Data_exploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Organics Data Project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "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>gender</th>\n",
       "      <th>region</th>\n",
       "      <th>loyalty_level</th>\n",
       "      <th>tvregion</th>\n",
       "      <th>affluence</th>\n",
       "      <th>age</th>\n",
       "      <th>loyalty_tenure</th>\n",
       "      <th>purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>Midlands</td>\n",
       "      <td>Tin</td>\n",
       "      <td>Midlands</td>\n",
       "      <td>10</td>\n",
       "      <td>65</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>North</td>\n",
       "      <td>Tin</td>\n",
       "      <td>N East</td>\n",
       "      <td>13</td>\n",
       "      <td>62</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>Midlands</td>\n",
       "      <td>Silver</td>\n",
       "      <td>East</td>\n",
       "      <td>10</td>\n",
       "      <td>62</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>Midlands</td>\n",
       "      <td>Silver</td>\n",
       "      <td>Midlands</td>\n",
       "      <td>8</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M</td>\n",
       "      <td>South East</td>\n",
       "      <td>Silver</td>\n",
       "      <td>S &amp; S East</td>\n",
       "      <td>7</td>\n",
       "      <td>66</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17958</th>\n",
       "      <td>F</td>\n",
       "      <td>South East</td>\n",
       "      <td>Tin</td>\n",
       "      <td>London</td>\n",
       "      <td>15</td>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17959</th>\n",
       "      <td>F</td>\n",
       "      <td>South East</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>S &amp; S East</td>\n",
       "      <td>3</td>\n",
       "      <td>54</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17961</th>\n",
       "      <td>F</td>\n",
       "      <td>South East</td>\n",
       "      <td>Silver</td>\n",
       "      <td>London</td>\n",
       "      <td>13</td>\n",
       "      <td>65</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17962</th>\n",
       "      <td>F</td>\n",
       "      <td>North</td>\n",
       "      <td>Gold</td>\n",
       "      <td>Yorkshire</td>\n",
       "      <td>9</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17963</th>\n",
       "      <td>F</td>\n",
       "      <td>North</td>\n",
       "      <td>Silver</td>\n",
       "      <td>N West</td>\n",
       "      <td>11</td>\n",
       "      <td>66</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15401 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender      region loyalty_level    tvregion  affluence  age  \\\n",
       "0          M    Midlands           Tin    Midlands         10   65   \n",
       "1          M       North           Tin      N East         13   62   \n",
       "2          M    Midlands        Silver        East         10   62   \n",
       "4          M    Midlands        Silver    Midlands          8   54   \n",
       "5          M  South East        Silver  S & S East          7   66   \n",
       "...      ...         ...           ...         ...        ...  ...   \n",
       "17958      F  South East           Tin      London         15   31   \n",
       "17959      F  South East      Platinum  S & S East          3   54   \n",
       "17961      F  South East        Silver      London         13   65   \n",
       "17962      F       North          Gold   Yorkshire          9   70   \n",
       "17963      F       North        Silver      N West         11   66   \n",
       "\n",
       "       loyalty_tenure  purchase  \n",
       "0                   7         1  \n",
       "1                   5         0  \n",
       "2                   3         0  \n",
       "4                   1         0  \n",
       "5                   5         0  \n",
       "...               ...       ...  \n",
       "17958               5         1  \n",
       "17959               3         1  \n",
       "17961               5         0  \n",
       "17962               5         0  \n",
       "17963               5         0  \n",
       "\n",
       "[15401 rows x 8 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas import Series, DataFrame\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from patsy import dmatrices\n",
    "%pylab inline\n",
    "\n",
    "organic = pd.read_csv(\"Organics Project Data.csv\")\n",
    "organic.columns = ['gender','region','loyalty_level','tvregion','affluence','age','loyalty_tenure','purchase']\n",
    "\n",
    "# CLEANING: drop NaN and non-entries, convert necessary variables to intergers. \n",
    "organic = organic.dropna()\n",
    "organic = organic[organic.age != \".\"]\n",
    "organic = organic[organic['loyalty_tenure'] != \".\"]\n",
    "organic = organic[organic['affluence'] != \".\"]\n",
    "organic['age'] = organic['age'].astype(np.int64)\n",
    "organic['loyalty_tenure'] = organic['loyalty_tenure'].astype(np.int64)\n",
    "organic['affluence'] = organic['affluence'].astype(np.int64)\n",
    "\n",
    "display(organic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The Organic Project Data contains 8 variables and over 22,000 observations. \n",
    "* **gender**: M = male, F = female, U = unknown\n",
    "* **region**: Geographic region\n",
    "* **loyalty level:**: tin, silver, gold, or platinum\n",
    "* **affluence**:  grade on a scale from 1 to 30\n",
