date_a_scientist.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing libraries and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import relevant libraries\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# set data_location containing profiles.csv file\n",
    "data_location = 'data/profiles.csv'\n",
    "\n",
    "# set branding colours\n",
    "light_blue = '#6fa5d2'\n",
    "medium_blue = '#456785'\n",
    "dark_blue = '#293748'\n",
    "\n",
    "# set max_columns and max_colwidth to make it easier to see data in DataFrame\n",
    "pd.options.display.max_columns = 60\n",
    "pd.options.display.max_colwidth = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "59946\n",
      "['age' 'body_type' 'diet' 'drinks' 'drugs' 'education' 'essay0' 'essay1'\n",
      " 'essay2' 'essay3' 'essay4' 'essay5' 'essay6' 'essay7' 'essay8' 'essay9'\n",
      " 'ethnicity' 'height' 'income' 'job' 'last_online' 'location' 'offspring'\n",
      " 'orientation' 'pets' 'religion' 'sex' 'sign' 'smokes' 'speaks' 'status']\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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>essay4</th>\n",
       "      <th>essay5</th>\n",
       "      <th>essay6</th>\n",
       "      <th>essay7</th>\n",
       "      <th>essay8</th>\n",
       "      <th>essay9</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>height</th>\n",
       "      <th>income</th>\n",
       "      <th>job</th>\n",
       "      <th>last_online</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 that i was some some kind of intellectual:\\neithe...</td>\n",
       "      <td>currently working as an international agent for a freight\\nforwarding company. import, export, d...</td>\n",
       "      <td>making people laugh.&lt;br /&gt;\\nranting about a good salting.&lt;br /&gt;\\nfinding simplicity in complexit...</td>\n",
       "      <td>the way i look. i am a six foot half asian, half caucasian mutt. it\\nmakes it tough not to notic...</td>\n",
       "      <td>books:&lt;br /&gt;\\nabsurdistan, the republic, of mice and men (only book that made me\\nwant to cry), ...</td>\n",
       "      <td>food.&lt;br /&gt;\\nwater.&lt;br /&gt;\\ncell phone.&lt;br /&gt;\\nshelter.</td>\n",
       "      <td>duality and humorous things</td>\n",
       "      <td>trying to find someone to hang out with. i am down for anything\\nexcept a club.</td>\n",
       "      <td>i am new to california and looking for someone to wisper my secrets\\nto.</td>\n",
       "      <td>you want to be swept off your feet!&lt;br /&gt;\\nyou are tired of the norm.&lt;br /&gt;\\nyou want to catch a...</td>\n",
       "      <td>asian, white</td>\n",
       "      <td>75.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>transportation</td>\n",
       "      <td>2012-06-28-20-30</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. i am a workaholic.&lt;br /&gt;\\n2. i love to cook rega...</td>\n",
       "      <td>dedicating everyday to being an unbelievable badass.</td>\n",
       "      <td>being silly. having ridiculous amonts of fun wherever. being a\\nsmart ass. ohh and i can cook. ;)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>i am die hard christopher moore fan. i don't really watch a lot of\\ntv unless there is humor inv...</td>\n",
       "      <td>delicious porkness in all of its glories.&lt;br /&gt;\\nmy big ass doughboy's sinking into 15 new inche...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>i am very open and will share just about anything.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>white</td>\n",
       "      <td>70.0</td>\n",
       "      <td>80000</td>\n",
       "      <td>hospitality / travel</td>\n",
       "      <td>2012-06-29-21-41</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 (poorly)</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 text on an online\\ndating site makes me pleasantly u...</td>\n",
       "      <td>i make nerdy software for musicians, artists, and experimenters to\\nindulge in their own weirdne...</td>\n",
       "      <td>improvising in different contexts. alternating between being\\npresent and decidedly outside of a...</td>\n",
       "      <td>my large jaw and large glasses are the physical things people\\ncomment on the most. when suffici...</td>\n",
       "      <td>okay this is where the cultural matrix gets so specific, it's like\\nbeing in the crosshairs.&lt;br ...</td>\n",
       "      <td>movement&lt;br /&gt;\\nconversation&lt;br /&gt;\\ncreation&lt;br /&gt;\\ncontemplation&lt;br /&gt;\\ntouch&lt;br /&gt;\\nhumor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>viewing. listening. dancing. talking. drinking. performing.</td>\n",
       "      <td>when i was five years old, i was known as \"the boogerman\".</td>\n",
       "      <td>you are bright, open, intense, silly, ironic, critical, caring,\\ngenerous, looking for an explor...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-06-27-09-10</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 according to the library\\nof congress classification s...</td>\n",
       "      <td>socially awkward but i do my best</td>\n",
       "      <td>bataille, celine, beckett. . .&lt;br /&gt;\\nlynch, jarmusch, r.w. fassbender. . .&lt;br /&gt;\\ntwin peaks &amp;a...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cats and german philosophy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>you feel so inclined.</td>\n",
       "      <td>white</td>\n",
       "      <td>71.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>student</td>\n",
       "      <td>2012-06-28-14-22</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 profile i know, more to\\ncome soon. looking to meet n...</td>\n",
       "      <td>work work work work + play</td>\n",
       "      <td>creating imagery to look at:&lt;br /&gt;\\nhttp://bagsbrown.blogspot.com/&lt;br /&gt;\\nhttp://stayruly.blogsp...</td>\n",
       "      <td>i smile a lot and my inquisitive nature</td>\n",
       "      <td>music: bands, rappers, musicians&lt;br /&gt;\\nat the moment: thee oh sees.&lt;br /&gt;\\nforever: wu-tang&lt;br ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>asian, black, other</td>\n",
       "      <td>66.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>artistic / musical / writer</td>\n",
       "      <td>2012-06-27-21-26</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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",
       "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 that i was some some kind of intellectual:\\neithe...   \n",
       "1  i am a chef: this is what that means.<br />\\n1. i am a workaholic.<br />\\n2. i love to cook rega...   \n",
       "2  i'm not ashamed of much, but writing public text on an online\\ndating site makes me pleasantly u...   \n",
       "3                                                            i work in a library and go to school. . .   \n",
       "4  hey how's it going? currently vague on the profile i know, more to\\ncome soon. looking to meet n...   \n",
       "\n",
       "                                                                                                essay1  \\\n",
       "0  currently working as an international agent for a freight\\nforwarding company. import, export, d...   \n",
