [
    "# In the next steps I will try different classifiers to see which performs best. \n",
    "# First MLPClassifer\n",
    "\n",
    "training_data, validation_data, training_labels, validation_labels = train_test_split(df_features_all, df_labels, test_size = 0.2, random_state = 100)\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "clf = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1)\n",
    "\n",
    "clf.fit(training_data, training_labels.values.ravel())\n",
    "print(clf.score(validation_data, validation_labels))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5609998016266614\n",
      "80\n"
     ]
    },
    {
     "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": [
    "# Second: K-Neighbors. To find perfect K, I iterate over K from 20 to 100: \n",
    "\n",
    "training_data, validation_data, training_labels, validation_labels = train_test_split(df_features_all, df_labels, test_size = 0.2, random_state = 100)\n",
    "\n",
    "accuracies = []\n",
    "for k in range(20,100):\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    knn.fit(training_data, training_labels.values.ravel())\n",
    "    knn_score = knn.score(validation_data, validation_labels)\n",
    "    accuracies.append(knn_score)\n",
    "\n",
    "print(max(accuracies))\n",
    "print(len(accuracies))\n",
    "\n",
    "plt.plot(range(20,100), accuracies)\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Validation accuracy')\n",
    "plt.title('Classifier for Body Type')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5528664947431066\n"
     ]
    }
   ],
   "source": [
    "# Third: Random forests: \n",
    "\n",
    "forest = RandomForestClassifier(random_state = 1)\n",
    "forest.fit(training_data, training_labels.values.ravel())\n",
    "print(forest.score(validation_data, validation_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5609998016266614\n",
      "0.5592144415790518\n",
      "0.5604046816107915\n",
      "0.5020829200555446\n"
     ]
    }
   ],
   "source": [
    "# Forth: SVC. We will try out different kernels and obtain the score for each: \n",
    "\n",
    "training_data, validation_data, training_labels, validation_labels = train_test_split(df_features_all, df_labels, test_size = 0.2, random_state = 100)\n",
    "\n",
    "kernels = ['linear', 'poly', 'rbf', 'sigmoid']\n",
    "\n",
    "for ker in kernels:\n",
    "    svc = SVC(kernel = ker, gamma = 3, C = 1)\n",
    "    svc.fit(training_data, training_labels.values.ravel())\n",
    "    print(svc.score(validation_data, validation_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Looks like the best score to be obtained is at roughly 0.561. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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