    "* **loyalty_tenure**: Time as loyalty card member"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'number of observations')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "organic['purchase'].hist(bins=5)\n",
    "xlabel('purchase')\n",
    "ylabel('number of observations')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Design Matrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "formula = ('purchase ~ 0 + C(gender) + C(region) + C(loyalty_level) + C(tvregion) + affluence + age + loyalty_tenure')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y,X = dmatrices(formula, organic, return_type = 'dataframe')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = Y['purchase'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Purchase vs affluence "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>purchase</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>affluence</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>126</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>339</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>623</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1029</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1375</td>\n",
       "      <td>249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1501</td>\n",
       "      <td>345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1455</td>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1364</td>\n",
       "      <td>503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1110</td>\n",
       "      <td>495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>794</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>550</td>\n",
       "      <td>388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>318</td>\n",
       "      <td>331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>192</td>\n",
       "      <td>265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>118</td>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>59</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>24</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>11</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "purchase      0    1\n",
       "affluence           \n",
       "0             3    0\n",
       "1            24    0\n",
       "2           126   10\n",
       "3           339   39\n",
       "4           623   58\n",
       "5          1029  136\n",
       "6          1375  249\n",
       "7          1501  345\n",
       "8          1455  423\n",
       "9          1364  503\n",
       "10         1110  495\n",
       "11          794  444\n",
       "12          550  388\n",
       "13          318  331\n",
       "14          192  265\n",
       "15          118  202\n",
       "16           59  135\n",
       "17           24   90\n",
       "18           11   61\n",
       "19            3   54\n",
       "20            0   47\n",
       "21            0   30\n",
       "22            0   19\n",
       "23            0   15\n",
       "24            0   13\n",
       "25            0   12\n",
       "26            0    5\n",
       "27            0    5\n",
       "28            0    1\n",
       "29            0    3\n",
       "30            0    2\n",
       "31            0    2\n",
       "34            0    1"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purchase_vs_affluence = pd.crosstab(organic['affluence'],organic['purchase'])\n",
    "purchase_vs_affluence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>purchase</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>affluence</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000272</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.002178</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.011436</td>\n",
       "      <td>0.002282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.030768</td>\n",
       "      <td>0.008898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.056544</td>\n",
       "      <td>0.013233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.093393</td>\n",
       "      <td>0.031029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.124796</td>\n",
       "      <td>0.056810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.136232</td>\n",
       "      <td>0.078713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.132057</td>\n",
       "      <td>0.096509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.123797</td>\n",
       "      <td>0.114762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.100744</td>\n",
       "      <td>0.112936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.072064</td>\n",
       "      <td>0.101300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.049918</td>\n",
       "      <td>0.088524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.028862</td>\n",
       "      <td>0.075519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.017426</td>\n",
       "      <td>0.060461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.010710</td>\n",
       "      <td>0.046087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.005355</td>\n",
       "      <td>0.030801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.002178</td>\n",
       "      <td>0.020534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.000998</td>\n",
       "      <td>0.013917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.000272</td>\n",
       "      <td>0.012320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000228</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "purchase          0         1\n",
       "affluence                    \n",
       "0          0.000272  0.000000\n",