       "1                                                 dedicating everyday to being an unbelievable badass.   \n",
       "2  i make nerdy software for musicians, artists, and experimenters to\\nindulge in their own weirdne...   \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 good salting.<br />\\nfinding simplicity in complexit...   \n",
       "1    being silly. having ridiculous amonts of fun wherever. being a\\nsmart ass. ohh and i can cook. ;)   \n",
       "2  improvising in different contexts. alternating between being\\npresent and decidedly outside of a...   \n",
       "3  playing synthesizers and organizing books according to the library\\nof congress classification s...   \n",
       "4  creating imagery to look at:<br />\\nhttp://bagsbrown.blogspot.com/<br />\\nhttp://stayruly.blogsp...   \n",
       "\n",
       "                                                                                                essay3  \\\n",
       "0  the way i look. i am a six foot half asian, half caucasian mutt. it\\nmakes it tough not to notic...   \n",
       "1                                                                                                  NaN   \n",
       "2  my large jaw and large glasses are the physical things people\\ncomment on the most. when suffici...   \n",
       "3                                                                    socially awkward but i do my best   \n",
       "4                                                              i smile a lot and my inquisitive nature   \n",
       "\n",
       "                                                                                                essay4  \\\n",
       "0  books:<br />\\nabsurdistan, the republic, of mice and men (only book that made me\\nwant to cry), ...   \n",
       "1  i am die hard christopher moore fan. i don't really watch a lot of\\ntv unless there is humor inv...   \n",
       "2  okay this is where the cultural matrix gets so specific, it's like\\nbeing in the crosshairs.<br ...   \n",
       "3  bataille, celine, beckett. . .<br />\\nlynch, jarmusch, r.w. fassbender. . .<br />\\ntwin peaks &a...   \n",
       "4  music: bands, rappers, musicians<br />\\nat the moment: thee oh sees.<br />\\nforever: wu-tang<br ...   \n",
       "\n",
       "                                                                                                essay5  \\\n",
       "0                                               food.<br />\\nwater.<br />\\ncell phone.<br />\\nshelter.   \n",
       "1  delicious porkness in all of its glories.<br />\\nmy big ass doughboy's sinking into 15 new inche...   \n",
       "2          movement<br />\\nconversation<br />\\ncreation<br />\\ncontemplation<br />\\ntouch<br />\\nhumor   \n",
       "3                                                                                                  NaN   \n",
       "4                                                                                                  NaN   \n",
       "\n",
       "                        essay6  \\\n",
       "0  duality and humorous things   \n",
       "1                          NaN   \n",
       "2                          NaN   \n",
       "3   cats and german philosophy   \n",
       "4                          NaN   \n",
       "\n",
       "                                                                            essay7  \\\n",
       "0  trying to find someone to hang out with. i am down for anything\\nexcept a club.   \n",
       "1                                                                              NaN   \n",
       "2                      viewing. listening. dancing. talking. drinking. performing.   \n",
       "3                                                                              NaN   \n",
       "4                                                                              NaN   \n",
       "\n",
       "                                                                     essay8  \\\n",
       "0  i am new to california and looking for someone to wisper my secrets\\nto.   \n",
       "1                        i am very open and will share just about anything.   \n",
       "2                when i was five years old, i was known as \"the boogerman\".   \n",
       "3                                                                       NaN   \n",
       "4                                                                       NaN   \n",
       "\n",
       "                                                                                                essay9  \\\n",
       "0  you want to be swept off your feet!<br />\\nyou are tired of the norm.<br />\\nyou want to catch a...   \n",
       "1                                                                                                  NaN   \n",
       "2  you are bright, open, intense, silly, ironic, critical, caring,\\ngenerous, looking for an explor...   \n",
       "3                                                                                you feel so inclined.   \n",
       "4                                                                                                  NaN   \n",
       "\n",
       "             ethnicity  height  income                          job  \\\n",
       "0         asian, white    75.0      -1               transportation   \n",
       "1                white    70.0   80000         hospitality / travel   \n",
       "2                  NaN    68.0      -1                          NaN   \n",
       "3                white    71.0   20000                      student   \n",
       "4  asian, black, other    66.0      -1  artistic / musical / writer   \n",
       "\n",
       "        last_online                         location  \\\n",
       "0  2012-06-28-20-30  south san francisco, california   \n",
       "1  2012-06-29-21-41              oakland, california   \n",
       "2  2012-06-27-09-10        san francisco, california   \n",
       "3  2012-06-28-14-22             berkeley, california   \n",
       "4  2012-06-27-21-26        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 (poorly)     single  \n",
       "2                                   english, french, c++  available  \n",
       "3                               english, german (poorly)     single  \n",
       "4                                                english     single  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import profiles.csv file as a DataFrame and explore columns\n",
    "all_data = pd.read_csv(data_location)\n",
    "print(len(all_data))\n",
    "print(all_data.columns.values)\n",
    "all_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Processing and cleaning data for analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['age', 'body_type', 'diet', 'drinks', 'drugs', 'height', 'sex',\n",
       "       'smokes'], dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop columns that will not be used for project\n",
    "columns_to_drop = [\n",
    "    'education',\n",
    "    'ethnicity',\n",
    "    'job',\n",
    "    'last_online',\n",
    "    'location',\n",
    "    'offspring',\n",
    "    'orientation',\n",
    "    'pets',\n",