       "1          0.002178  0.000000\n",
       "2          0.011436  0.002282\n",
       "3          0.030768  0.008898\n",
       "4          0.056544  0.013233\n",
       "5          0.093393  0.031029\n",
       "6          0.124796  0.056810\n",
       "7          0.136232  0.078713\n",
       "8          0.132057  0.096509\n",
       "9          0.123797  0.114762\n",
       "10         0.100744  0.112936\n",
       "11         0.072064  0.101300\n",
       "12         0.049918  0.088524\n",
       "13         0.028862  0.075519\n",
       "14         0.017426  0.060461\n",
       "15         0.010710  0.046087\n",
       "16         0.005355  0.030801\n",
       "17         0.002178  0.020534\n",
       "18         0.000998  0.013917\n",
       "19         0.000272  0.012320\n",
       "20         0.000000  0.010723\n",
       "21         0.000000  0.006845\n",
       "22         0.000000  0.004335\n",
       "23         0.000000  0.003422\n",
       "24         0.000000  0.002966\n",
       "25         0.000000  0.002738\n",
       "26         0.000000  0.001141\n",
       "27         0.000000  0.001141\n",
       "28         0.000000  0.000228\n",
       "29         0.000000  0.000684\n",
       "30         0.000000  0.000456\n",
       "31         0.000000  0.000456\n",
       "34         0.000000  0.000228"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_affluence = purchase_vs_affluence.apply(sum)\n",
    "purchase_vs_affluence /= total_affluence\n",
    "purchase_vs_affluence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "affluence\n",
       "9     0.114762\n",
       "10    0.112936\n",
       "11    0.101300\n",
       "8     0.096509\n",
       "12    0.088524\n",
       "7     0.078713\n",
       "13    0.075519\n",
       "14    0.060461\n",
       "6     0.056810\n",
       "15    0.046087\n",
       "5     0.031029\n",
       "16    0.030801\n",
       "17    0.020534\n",
       "18    0.013917\n",
       "4     0.013233\n",
       "19    0.012320\n",
       "20    0.010723\n",
       "3     0.008898\n",
       "21    0.006845\n",
       "22    0.004335\n",
       "23    0.003422\n",
       "24    0.002966\n",
       "25    0.002738\n",
       "2     0.002282\n",
       "26    0.001141\n",
       "27    0.001141\n",
       "29    0.000684\n",
       "31    0.000456\n",
       "30    0.000456\n",
       "34    0.000228\n",
       "28    0.000228\n",
       "1     0.000000\n",
       "0     0.000000\n",
       "Name: 1, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purchase_vs_affluence[1].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8338058290>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "purchase_vs_affluence.plot(kind='bar', figsize=(10,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**\n",
    "* affluence from 9-11 has a higher tendency to purchase organic products\n",
    "* surprisingly affluence from 30-34 has a lower tendency to purchase organic products\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### purchase vs age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>purchase</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>162</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>188</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>163</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>119</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>88</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "purchase    0   1\n",
       "age              \n",
       "18          4   0\n",
       "19          4   0\n",
       "20          2   6\n",
       "21          2   4\n",
       "22          6  11\n",
       "..        ...  ..\n",
       "75        162  39\n",
       "76        188  37\n",
       "77        163  39\n",
       "78        119  34\n",
       "79         88  21\n",
       "\n",
       "[62 rows x 2 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purchase_vs_age = pd.crosstab(organic['age'],organic['purchase'])\n",
    "purchase_vs_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "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>purchase</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.000363</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.000363</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.000182</td>\n",
       "      <td>0.001369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.000182</td>\n",
       "      <td>0.000913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.000545</td>\n",
       "      <td>0.002510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>0.014703</td>\n",
       "      <td>0.008898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>0.017063</td>\n",
       "      <td>0.008442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>0.014794</td>\n",
       "      <td>0.008898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>0.010801</td>\n",
       "      <td>0.007757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>0.007987</td>\n",
       "      <td>0.004791</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "purchase         0         1\n",
       "age                         \n",
       "18        0.000363  0.000000\n",
       "19        0.000363  0.000000\n",
       "20        0.000182  0.001369\n",
       "21        0.000182  0.000913\n",
       "22        0.000545  0.002510\n",
       "..             ...       ...\n",
       "75        0.014703  0.008898\n",
       "76        0.017063  0.008442\n",
       "77        0.014794  0.008898\n",
       "78        0.010801  0.007757\n",
       "79        0.007987  0.004791\n",