    "    'religion',\n",
    "    'sign',\n",
    "    'speaks',\n",
    "    'status',\n",
    "    'income', \n",
    "    'essay0',\n",
    "    'essay1',\n",
    "    'essay2',\n",
    "    'essay3',\n",
    "    'essay4',\n",
    "    'essay5',\n",
    "    'essay6',\n",
    "    'essay7',\n",
    "    'essay8',\n",
    "    'essay9',\n",
    "    'sex'\n",
    "]\n",
    "all_data.drop(columns_to_drop, axis=1, inplace=True)\n",
    "all_data.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age          False\n",
       "body_type     True\n",
       "diet          True\n",
       "drinks        True\n",
       "drugs         True\n",
       "height        True\n",
       "sex          False\n",
       "smokes        True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check which columns have NaN values\n",
    "all_data.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove NaNs in columns\n",
    "other_columns_with_nan_values = [\n",
    "    'body_type', \n",
    "    'diet', \n",
    "    'drinks', \n",
    "    'drugs', \n",
    "    'height', \n",
    "    'smokes'\n",
    "]\n",
    "all_data.dropna(subset=other_columns_with_nan_values, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25202\n"
     ]
    }
   ],
   "source": [
    "# check that no columns have NaN values\n",
    "all_data.isna().any()\n",
    "\n",
    "# return length of data frame to see how many rows have been dropped\n",
    "print(len(all_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# determine value counts for diet responses\n",
    "all_data['diet'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    21272\n",
       "1     3320\n",
       "0      468\n",
       "2      142\n",
       "Name: diet_code, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# map diet responses to a number code\n",
    "\n",
    "def map_diet_to_code(diet):\n",
    "    if \"vegan\" in diet:\n",
    "        return 0\n",
    "    elif \"vegetarian\" in diet:\n",
    "        return 1\n",
    "    elif \"kosher\" in diet or \"halal\" in diet:\n",
    "        return 2\n",
    "    else:\n",
    "        return 3\n",
    "    \n",
    "all_data['diet_code'] = all_data['diet'].apply(map_diet_to_code)\n",
    "\n",
    "# check that mapping diet responses has worked \n",
    "\n",
    "all_data['diet_code'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# determine value counts for drinks responses\n",
    "all_data['drinks'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    17929\n",
       "1     2896\n",
       "3     2162\n",
       "0     1789\n",
       "4      264\n",
       "5      162\n",
       "Name: drinks_code, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# map drinks responses to a number code\n",
    "\n",
    "drinks_codes = {\n",
    "    \"not at all\": 0,\n",
    "    \"rarely\": 1,\n",
    "    \"socially\": 2,\n",
    "    \"often\": 3,\n",
    "    \"very often\": 4,\n",
    "    \"desperately\": 5\n",
    "}\n",
    "\n",
    "all_data[\"drinks_code\"] = all_data['drinks'].map(drinks_codes)\n",
    "\n",
    "#check that mapping drinks responses has worked \n",
    "\n",
    "all_data['drinks_code'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "never        20339\n",
       "sometimes     4600\n",
       "often          263\n",
       "Name: drugs, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#determine value counts for drugs responses\n",
    "all_data['drugs'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    20339\n",
       "1     4600\n",
       "2      263\n",
       "Name: drugs_code, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# map drugs responses to a number code\n",
    "\n",
    "drugs_codes = {\n",
    "    \"never\": 0,\n",
    "    \"sometimes\": 1,\n",
    "    \"often\": 2\n",
    "}\n",
    "\n",
    "all_data[\"drugs_code\"] = all_data['drugs'].map(drugs_codes)\n",
    "\n",
    "#check that mapping drugs responses has worked \n",
    "\n",
    "all_data['drugs_code'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no                20402\n",
       "sometimes          1688\n",
       "when drinking      1429\n",
       "yes                1032\n",
       "trying to quit      651\n",
       "Name: smokes, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# determine value counts for smokes responses\n",
    "all_data['smokes'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    20402\n",
       "2     1688\n",
       "3     1683\n",
       "1     1429\n",
       "Name: smokes_code, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# map smokes responses to a number code\n",
    "\n",
    "smokes_codes = {\n",
    "    \"no\": 0,\n",
    "    \"when drinking\": 1,\n",
    "    \"sometimes\": 2,\n",
    "    \"yes\": 3,\n",
    "    \"trying to quit\": 3\n",
    "}\n",
    "\n",
    "all_data[\"smokes_code\"] = all_data['smokes'].map(smokes_codes)\n",
    "\n",
    "#check that mapping smokes responses has worked \n",
    "\n",
    "all_data['smokes_code'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "average           6802\n",
       "fit               5742\n",
       "athletic          5366\n",
       "thin              2191\n",
       "curvy             1843\n",
       "a little extra    1312\n",
       "skinny             804\n",
       "full figured       464\n",
       "overweight         227\n",
       "jacked             191\n",
       "used up            182\n",
       "rather not say      78\n",
       "Name: body_type, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# determine value counts for body type responses\n",
    "all_data['body_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     6802\n",
       "1     5742\n",
       "2     5366\n",
       "3     2191\n",
       "4     1843\n",
       "5     1312\n",
       "6      804\n",
       "7      464\n",
       "8      227\n",
       "9      191\n",
       "10     182\n",
       "Name: body_type_code, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove rows where response is \"rather not say\"\n",
    "\n",
    "all_data = all_data.loc[all_data['body_type'] != \"rather not say\",:]\n",
    "\n",
    "# map body_type responses to a number code\n",
    "\n",
    "body_type_codes = {\n",
    "    \"average\": 0,\n",
    "    \"fit\": 1,\n",
    "    \"athletic\": 2,\n",
    "    \"thin\": 3,\n",
    "    \"curvy\": 4,\n",
    "    \"a little extra\": 5,\n",
    "    \"skinny\": 6,\n",
    "    \"full figured\": 7,\n",
    "    \"overweight\": 8,\n",
    "    \"jacked\": 9,\n",
    "    \"used up\": 10\n",
    "}\n",
    "\n",
    "all_data[\"body_type_code\"] = all_data['body_type'].map(body_type_codes)\n",
    "\n",
    "#check that mapping smokes responses has worked \n",
    "\n",
    "all_data['body_type_code'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25124\n"
     ]
    }
   ],
   "source": [
    "# return length of data frame to see how many responses will be used for analysis\n",
    "print(len(all_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring the cleaned and processed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average           6802\n",
      "fit               5742\n",
      "athletic          5366\n",
      "thin              2191\n",
      "curvy             1843\n",
      "a little extra    1312\n",
      "skinny             804\n",
      "full figured       464\n",
      "overweight         227\n",
      "jacked             191\n",
      "used up            182\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b269160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#generate bar chart for body type responses\n",
    "\n",
    "body_type_responses = pd.value_counts(all_data['body_type'].values, sort=True)\n",
    "print(body_type_responses)\n",
    "ax = body_type_responses.plot(kind='bar', title='Body type responses', color=light_blue)\n",
    "ax.set(xlabel='Body type', ylabel='Number of responses')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11515b2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#generate histogram chart for age responses\n",
    "\n",
    "plt.hist(all_data['age'], color=light_blue, bins=20)\n",
    "plt.title(\"Distribution of age responses\")\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.xlim(16, 80)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>height</th>\n",
       "      <th>diet_code</th>\n",
       "      <th>drinks_code</th>\n",
       "      <th>drugs_code</th>\n",
       "      <th>smokes_code</th>\n",
       "      <th>body_type_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.046986</td>\n",
       "      <td>-0.039399</td>\n",
       "      <td>-0.112828</td>\n",
       "      <td>-0.167408</td>\n",
       "      <td>-0.153620</td>\n",
       "      <td>-0.048182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>height</th>\n",
       "      <td>-0.046986</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.065915</td>\n",
       "      <td>0.035809</td>\n",
       "      <td>0.073346</td>\n",
       "      <td>0.057508</td>\n",
       "      <td>-0.041274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diet_code</th>\n",
       "      <td>-0.039399</td>\n",
       "      <td>0.065915</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.069316</td>\n",
       "      <td>-0.060261</td>\n",
       "      <td>0.017134</td>\n",
       "      <td>-0.014467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drinks_code</th>\n",
       "      <td>-0.112828</td>\n",
       "      <td>0.035809</td>\n",
       "      <td>0.069316</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.218737</td>\n",
       "      <td>0.142761</td>\n",
       "      <td>0.020841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drugs_code</th>\n",
       "      <td>-0.167408</td>\n",
       "      <td>0.073346</td>\n",
       "      <td>-0.060261</td>\n",
       "      <td>0.218737</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.348970</td>\n",
       "      <td>0.084404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smokes_code</th>\n",
       "      <td>-0.153620</td>\n",
       "      <td>0.057508</td>\n",
       "      <td>0.017134</td>\n",
       "      <td>0.142761</td>\n",
       "      <td>0.348970</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.080216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>body_type_code</th>\n",
       "      <td>-0.048182</td>\n",
       "      <td>-0.041274</td>\n",
       "      <td>-0.014467</td>\n",
       "      <td>0.020841</td>\n",
       "      <td>0.084404</td>\n",
       "      <td>0.080216</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     age    height  diet_code  drinks_code  drugs_code  \\\n",
       "age             1.000000 -0.046986  -0.039399    -0.112828   -0.167408   \n",
       "height         -0.046986  1.000000   0.065915     0.035809    0.073346   \n",
       "diet_code      -0.039399  0.065915   1.000000     0.069316   -0.060261   \n",
       "drinks_code    -0.112828  0.035809   0.069316     1.000000    0.218737   \n",
       "drugs_code     -0.167408  0.073346  -0.060261     0.218737    1.000000   \n",
       "smokes_code    -0.153620  0.057508   0.017134     0.142761    0.348970   \n",
       "body_type_code -0.048182 -0.041274  -0.014467     0.020841    0.084404   \n",
       "\n",
       "                smokes_code  body_type_code  \n",
       "age               -0.153620       -0.048182  \n",
       "height             0.057508       -0.041274  \n",
       "diet_code          0.017134       -0.014467  \n",
       "drinks_code        0.142761        0.020841  \n",
       "drugs_code         0.348970        0.084404  \n",
       "smokes_code        1.000000        0.080216  \n",
       "body_type_code     0.080216        1.000000  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1: Can we use diet, lifestyle information (alcohol consumption, drug use, tobacco use), and body type to predict age? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare data for analysis - scale features and generate training and test data sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select features and scale data for regression\n",
    "\n",
    "from sklearn.preprocessing import scale\n",
    "\n",
    "list_of_features_for_analysis = [\n",
    "    'diet_code',\n",
    "    'drinks_code',\n",
    "    'drugs_code',\n",
    "    'smokes_code',\n",
    "    'body_type_code',\n",
    "]\n",
    "features = all_data[list_of_features_for_analysis]\n",
    "scaled_features = scale(features, axis=0)\n",
    "age = all_data['age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate training and test sets\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(scaled_features, age, test_size=0.2, random_state=25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use Multiple Linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLR training set score: 0.0463680266632\n",
      "MLR test set score: 0.0432589272067\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11695db70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# build Multiple Linear Regression model to predict age\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "mlr_model = LinearRegression()\n",
    "\n",
    "mlr_model.fit(X_train, y_train)\n",
    "\n",
    "mlr_training_score = mlr_model.score(X_train, y_train)\n",
    "\n",
    "print(\"MLR training set score: %s\" %(mlr_training_score))\n",
    "\n",
    "mlr_test_score = mlr_model.score(X_test, y_test)\n",
    "\n",
    "print(\"MLR test set score: %s\" %(mlr_test_score))\n",
    "\n",
    "#generate plot showing difference between predicted age and actual age using Multiple Linear Regression model\n",
    "\n",
    "mlr_predictions = mlr_model.predict(X_test)\n",
    "\n",
    "plt.scatter(y_test, mlr_predictions, c=light_blue, alpha=0.2)\n",
    "plt.title(\"Actual age versus predicted age using Multiple Linear Regression\")\n",
    "plt.xlabel(\"Actual age\")\n",
    "plt.ylabel(\"Predicted age\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use K Neighbors regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNR training set score: 0.09228919445\n",
      "KNR test set score: 0.026069192568\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1053b0fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#build K Neighbors Regressor to predict age\n",
    "\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "kn_regressor = KNeighborsRegressor(n_neighbors = 50, weights = \"distance\")\n",