       "\n",
       "[62 rows x 2 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_age = purchase_vs_age.apply(sum)\n",
    "purchase_vs_age /= total_age\n",
    "purchase_vs_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age\n",
       "37    0.050194\n",
       "39    0.041980\n",
       "35    0.039699\n",
       "36    0.037874\n",
       "38    0.037645\n",
       "        ...   \n",
       "23    0.002282\n",
       "20    0.001369\n",
       "21    0.000913\n",
       "19    0.000000\n",
       "18    0.000000\n",
       "Name: 1, Length: 62, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purchase_vs_age[1].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f83377e9710>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "purchase_vs_age.plot(kind='bar', figsize=(10,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**\n",
    "* aging group from 35-38 has a higher tendency to purchase organic products\n",
    "* aging group from 18-23 has a lower tendency to purchase organic products. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split the data into training and testing sets "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit the Classifer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hau/Anaconda3/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()\n",
    "result = model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate Training Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7845083487940631\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "prediction_train = model.predict(X_train)\n",
    "print(metrics.accuracy_score(y_train, prediction_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate Testing Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7931183726466133\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "prediction_test = model.predict(X_test)\n",
    "print(metrics.accuracy_score(y_test, prediction_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Explaination**\n",
    "* The **training accuracy** and **testing accuracy** is pretty close. \n",
    "* We can confirm the **Logistics Regression Model** has the correct accuracy level"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Baseline Accuracy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of positive examples = 3092\n",
      "Number of negative examples = 7688\n"
     ]
    }
   ],
   "source": [
    "# y_train is 0 or 1 \n",
    "print('Number of positive examples =', len(y_train[y_train == 1]))\n",
    "print('Number of negative examples =', len(y_train[y_train == 0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Explaination**\n",
    "* **Baseline Accuracy** will always predicting **negative class**\n",
    "* People will be less likely to purchase organic products"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.3072977 , -0.65411672, -0.07677258, -0.04473323, -0.04550844,\n",
       "         0.03276696, -0.2746435 , -0.07627505, -0.03128252, -0.00524695,\n",
       "        -0.00583872, -0.03469452, -0.05558392,  0.21476416,  0.05766547,\n",
       "        -0.16216707, -0.01081392,  0.03276696, -0.1406893 , -0.0104598 ,\n",
       "        -0.12936966,  0.24868766, -0.05425891,  0.00233927]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Slope \n",
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.36045996])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Intercept \n",
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C(gender)[M]                   -0.654117\n",
       "C(loyalty_level)[T.Platinum]   -0.274643\n",
       "C(tvregion)[T.N West]          -0.162167\n",
       "C(tvregion)[T.Ulster]          -0.140689\n",
       "C(tvregion)[T.Yorkshire]       -0.129370\n",
       "C(region)[T.North]             -0.076773\n",
       "C(loyalty_level)[T.Silver]     -0.076275\n",
       "C(tvregion)[T.Midlands]        -0.055584\n",
       "age                            -0.054259\n",
       "C(region)[T.South East]        -0.045508\n",
       "C(region)[T.Scottish]          -0.044733\n",
       "C(tvregion)[T.London]          -0.034695\n",
       "C(loyalty_level)[T.Tin]        -0.031283\n",
       "C(tvregion)[T.S & S East]      -0.010814\n",
       "C(tvregion)[T.Wales & West]    -0.010460\n",
       "C(tvregion)[T.East]            -0.005839\n",
       "C(tvregion)[T.C Scotland]      -0.005247\n",
       "loyalty_tenure                  0.002339\n",
       "C(region)[T.South West]         0.032767\n",
       "C(tvregion)[T.S West]           0.032767\n",
       "C(tvregion)[T.N Scot]           0.057665\n",
       "C(tvregion)[T.N East]           0.214764\n",
       "affluence                       0.248688\n",
       "C(gender)[F]                    0.307298\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights = Series(model.coef_[0], index = X.columns.values)\n",
    "weights.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "**Observations**\n",
    "\n",
    "* Male has a negative weight\n",
    "    * Ma`le shoppers are less likely to purchase organic products\n",
    "* Suprisingly, platinum level has a negative weight.\n",
    "    * platinum level (the highest loyalty level) shopper has a less likelyhood to purchase organic products\n",
    "* Female has a positive weight \n",
    "    * Female shoppers are more likely to purchase organic products\n",
    "* affluence has a positive weight \n",
    "    * The more affluence customers are more likely to purchase organic products\n",
    "    * However, from our observation above, only the middle class affluenc"
   ]
  },
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