    "kn_regressor.fit(X_train, y_train)\n",
    "\n",
    "knr_training_score = kn_regressor.score(X_train, y_train)\n",
    "\n",
    "print(\"KNR training set score: %s\" %(knr_training_score))\n",
    "\n",
    "knr_test_score = kn_regressor.score(X_test, y_test)\n",
    "\n",
    "print(\"KNR test set score: %s\" %(knr_test_score))\n",
    "\n",
    "#generate plot showing difference between predicted age and actual age using K Neighbors Regressor\n",
    "\n",
    "knr_predictions = kn_regressor.predict(X_test)\n",
    "\n",
    "plt.scatter(y_test, knr_predictions, c=light_blue, alpha=0.2)\n",
    "plt.title(\"Actual age versus predicted age using K Neighbors Regression\")\n",
    "plt.xlabel(\"Actual age\")\n",
    "plt.ylabel(\"Predicted age\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 2: Can we use diet, lifestyle information (alcohol consumption, drug use, tobacco use), and age to predict body type?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare data for analysis - scale features and generate training and test data sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select features and scale data for regression\n",
    "\n",
    "from sklearn.preprocessing import scale\n",
    "\n",
    "list_of_features_classification = [\n",
    "    'diet_code',\n",
    "    'drinks_code',\n",
    "    'drugs_code',\n",
    "    'smokes_code',\n",
    "    'age'\n",
    "]\n",
    "features_classification = all_data[list_of_features_classification]\n",
    "scaled_features_classification = scale(features_classification, axis=0)\n",
    "labels = all_data['body_type_code']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate training and test sets\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_data, test_data, train_labels, test_labels = train_test_split(scaled_features_classification, labels, test_size=0.2, random_state=40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use K Nearest Neighbours classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN training set score: 0.287924772377\n",
      "KNN test set score: 0.277810945274\n"
     ]
    }
   ],
   "source": [
    "# build K Nearest Neighbors model to predict body type\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn_classifier = KNeighborsClassifier(n_neighbors=76)\n",
    "knn_classifier.fit(train_data, train_labels)\n",
    "\n",
    "knn_training_score = knn_classifier.score(train_data, train_labels)\n",
    "\n",
    "print(\"KNN training set score: %s\" %(knn_training_score))\n",
    "\n",
    "knn_test_score = knn_classifier.score(test_data, test_labels)\n",
    "\n",
    "print(\"KNN test set score: %s\" %(knn_test_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[773 357 213   0   0   0   0   0   0   0   0]\n",
      " [525 402 212   0   0   0   0   0   0   0   0]\n",
      " [508 344 221   0   0   0   0   0   0   0   0]\n",
      " [249 136  82   0   0   0   0   0   0   0   0]\n",
      " [190 121  61   0   0   0   0   0   0   0   0]\n",
      " [160  64  37   0   0   0   0   0   0   0   0]\n",
      " [112  42  19   0   0   0   0   0   0   0   0]\n",
      " [ 36  28  16   0   0   0   0   0   0   0   0]\n",
      " [ 22  13   7   0   0   0   0   0   0   0   0]\n",
      " [ 22  12   5   0   0   0   0   0   0   0   0]\n",
      " [ 27   5   4   0   0   0   0   0   0   0   0]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.29      0.58      0.39      1343\n",
      "          1       0.26      0.35      0.30      1139\n",
      "          2       0.25      0.21      0.23      1073\n",
      "          3       0.00      0.00      0.00       467\n",
      "          4       0.00      0.00      0.00       372\n",
      "          5       0.00      0.00      0.00       261\n",
      "          6       0.00      0.00      0.00       173\n",
      "          7       0.00      0.00      0.00        80\n",
      "          8       0.00      0.00      0.00        42\n",
      "          9       0.00      0.00      0.00        39\n",
      "         10       0.00      0.00      0.00        36\n",
      "\n",
      "avg / total       0.19      0.28      0.22      5025\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Andrew/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "# generate classification report and confusion matrix for K Nearest Neighbors classifier\n",
    "\n",
    "from sklearn.metrics import classification_report, confusion_matrix \n",
    "\n",
    "knn_predictions = knn_classifier.predict(test_data)\n",
    "print(confusion_matrix(test_labels, knn_predictions))\n",
    "print(classification_report(test_labels, knn_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106cc5fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'k': 76, 'score': 0.27781094527363182}\n"
     ]
    }
   ],
   "source": [
    "# generate plot showing K Nearest Neighbors classifier score based on different n_neighbors values \n",
    "# and print out n_neighbors value that results in highest score\n",
    "\n",
    "scores = []\n",
    "\n",
    "highest_score = {\n",
    "    'k': 0,\n",
    "    'score': 0\n",
    "}\n",
    "\n",
    "for k in range(1, 101):\n",
    "    classifier = KNeighborsClassifier(n_neighbors=k)\n",
    "    classifier.fit(train_data, train_labels)\n",
    "    score = classifier.score(test_data, test_labels)\n",
    "    scores.append(score)\n",
    "    if score > highest_score['score']:\n",
    "        highest_score = {\n",
    "            'k': k,\n",
    "            'score': score\n",
    "        }\n",
    "\n",
    "x_values = range(1, 101)\n",
    "\n",
    "y_values = scores\n",
    "\n",
    "plt.plot(x_values, y_values)\n",
    "plt.title('Accuracy of K Nearest Neighbours classifier based on different k values')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('k value')\n",
    "plt.show()\n",
    "print(highest_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use Support Vector Machines classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC training set score: 0.321359271606\n",
      "SVC test set score: 0.271641791045\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svc_classifier = SVC(kernel='rbf', gamma=10)\n",
    "svc_classifier.fit(train_data, train_labels)\n",
    "\n",
    "svc_training_score = svc_classifier.score(train_data, train_labels)\n",
    "\n",
    "print(\"SVC training set score: %s\" %(svc_training_score))\n",
    "\n",
    "svc_test_score = classifier.score(test_data, test_labels)\n",
    "\n",
    "print(\"SVC test set score: %s\" %(svc_test_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[753 320 260   4   1   1   1   1   0   0   2]\n",
      " [554 305 273   3   1   2   0   0   0   0   1]\n",
      " [526 277 265   3   0   0   0   0   0   0   2]\n",
      " [245 127  89   3   0   0   1   0   0   0   2]\n",
      " [212  84  74   1   0   0   1   0   0   0   0]\n",
      " [156  54  46   3   0   2   0   0   0   0   0]\n",
      " [114  22  32   2   2   0   1   0   0   0   0]\n",
      " [ 44  20  16   0   0   0   0   0   0   0   0]\n",
      " [ 23  13   5   0   0   0   0   0   1   0   0]\n",
      " [ 24   7   7   0   0   0   0   0   0   0   1]\n",
      " [ 26   6   3   0   0   0   0   0   0   0   1]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.28      0.56      0.37      1343\n",
      "          1       0.25      0.27      0.26      1139\n",
      "          2       0.25      0.25      0.25      1073\n",
      "          3       0.16      0.01      0.01       467\n",
      "          4       0.00      0.00      0.00       372\n",
      "          5       0.40      0.01      0.02       261\n",
      "          6       0.25      0.01      0.01       173\n",
      "          7       0.00      0.00      0.00        80\n",
      "          8       1.00      0.02      0.05        42\n",
      "          9       0.00      0.00      0.00        39\n",
      "         10       0.11      0.03      0.04        36\n",
      "\n",
      "avg / total       0.24      0.26      0.21      5025\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Andrew/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "# generate classification report and confusion matrix for Support Vector Machines classifier\n",
    "\n",
    "from sklearn.metrics import classification_report, confusion_matrix \n",
    "\n",
    "svc_predictions = svc_classifier.predict(test_data)\n",
    "print(confusion_matrix(test_labels, svc_predictions))\n",
    "print(classification_report(test_labels, svc_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZCy9vJIpIHxEZ6x2X9nmxV7pvVpXgL8Vdu8n1zoo3qupG4CHgR96O9wvcDW6ZuDtT/4K7qS0XV01xi9d9Ae4GDXA3CBXhNo4puJOByryPSxorcdVX+zi4mul+3E7yAe6g9B/cNdlyU4BBVFw9j5eIT/Pi3Yar5jvNd2miKs2B13EHyzW4M6wzvGlvx9349QTuDH83rrrR703cNcLyG/3O9lWJg0vkH3jTXoO7voyqTsddD3wNd+DvibvRrzJ3AVPEVQGdV8lwU7zl8JfeUdV1uNLwbbiNeh3uRsfy7eliXGJYjrtmdLM33nJcQl/jzbuTtxxZuLuNF+Pulv5DFfGHpc50Vc0P0/sZ3LazHnejzdyQ/mG340OJoxL/wN0A9IGI7PRiGOX164A7odqBqyL9lDAnOl4iOh13PS4Xtx2dH2ZeVe0zE4AlIrLLi+sC71JIteKohktwB77yJz9exV3CqYmw+4SqLsVdS52DO4YMwt14Wm4EMM9btrdw10LXqupO3IH3AlwJfCPf3cAJrkagpdf9adyBPCxVXYG75PcgrgbodOB0VS2q4TKCO6i/j7t58ivcicYh8W0fQ3E3JW7FHXeSKhmtsu2yKlO9/9tE5Ksw8RThagCvwB0bL8KdaO33BnkAd6ze6s33vWrOtzYcyrxfwNUCHqie97arG3E5qABXW/hWJdO4CfcblV8iOlCr7BWkrsLl2ALcDYaXeb2b4u7z2orbRtvjjsEVkoMvjUQnrwT5HO7Gs7Kg4wklInfhbrq4qIL+2bibbD6qz7iMMaa2icg83A2HFZ5AmdoR9U3VetVnNwFPRGJyN8aYaCYio0Wkg1dFfykwmPotqTdaUd2KkYj0w1X/LsQ9+2qMMaZ+9cFVX7fEPakyybtPw9SxRlFFb4wxxjQ2UV9Fb4wxxjRGUV1FH5R27dppt27dgg7DGGMalC+//HKrqoa2EWIOkSX4OtCtWzeysrKqHtAYY8wBInI4rfiZEFZFb4wxxkQhS/DGGGNMFLIEb4wxxkQhS/DGGGNMFLIEb4wxxkShqErwIjJBRFaIyCoR+d4bmUTk5yKyVEQWicj08rcnef3uFffu6GUi8k/vTT7NReR/IrLc6/fn+l0iY4wx5tBETYL3Xm/5MHAy7jWvk71XPvrNBzJUdTDu7Vb3euMeAxyLayN5IO5tVOWv8PurqvYFhgHHeq8CNMYYYyJa1CR43DuGV6nqGu8VhS/hXml6gKrOUNU93te5QPk7oxVI4Lv3+sYDm1R1j6rO8MYtwr3KMQ1jKlGwu4inZq1lyYbtWFPQxpigRFNDN505+H3XeVT+TuMrcO/LRlXniMgM3PvUBXhIVZf5BxaRZNw7fP8RbmIicjVwNUDXrl0PcRFMNHh2bg73f7gSgE5JCYzvn8pJ/VMZ1b0tTeKi6ZzaGBPJoinBS5huYYtPInIRkIFXDS8ivYB+fFc6/1BETlDVmV7/OOBF4J+quibcNFX1MeAxgIyMDCu2NWKZ2fn0TGnBNaN78uHSTbyStY5n5uTQsmkco/ukcFK/VMb0aU9S8/igQzXGRLFoSvB5QBff9zRgQ+hAIjIeuB0Yrar7vc5nAXNVdZc3zDTgKGCm1/8x4BtVfaCOYjdRoqS0jK9yCjjryM6cl9GF8zK6sK+4lFmrtvLRsk18uHQz/1v0LbExwshubVzpvl8qXds2Dzp0Y0yUiaYEnwn0FpHuwHrgAuBC/wAiMgx4FJigqpt9vXKBq0TkT7iagNHAA944fwCSgCvrfAlMg7d84052F5UyolubA90S4mMZ1y+Vcf1S+eOZysK8Qj5atomPlm7m9+8s5ffvLOWI1Jac1D+V8f1SGZKWTExMuAopY4ypvqhJ8KpaIiLXA+8DscCTqrpERO4GslT1LeA+oCUwVUQAclX1DNwd9WOBxbhq/fdU9W0RScOV9pcDX3njPKSqT9Tz4pkG4sucAgAyfAneLyZGGNa1NcO6tub/ftiXnG27+WjZZj5auolHPl3DwzNW065lU8b3a8/4fqkc17sdCfGx9bkIxpgoIXaXb+3LyMhQe5tc43T9C1/xVU4Bs389rsbjbt9TzCcrN/PB0k18umILu/aXkBAfw/G93XX7sf3a065l0zqI2pjIICJfqmpG0HFEi6gpwRsTNFUlMzufUd3bHtL4Sc3jmTi0MxOHdqaopIx5a7fx0dJNfOj9icCRXVszvl8qJ/VvT8+Ulni1SsYY8z2W4I2pJXkFe9m0Yz8jurU+7Gk1iXMl9+N7p3DXGQNY+u0OPlq6mY+WbeIv7y3nL+8tp1vb5l6yT2V4emviYu0RPGPMdyzBG1NLsnLyARieHv76+6ESEQZ0SmJApyRuGt+bb7fvPXDd/pk5OTzx+VqSm8cztk97xvdP5YQjUmjZ1HZtYxo7OwoYU0syswtIbBpHnw6JdTqfjknNuPiodC4+Kp1d+0v4bOUWPly6iY9XbOa/89fTJDaGo3u2ZXz/VMb3a0/HpGZ1Go8xJjJZgjemlnyZXcCR6a2JrcdH3Fo2jePkQR05eVBHSkrL+DKnwHvefhN3vPE1d7wBgzonMb5fKuP7t6d/x1Z23d6YRsISvDG1YPueYlZs2snpQzoGFkNcbAyjerRlVI+23HZKP1Zv2cWH3nX7B6av5O8fraRHuxY8f9UoK9Ub0whYgjemFnyZ666/V/T8e30TEXq1T6RX+0SuPbEnW3ftZ/qyTfzu7aXc8spCnrtilDWmY0yUs9tujakFmdkFxMcKQ9KSgw4lrHYtm3L+iK789vT+zF69jSdnrQ06JGNMHbMEb0wtyMrOZ0CnJJo1iexW587L6MIP+qdy73srWL5xR9DhGGPqkCV4Yw7T/pJSFuZtr5Xn3+uaiPCnswfRqlk8N7+0gH3FpUGHZIypI5bgjTlMX6/fTlFJWcRcf69K25ZNuW/SYJZv3MnfPlgRdDjGmDpiCd6Yw5SZ7b1gJj3yS/DlxvRtz8VHpfPE52uZvWpr0OEYY+qAJXhjDlNWdj49UlrQtoG9COa2U/rRvV0Lbpm6kO17ioMOxxhTyyzBG3MYysqUrJwCRtRy87T1oVmTWB44fyhbdu7njje/DjocY0wtswRvzGFYvWUXhXuKGd4AbrALZ3BaMjeP781bCzfw5oL1QYdjjKlFluCNOQzl199HNJAb7MK59sReZKS35jdvfM36wr1Bh2OMqSWW4I05DFk5+bRr2YRubZsHHcohi40R/n7+UMrKlFteWUBZmQYdkjGmFliCN+YwZGUXkJHepsG/wKVLm+b89owBzF2TzxOfrwk6HGNMLbAEb8wh2rRjH7n5e8hooNffQ507PI0JAzpw3/srWLrBWrkzpqGzBG/MIcqKguvvfiLCPWcPIrl5E25+eb61cmdMA2cJ3phDlJmdT7P4WPp3ahV0KLWmTYsm/PXcIazctIt737NW7oxpyCzBG3OIsnLyGdolmfjY6NqNRh+RwqVHp/PkrLV8/o21cmdMQxVdRyZj6smu/SUs3bCjQbxg5lDcenI/erVvyS1TF1C4pyjocIwxh8ASvDGHYEFuIWVKg3nBTE2Vt3K3bVcRt7/+Nar26JwxDY0leGMOQWZ2PjECw7omBx1KnRnYOYmf/+AI/rf4W16fb63cGdPQWII35hBk5eTTr2MrEhPigw6lTl1zQk9GdmvDb99cwrr8PUGHY4ypAUvwxtRQcWkZ83MLG9TrYQ9VbIzwt/OGoMAtryyk1Fq5M6bBsARvTA0t+3YHe4pKo/b6e6gubZrzuzMG8EV2Po/NtFbujGkoLMEbU0PlDdxESwt21XH2kZ05dVBH7v9wBV+v3x50OMaYarAEb0wNZeXkk9a6GR2TmgUdSr0REf541kDatGjCzS8vsFbujGkAoirBi8gEEVkhIqtE5NYw/X8uIktFZJGITBeRdF+/e0VkiYgsE5F/ivf2EBEZLiKLvWke6G4aJ1UlM7sgapqnrYnk5q6Vu1Wbd/HnacuDDscYU4WoSfAiEgs8DJwM9Acmi0j/kMHmAxmqOhh4FbjXG/cY4FhgMDAQGAGM9sb5N3A10Nv7m1C3S2IiWW7+Hrbs3N+oquf9ju+dwo+P7cbTs7P5dOWWoMMxxlQiahI8MBJYpaprVLUIeAmY6B9AVWeoavmzPnOBtPJeQALQBGgKxAObRKQj0EpV56hr6eMZ4My6XxQTqTLLr7+nN74SfLlfTejLEakt+b+pCynYba3cGROpoinBdwbW+b7ned0qcgUwDUBV5wAzgG+9v/dVdZk3fl51pikiV4tIlohkbdliJZtolZWdT6uEOHq3bxl0KIFJiI/lgfOHUbCniF//d7G1cmdMhIqmBB/u2njYI4+IXARkAPd533sB/XAl+s7AWBE5oSbTVNXHVDVDVTNSUlIOIXzTEGTlFJDRrQ0xMY37Voz+nVrxix/04b0lG3n1y7yqRzDG1LtoSvB5QBff9zRgQ+hAIjIeuB04Q1X3e53PAuaq6i5V3YUr2R/lTTPNN3rYaZrGIX93Eas272q0199DXXl8D0Z1b8Pv3l5qrdwZE4GiKcFnAr1FpLuINAEuAN7yDyAiw4BHccl9s69XLjBaROJEJB53g90yVf0W2CkiR3l3z18CvFkfC2Miz5c57vp7Y7yDPpzYGOH+84ciAj97eYG1cmdMhImaBK+qJcD1wPvAMuAVVV0iIneLyBneYPcBLYGpIrJARMpPAF4FVgOLgYXAQlV92+t3LfAEsMobZlq9LJCJOFnZ+TSJjWFQ56SgQ4kYnZOb8fuJA8nKKeCRT1cHHY4xxicu6ABqk6q+C7wb0u1O3+fxFYxXClxTQb8s3KNzppHLzM5nUFoSCfGxQYcSUSYO7cT05Zv5+4crOaF3CoPS7ATImEgQNSV4Y+rSvuJSFq/fbtffwxAR/jBxICmJTbnp5fnsLbJW7oyJBJbgjamGRXnbKS5VRjTi598rk9Q8nr+dO4Q1W3bzp2nLgg7HGIMleGOqJTM7H4DhjeAVsYfqmF7tuPK47jwzJ4cZKzZXPYIxpk5ZgjemGrKy8+ndviWtWzQJOpSI9osf9qFvh0R++eoitu3aX/UIxpg6YwnemCqUlemBBm5M5RLiY3nggqFs31NsrdwZEzBL8MZUYeXmnezcV0KGVc9XS98OrfjlhD58sHQTU7OslTtjgmIJ3pgqlL9gxhq4qb7Lj+3OMT3bctfbS8jZtjvocIxplCzBG1OFrOx82ic2pUubZkGH0mDExAh/PXcIcTHCz15eQElpWdAhGdPoWII3pgpZ2QWM6NYG11qxqa5Oyc34w1mD+Cq3kH99Yq3cGVPfLMEbU4kNhXtZX7jXGrg5RGcM6cSZQzvxj+nfsGBdYdDhGNOoWII3phJZ3gtmMqyBm0P2u4kDSU1sys9eXsCeopKgwzGm0bAEb0wlsrLzad4kln4dE4MOpcFKahbP384bSva23fzxf9bKnTH1xRK8MZXIzC7gyK6tiYu1XeVwHN2zLVcf34Pn5+UyfdmmoMMxplGwo5YxFdixr5jlG3fY9fda8vMfHEG/jq341WuL2Gqt3BlT5yzBG1OB+bmFqNrz77WlaVwsD5w/lB37Srj1tUXWyp0xdcwSvDEVyMrOJzZGGNolOehQokafDon8akJfPlq2mZcy1wUdjjFRzRK8MRXIzM6nf8dWtGgaF3QoUeXHx3TjuF7tuPvtpazdaq3cGVNXLMEbE0ZRSRkL1hXa9fc6UN7KXZO4GG5+eQHF1sqdMXXCErwxYSzZsJ19xWV2/b2OdEhK4J6zBrFwXSEPfbwq6HCMiUqW4I0JIyu7vIEbK8HXlVMHd+TsYZ15aMYqvsotCDocY6KOJXhjwsjKySe9bXPat0oIOpSodtfEAXRolcBNL83n2TnZLFhXyL7i0qDDMiYq2N1DxoRQVbKyCzixT/ugQ4l6rRLi+efkoVz73Ffc8eYSAOJjhT4dEhnUOZnBaUkM6pxEnw6JxFtjQ8bUiCV4Y0Ks3bqbbbuL7Aa7ejI8vQ3zbhvHhu37WJxXyKK87Sxev513F3/Li1/kAtAkLoZ+HVsxxEv4g9OS6ZnSwloYNKYSluCNCVF+/X2EJfh6IyJ0Tm5G5+RmTBjYEXA1Kbn5ew4k/EV5hfz3q/U8MyetfqRJAAAgAElEQVQHgGbxsQzo1IpBaUkMTnNJv3vbFsTE2Gt9jQFL8MZ8T2Z2Pq2bx9MzpWXQoTRqIkJ62xakt23B6UM6AVBWpqzdtptF5SX9vO28+EUuT81yj9q1bBrHwM6tGJyW7JX0k+japjkilvRN42MJ3pgQWTkFDE9vY0khAsXECD1TWtIzpSVnDUsDoKS0jNVbXNJfvH47C/O28/TsbIpKXNJPahZ/4Fr+4LQkBqUl0ykpwX5fE/UswRvjs3XXftZu3c0FI7oEHYqpprjYGPp0SKRPh0TOzXC/W1FJGSs37fSq9rezeH0hj81cQ0mZa/++bYsmXtV+MoO9xG9PTJhoYwneGJ8Dz79bAzcNWpO4GAZ2TmJg5yQmj3Td9hWXsnzjzoNu5Ju58hu8nE9qq6YH7twvL/G3bdk0uIUw5jBZgjfGJys730sOrYIOxdSyhPhYhnZJPujlQXuLSln67fYD1/MX5hUyffkmyl9016VNM84c2pnJI7vSKblZQJEbc2gswRvjk5lTwNC0ZJrGxQYdiqkHzZrEMjy9DcPTv6ux2bmvmCUbdrA4bzuzVm/loRmreHjGKsb2bc+PjkrnhN4pxNqd+qYBiKqHSEVkgoisEJFVInJrmP4/F5GlIrJIRKaLSLrXfYyILPD97RORM71+40TkK6/75yLSq76Xy9SPPUUlLFm/3Z5/b+QSE+I5qkdbrjqhB0//eCSf/XIM157YkwXrtvPjpzIZfd8MHp6xii079wcdqjGVEi2vi2rgRCQWWAmcBOQBmcBkVV3qG2YMME9V94jItcCJqnp+yHTaAKuANG+4lcBEVV0mIj8FRqrqZZXFkpGRoVlZWbW5eKYezF69lQsfn8dTl41gTF9rxc4crKikjA+XbuL5eTnMXr2N+FjhhwM68KNR6RzVw566qA0i8qWqZgQdR7SIpir6kcAqVV0DICIvAROBAwleVWf4hp8LXBRmOpOAaaq6p3w0oPyCbBKwoZbjNhHiy+wCRODIrlaCN9/XJC6GUwd35NTBHVm9ZRcvzMvl1S/zeGfRt/RMacGPRqVzzpFpJDWPDzpUY4DoqqLvDKzzfc/zulXkCmBamO4XAC/6vl8JvCsiecDFwJ/DTUxErhaRLBHJ2rJlS40CN5EhM6eAPqmJdoA2VeqZ0pI7TuvPvNvG8ddzh9CqWTx3v7OUkfd8xC+mLmTBukKipXbUNFwRl+BF5HoROZQiVLj6sbB7mIhcBGQA94V07wgMAt73df4ZcIqqpgFPAfeHm6aqPqaqGaqakZKScgjhmyCVlilf5RQw3F4Pa2ogIT6WScPTeP2nx/K/G4/jnOFpvLv4W858eBanPfg5L8zLZff+kqDDNI1UxCV4oAOQKSKveDfNVffCVh7gb50kjTDV6SIyHrgdOENVQ++SOQ94XVWLvWFTgCGqOs/r/zJwTPUXxTQUyzfuYNf+EkbY8+/mEA3olMQ9Zw1i3m3j+MOZAyktU257fTGj7pnOHW98zfKNO4IO0TQyEZfgVfU3QG/gP8BlwDcico+I9Kxi1Eygt4h0F5EmuKr2t/wDiMgw4FFcct8cZhqTObh6vgBIEpEjvO8nActquEimAfiugRsrwZvDk5gQz0VHpTPtpuN57dpj+EH/VF7OWseEBz5j0r9n8/r8PHvnvakXEXmTnaqqiGwENgIlQGvgVRH5UFV/WcE4JSJyPa56PRZ4UlWXiMjdQJaqvoWrkm8JTPUqBnJV9QwAEemGqwH4NGSaVwGviUgZLuFfXhfLbIKVmZ1Px6QEOltjJqaWiAjD01szPL01d5zWn9e+yuP5ebn87OWF3P32Us7N6MLkkV3p3q5F0KGaKBVxj8mJyI3ApcBW4AngDVUtFpEY4BtVraokHzh7TK5hUVWO/tPHjOjehgcnDws6HBPFVJXZq7fx/LwcPliyiZIy5bhe7fjRqK6M759KfCN/v709Jle7IrEE3w44W1Vz/B1VtUxETgsoJhPF1hfuZeOOffb+d1PnRIRje7Xj2F7t2LxjHy9nruPFL3K59vmvaJ/YlAtGdOECaxbX1JJITPDvAvnlX0QkEeivqvNU1a5/m1pXfv3d7qA39al9qwRuGNebn47pxScrNvPc3BwenLGKh2asYmzfVH50VFdG904hxprFNYcoEhP8v4Ejfd93h+lmTK3JzM6nZdM4+nawF8yY+hcbI4zrl8q4fqmsy9/DS5m5vJy5jo+WbSKtdTMuHNWV8zK60M7ebGdqKBIv+Ij6bgxQ1TIi80TERIms7AKOTG9tLxAxgevSpjn/98O+zL51HA9dOIwurZtz73srOPpP07nhxfnMXbPNGtAx1RaJiXONd6Pdv73vPwXWBBiPiWLb9xSzYtNOThvcMehQjDmgSVwMpw3uxGmDO7Fqc3mzuOt4e+EGeqa04KKj0rlwVFd766GpVCSW4H+Ca0xmPa7xmlHA1YFGZKLWV7nlz79bAzcmMvVq35I7T+/PvNvGc9+kwSQmxPO7t5dy1sOzWbV5Z9DhmQgWcQleVTer6gWq2l5VU1X1wgoapTHmsGVm5xMXIwztkhx0KMZUqlmTWM7N6MIb1x3Lfy7NYOOOfZz6z895dm6OVdubsCKuil5EEnAvghkAJJR3V1VrYMbUuqzsAgZ0TqJZE6vqNA3HuH6pvHfz8fxi6iLueONrPl2xmb+cM5i2diOe8Ym4EjzwLK49+h/iWpVLA6weytS6/SWlLMgrZIQ9HmcaoPaJCTx92QjuPK0/M1duZcI/PmPmSnuTpflOJCb4Xqp6B7BbVacAp+Le8GZMrfp6/XaKSsrs+rtpsGJihMuP686b1x9L6+bxXPLkF9z99lJr694AkZngi73/hSIyEEgCugUXjolWmfaCGRMl+nVsxVvXH8elR6fz5Ky1nPnwLFZusorPxi4SE/xj3vvgf4N7G9xS4C/BhmSiUVZ2AT3atbAGRExUSIiP5XcTB/LUZSPYums/pz/4Oc/MybYb8BqxiErw3gtldqhqgarOVNUe3t30jwYdm4kuZWXKlzn5Vno3UWdM3/ZMu+kEju7ZljvfXMIVU7LYumt/0GGZAERUgvdarbs+6DhM9FuzdRcFe4rJSLfr7yb6pCQ25anLRnDX6f35fNVWJjzwGZ+ssKeNG5uISvCeD0XkFyLSRUTalP8FHZSJLnb93UQ7EeGyY7vz9vXH0bZFEy57KpO73lpiN+A1IhH3HDxQ/rz7db5uCvQIIBYTpTKz82nbognd27UIOhRj6lSfDom8ef2x/Hnacp6enc2c1dv45+Rh9OmQGHRopo5FXAleVbuH+bPkbmpVVnYBGd1aI2IvmDHRLyE+lrvOGMBTPx7Btt1FnP7Q5zw9a63dgBflIq4ELyKXhOuuqs/UdywmOm3esY/c/D1ccnR60KEYU6/G9GnPezcfzy9fXcRdby/lk5VbuG/SEFIS7UmSaBRxJXhghO/veOAu4IwgAzLRJSvHXjBjGq92LZvyn0szuHviAOas3saEB2by8fJNQYdl6kDEleBV9Qb/dxFJwjVfa0ytyMzOJyE+hgGdWgUdijGBEBEuObobR/Voy40vzufyp7O49Oh0fn1KPxLi7b0M0SISS/Ch9gC9gw7CRI+s7AKGdkkmPrYhbP7G1J0jUhN547pjueK47kyZk8MZD33Osm93BB2WqSURd4QTkbdF5C3v7x1gBfBm0HGZ6LBrfwlLNmxnhFXPGwO4G/DuOK0/Uy4fScGeYiY+PIsnP19LWZndgNfQRVwVPfBX3+cSIEdV84IKxkSXBbmFlKldfzcm1OgjUnjvpuP51WuLuPsddwPeX88dTPvEhKpHNhEp4krwQC4wT1U/VdVZwDYR6RZsSCZaZOXkEyNwZNfkoEMxJuK0bdmUxy/J4PdnDmTemm1MeOAzpi+zG/AaqkhM8FOBMt/3Uq+bMYctK7uAvh1akZgQH3QoxkQkEeHio9L5343HkdoqgSumZHHHG1+zt8hawGtoIjHBx6lqUfkX73OTAOMxUaKktIyvcguseVpjqqFX+0TeuO4Yrjq+O8/OzeH0hz5n6Qa7Aa8hicQEv0VEDjz3LiITga0BxmOixLJvd7KnqNSuvxtTTU3jYrn91P48e8VIduwt5syHZ/HEZ2vsBrwGIhIT/E+A20QkV0RygV8B1wQck4kCmdn5AIywErwxNXJ87xTeu/kERvdJ4Q//W8alT33B5h37gg7LVCHiEryqrlbVo4D+wABVPUZVVwUdl2n4snLy6ZzcjI5JzYIOxZgGp02LJjx28XDuOWsQmdn5/PCBmXy41G7Ai2QRl+BF5B4RSVbVXaq6U0Rai8gfgo7LNGyqSlZ2gZXejTkMIsKFo7ryzg3H0ym5GVc9k8Xtry+2G/AiVMQleOBkVS0s/6KqBcAp1RlRRCaIyAoRWSUit4bp/3MRWSoii0Rkuoike93HiMgC398+ETnT6yci8kcRWSkiy0TkxlpaTlOP1uXvZfPO/Qy36+/GHLZe7Vvy358ewzUn9OD5ebmc9uBnfL1+e9BhmRCRmOBjReTAq41EpBlQ5auORCQWeBg4GVe9P1lE+ocMNh/IUNXBwKvAvQCqOkNVh6rqUGAsrnncD7xxLgO6AH1VtR/w0mEsmwmIXX83pnY1jYvl16f04/krR7Frfwln/WsWj81cbTfgRZBITPDPAdNF5AoRuQL4EJhSjfFGAqtUdY33aN1LwET/AF4i3+N9nQukhZnOJGCab7hrgbtVtcybxuYaL5EJXFZOPokJcRzRPjHoUIyJKsf2asd7N53A2L7tuefd5bz6pTU8GikirqlaVb1XRBYB4wEB3gOq8+LuzsA63/c8YFQlw18BTAvT/QLgft/3nsD5InIWsAW4UVW/CR1JRK4Grgbo2rVrNcI19Skzu4CM9NbExEjQoRgTdVq3aMIjFw3nva83clL/1KDDMZ5ILMEDbMS1ZncOMA5YVo1xwh25w9YVichFQAZwX0j3jsAg4H1f56bAPlXNAB4Hngw3TVV9TFUzVDUjJSWlGuGa+lKwu4hVm3fZ8+/G1CER4eRBHYmztzRGjIj5JUTkCBG5U0SWAQ/hSuOiqmNU9aFqTCIPd628XBqwIcx8xgO3A2eo6v6Q3ucBr6tqcch0X/M+vw4MrtYCHYL1hXt5c8H6upp8o/VlTgGAvUHOGNOoREyCB5bjSuunq+pxqvogrh366soEeotIdxFpgqtqf8s/gIgMAx7FJfdw19InAy+GdHsDd+MdwGhgZQ1iqpFHP13NL6YuZH3h3rqaRaOUmZNPfKwwOC0p6FCMMabeRFKCPwdXNT9DRB4XkXGEr3YPS1VLgOtx1evLgFdUdYmI3O1r+vY+oCUw1Xsc7sAJgPfGui7ApyGT/jNwjogsBv4EXHkoC1cd14zuCbhEb2pPVnYBgzonkRAfG3QoxhhTbyLmJjtVfR14XURaAGcCPwNSReTfuGrzDyqdgJvGu8C7Id3u9H0eX8m42bgb9UK7FwKnVnMxDkvn5GZMGt6Fl75Yx09P7EWHJHsP8+HaV1zKorxCLj+2e9ChGGNMvYqkEjwAqrpbVZ9X1dNw19EXAN9rtCZa/fTEnpSq8uhMK8XXhkV52ykuVbvBzhjT6ERcgvdT1XxVfVRVx1Y9dHTo0qY5Zw/rzAvzctm8017mcLjKG7gZnm4N3BhjGpeITvCN1XVjelFcWsYTn60NOpQG78ucAnq1b0mbFk2CDsUYY+qVJfgI1K1dCyYO7cyzc3LYtiv0ST5TXWVlSlZ2PhlWejfGNEKW4CPUdWN6sa+klP98bqX4Q/XN5l3s2Fdi19+NMY2SJfgI1at9S04d1JEps7Mp3FMUdDgNkr1gxhjTmFmCj2A3jO3N7qJSnpyVHXQoDVJWdj4piU3p2qZ50KEYY0y9swQfwfp0SOTkgR14atZatu8trnoEc5DM7AJGdGuNiL1gxhjT+FiCj3DXj+3Fzn0lTJmdHXQoDcq32/eyvnAvGel2/d0Y0zhZgo9wAzolMb5fKv/5fC279pcEHU6DkZXtXjCTYdffjTGNlCX4BuDGcb3YvreYZ+ZkBx1Kg5GVnU/zJrH079gq6FCMMSYQluAbgMFpyZzYJ4UnPlvLniIrxVdHZnYBw7om27upjTGNlh39GogbxvYmf3cRz8/NDTqUiLdjXzHLN+6w6+/GmEbNEnwDMTy9Ncf3bsejM9ewt6g06HAi2vzcQsoURlgDN8aYRswSfANyw9jebN21nxe/sFJ8ZbKy84mNEYZ2TQ46FGOMCYwl+AZkZPc2HNWjDY/OXM2+YivFVyQru4B+HRNp2TQu6FCMMSYwluAbmBvH9mbTjv1MzVoXdCgRqbi0jPnrCuz6uzGm0bME38Ac3bMtGemt+fcnqykqKQs6nIizZMMO9hWX2fV3Y0yjZwm+gRERbhzXmw3b9/HaV3lBhxNxsrwXzFgDN8aYxs4SfAN0fO92DOmSzMMzVlFcaqV4v8zsfLq2aU5qq4SgQzHGmEBZgm+ARISbxvUir2Avr89fH3Q4EUNVycousNK7McZgCb7BGtOnPQM7t+JfM1ZRYqV4ALK37WHb7iK7wc4YY7AE32CJCDeM7U32tj28vWhD0OFEhEzv+vsIK8EbY4wl+IbspH6p9O2QyIMfr6K0TIMOJ3BZ2fkkN4+nZ0rLoEMxxpjAWYJvwGJi3B31a7bs5t3F3wYdTuCysgvISG9NTIwEHYoxxgTOEnwDN2FAB3q3b8mDH39DWSMuxW/dtZ81W3eTYc+/G2MMYAm+wYuJEa4f24uVm3bxwdKNQYcTmKzsAsCuvxtjTDlL8FHgtMGd6NGuBf+YvgrVxlmK/zInnyZxMQzsnBR0KMYYExEswUeB2BjhujG9WPbtDj5atjnocAKRmV3AkLQkmsbFBh2KMcZEBEvwUWLi0E50bdOcBz/+ptGV4vcWlfL1+u12/d0YY3yiKsGLyAQRWSEiq0Tk1jD9fy4iS0VkkYhMF5F0r/sYEVng+9snImeGjPugiOyqr2WpqbjYGK4b05NFedv5ZOWWoMOpVwvWFVJSpnb93RhjfKImwYtILPAwcDLQH5gsIv1DBpsPZKjqYOBV4F4AVZ2hqkNVdSgwFtgDfOCbdgaQXPdLcXjOGpZG5+Rm/HN64yrFl79gZnhXK8EbY0y5qEnwwEhglaquUdUi4CVgon8AL5Hv8b7OBdLCTGcSMK18OO/E4T7gl3UWeS1pEhfDT8f0ZH5uIbNWbQs6nHqTmVNAn9REkprHBx2KMcZEjGhK8J2Bdb7veV63ilwBTAvT/QLgRd/364G3VLXSlmRE5GoRyRKRrC1bgqsinzQ8jY5JCfxj+spGUYovLVPm5xQw3KrnjTHmINGU4MM1XxY2w4nIRUAGrmTu794RGAS8733vBJwLPFjVzFX1MVXNUNWMlJSUGoZee5rGxfKT0T3JzC5g7pr8wOKoLys27mTn/hK7/m6MMSGiKcHnAV1839OA772FRUTGA7cDZ6jq/pDe5wGvq2qx930Y0AtYJSLZQHMRWVXbgde280d0oX1iUx78+JugQ6lzWTnuJMbeIGeMMQeLpgSfCfQWke4i0gRX1f6WfwARGQY8ikvu4R4Yn4yvel5V/6eqHVS1m6p2A/aoaq86W4JakhAfy9Un9GD26m0H3rAWrTKzC+jQKoG01s2CDsUYYyJK1CR4VS3BXS9/H1gGvKKqS0TkbhE5wxvsPqAlMNV7HO7ACYCIdMPVAHxar4HXkR+NSqddyyb8c3r0luJVlcy1+WR0a42IvWDGGGP84oIOoDap6rvAuyHd7vR9Hl/JuNlUflMeqtpg3kParEksVx3fgz9NW8783AKGdY2+a9TrC/eyccc+RlgDN8YY8z1RU4I333fRUem0bh7Pgx9H/G0Dh2TGCve0wvD06Dt5McaYw2UJPoq1aBrHlcf34OPlm1mctz3ocGrVmwvW87u3ljA4LYl+HVsFHY4xxkQcS/BR7pKj02mVEBdVd9Q/PWstN720gOHprXnuylHExtj1d2OMCWUJPsolJsRz+XHd+WDpJpZu2BF0OIdFVbn/gxXc9fZSftA/lSmXj6RVgrVeZ4wx4ViCbwR+fEx3EpvG8dCMhluKLy1TfvPG1/zz41Wcn9GFf/3oSBLi7dWwxhhTEUvwjUBS83guPaYb077eyMpNO4MOp8b2l5Ryw4tf8fy8XH4yuid/PmcQcbG26RpjTGXsKNlIXHFcd5rFx/JQA7ujftf+Ei5/OpN3F2/k9lP6cevJfe2Zd2OMqQZL8I1E6xZNuOTobry9aAOrt0Tsa+0Psm3Xfi58fC5z1+Tz13OHcNUJPYIOyRhjGgxL8I3Ilcd3p2lcDA/PiPxS/PrCvZz76BxWbNzJoxcNZ9LwcG/2NcYYUxFL8I1Iu5ZNuWhUOm8u2EDOtt1Bh1Ohbzbt5Jx/zWbLzv08d+UoxvdPDTokY4xpcCzBNzJXn9CDuBjhXzNWBx1KWF/lFnDuo3MoVeWVa462ZmiNMeYQWYJvZNq3SmDyyK689lUe6/L3BB3OQT5ZsZkfPT6PpGbxvPaTY6yFOmOMOQyW4Buha0b3IEaEf38aOaX4Nxes58opWXRv14KpPzmarm2bBx2SMcY0aJbgG6GOSc04NyONqVnr2FC4N+hwmDI7m5tfdk3PvnTNUbRPTAg6JGOMafAswTdS157YE1V4NMBSvKpy/4cr+e1bSxjfz5qeNcaY2mQJvpFKa92cScPTeDFzHZt37Kv3+ZeWKXe8+TX/nP4N52Wk8W9retYYY2qVJfhG7Kcn9qK0THl05pp6ne/+klJufGk+z83N5ZrRPfjLOYOt6VljjKlldlRtxLq2bc6ZQzvz/LwctuzcXy/z3LW/hCuezuJ/i77ltlP68uuT+1nTs8YYUwcswTdy143pSVFJGU98Vvel+PzdRfzo8bnMWbONv547hKtP6Fnn8zTGmMbKEnwj1yOlJacP6cSzc3PI311UZ/NZX7iXSY/MZrk1PWuMMfXCErzh+jG92Ftcyn8+r5tS/KrNO5n0b9f07LNXWNOzxhhTHyzBG3qnJnLKoI5MmZ1D4Z7aLcXPzy1g0iNzKClTXr76aEZ2t6ZnjTGmPliCNwDcMLYXu/aX8NSs7Fqb5qcrt3Chr+nZ/p2s6VljjKkvluANAH07tOKHA1J5ctZaduwrPuzpvbVwA1dOyaSbNT1rjDGBsARvDrhhbG927ithymGW4p+Zk81NL81nWNfWvGxNzxpjTCAswZsDBnZOYlzf9vxn1lp27S+p8fiqyt8/XMmdby5hXN9UnrGmZ40xJjCW4M1BbhjXm8I9xTw7J6dG45WWKXe+uYR/TP+Gc4en8chF1vSsMcYEyRK8OcjQLsmccEQKj3+2hj1F1SvFlzc9++zcHK4Z3YN7J1nTs8YYEzQ7CpvvuWlcL/J3F/HCvNwqh91tTc8aY0xEsgRvvmd4ehuO7dWWRz5dw77i0gqHy99dxIVe07P3TRpsTc8aY0wEiaoELyITRGSFiKwSkVvD9P+5iCwVkUUiMl1E0r3uY0Rkge9vn4ic6fV73pvm1yLypIg0irvGbhjbm6279vPiF+FL8esL93Ku1/TsIxcN59yMLvUcoTHGmMpETYIXkVjgYeBkoD8wWUT6hww2H8hQ1cHAq8C9AKo6Q1WHqupQYCywB/jAG+d5oC8wCGgGXFnXyxIJjurRlpHd2/DIp6u/V4ovb3p2s9f07EnW9KwxxkScqEnwwEhglaquUdUi4CVgon8AL5Hv8b7OBcK98WQSMK18OFV9Vz3AFxWME5VuGtebTTv2M/XLvAPdFqwr5FxretYYYyJeNCX4zsA63/c8r1tFrgCmhel+AfBiaEevav5i4L1wExORq0UkS0SytmzZUu2gI9kxPdtyZNdkHvlkNUUlZcxcuYULH59LYoI1PWuMMZEumhJ8uFu3NeyAIhcBGcB9Id074qri3w8z2r+Amar6WbhpqupjqpqhqhkpKSk1CjxSiQg3juvN+sK93DJ1IVdMySS9bQtevdaanjXGmEgXF3QAtSgP8N/plQZsCB1IRMYDtwOjVXV/SO/zgNdVtThknN8CKcA1tRpxAzD6iBSGpCXx9sINjOzehscvySCpWaO4z9AYYxq0aErwmUBvEekOrMdVtV/oH0BEhgGPAhNUdXOYaUwGfh0yzpXAD4FxqlpWF4FHMhHhnrMH8e7ib7lhbG9rnc4YYxqIqEnwqloiItfjqtdjgSdVdYmI3A1kqepbuCr5lsBUrzGWXFU9A0BEuuFqAD4NmfQjQA4wxxvnv6p6d90vUeQY0CmJAZ2Sgg7DGGNMDURNggd3xzvwbki3O32fx1cybjZhbspT1ahaR8YYYxqHaLrJzhhjjDEeS/DGGGNMFLIEb4wxxkQhS/DGGGNMFLIEb4wxxkQhS/DGGGNMFLIEb4wxxkQhcS9JM7VJRLbgGsc5FO2ArbUYTm2xuGrG4qoZi6vmIjW2w4krXVWj42UeEcASfIQRkSxVzQg6jlAWV81YXDVjcdVcpMYWqXE1RlZFb4wxxkQhS/DGGGNMFLIEH3keCzqAClhcNWNx1YzFVXORGlukxtXo2DV4Y4wxJgpZCd4YY4yJQpbgjTHGmChkCT4gIjJBRFaIyCoRuTVM/xNE5CsRKRGRSREU189FZKmILBKR6SKSHiFx/UREFovIAhH5XET6R0JcvuEmiYiKSL08PlSN9XWZiGzx1tcCEbkyEuLyhjnP28aWiMgLkRCXiPzdt65WikhhhMTVVURmiMh8b588JULiSveOD4tE5BMRSauPuEwIVbW/ev4DYoHVQA+gCbAQ6B8yTDdgMPAMMCmC4hoDNPc+Xwu8HCFxtfJ9PgN4LxLi8oZLBGYCc4GMSIgLuAx4qD62qxrG1RuYD7T2vrePhLhChr8BeDIS4sLd0Hat97k/kB0hcU0FLvU+jwWerc9tzf7cn5XggzESWKWqa1S1CIqYiqsAAAYdSURBVHgJmOgfQFWzVXURUBZhcc1Q1T3e17lAfZyZVyeuHb6vLYD6uHu0yrg8vwfuBfbVQ0w1iau+VSeuq4CHVbUAQFU3R0hcfpOBFyMkLgVaeZ+TgA0REld/YLr3eUaY/qYeWIIPRmdgne97ntctaDWN6wpgWp1G5FQrLhG5TkRW45LpjZEQl4gMA7qo6jv1EE+14/Kc41WhvioiXSIkriOAI0RklojMFZEJERIX4Kqege7AxxES113ARSKSB7yLq12IhLgWAud4n88CEkWkbT3EZnwswQdDwnSLhOcVqx2XiFwEZAD31WlE3uzCdPteXKr6sKr2BH4F/KbOo6oiLhGJAf4O3FIPsfhVZ329DXRT1cHAR8CUOo+qenHF4arpT8SVlJ8QkeQIiKvcBcCrqlpah/GUq05ck4GnVTUNOAV41tvugo7rF8BoEZkPjAbWAyV1HJcJYQk+GHmAv8SURv1UrVWlWnGJyHjgduAMVd0fKXH5vAScWacROVXFlQgMBD4RkWzgKOCterjRrsr1parbfL/d48DwOo6pWnF5w7ypqsWquhZYgUv4QcdV7gLqp3oeqhfXFcArAKo6B0jAvewl0LhUdYOqnq2qw3DHClR1ex3HZUIFfRNAY/zDlVLW4Kr6ym9SGVDBsE9TfzfZVRkXMAx3g03vSFpf/niA04GsSIgrZPhPqJ+b7Kqzvjr6Pp8FzI2QuCYAU7zP7XBVwW2Djssbrg+QjddAWISsr2nAZd7nfrhEW6fxVTOudkCM9/mPwN31sc7sL+S3CjqAxvqHq05b6SXL271ud+NKxfD/7d1PaFxVFMfx7w//RSuKIHQnsULxX1v/NC7caAsWBBdKpRG0uBYR6aqCWFpcWBCUKlZRKXEjpAURXSlS/xRRmxqTTmOR0lpxUSqKdSGoaI6Le8Z5hkmZUedleP4+8OC+O/fyzrwQzvuT3ANjlCvln4EfgLkhies94DQwk9tbQxLXLmAuY3r/bIm2zrgWjK0lwfd4vp7K8zWb5+vqIYlLwDPAl0ALuG8Y4sr97cDOOuLp43xdC3ycP8cZYMOQxHUvcCzHvApcUOd581Y2L1VrZmbWQH4Hb2Zm1kBO8GZmZg3kBG9mZtZATvBmZmYN5ARvZmbWQE7wZjWRtFzS65JOSPpc0ieS7lnquP4tSRN1Vjw0s944wZvVQJKAN4GPImJFRNxMWRXNZTTNbCCc4M3qsR74LSJeandExDcR8TyApFFJByRN53Zr9t8u6UNJe7MO+U5J90s6KKkl6aocNyHpxawNfkLSbZL2SDoqaaJ9zBxzKGut71gYpKRrJB2s7I9KOpztbZKmJB2R9HJetCycf1LS5dleK+mDbC/LeKaydrmri5kNmBO8WT2uA6bP8vl3wB0RcRMwDjxX+WwN8CiwCtgMrIyIWygrhFWrh11GuZDYQikm82wed5WkG3LM4xGxFlhNKQayuhpERBwFzpe0IrvGybXOKfXjxyLieuBC4K5evzxlPfL9ETEGrAOelrSsj/lm1icneLMlIOkFSbOSprLrPOAVSS1gH2UJ0rapiDgVpTjMceDd7G8Bo5Vxb0dZmrIFnI6IVkTMU5akbY/bJGka+IKS/KvHadsLbMr2ODCZ7XWSPssY1+f8Xm0AHpM0Q1mydwS4oo/5Ztanc5c6ALP/iTk69bGJiIfzUfah7NpCWeN/DeXC+5fK3GrFvvnK/jx//x3+tcuYv8ZJupJSxnMsIn7MR/cjXWKdBPZJeqOEGsckjQC7KWvpfytp+yJzf6dz41D9XMDGiPiqyxwzGwDfwZvVYz8wIumhSt9FlfalwKm8494MnDOAGC6hFC/6SdJy4M5ugyLiOPAH8ASdu/d2sv5e0sWUYiLdnKRTenZjpf8d4JH2e3tJN/7D72BmPXKCN6tBPjq/m/Le++v8Q7bXgK05ZDfwoKRPgZWURPxfxzBLeTQ/B+yhVCFbzCTwAJ1a42codeNblP8GmFpk3g5gl6QDlIuEticpryEOSzqS+2Y2QK4mZ2Zm1kC+gzczM2sgJ3gzM7MGcoI3MzNrICd4MzOzBnKCNzMzayAneDMzswZygjczM2ugPwEV8vgubnaJbwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b28d6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'gamma': 0.40000000000000002, 'score': 0.28199004975124375}\n"
     ]
    }
   ],
   "source": [
    "# generate plot showing SVC Classifier score based on different gamma values \n",
    "# and print out gamma value that results in highest score\n",
    "\n",
    "scores = []\n",
    "\n",
    "highest_score = {\n",
    "    'gamma': 0,\n",
    "    'score': 0\n",
    "}\n",
    "\n",
    "gamma_values = np.arange(0.1, 1.0, 0.1)\n",
    "\n",
    "for val in gamma_values:\n",
    "    svc_classifier = SVC(kernel='rbf', gamma=val)\n",
    "    svc_classifier.fit(train_data, train_labels)\n",
    "    score = svc_classifier.score(test_data, test_labels)\n",
    "    scores.append(score)\n",
    "    if score > highest_score['score']:\n",
    "        highest_score = {\n",
    "            'gamma': val,\n",
    "            'score': score\n",
    "        }\n",
    "\n",
    "x_values = gamma_values\n",
    "\n",
    "y_values = scores\n",
    "\n",
    "plt.plot(x_values, y_values)\n",
    "plt.title('Accuracy of Support Vector Machines classifier based on different gamma values')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Gamma value')\n",
    "plt.show()\n",
    "print(highest_score)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}