Starbucks_Capstone_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Project Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This data set contains simulated data that mimics customer behavior on the Starbucks rewards mobile app. Once every few days, Starbucks sends out an offer to users of the mobile app. An offer can be merely an advertisement for a drink or an actual offer such as a discount or BOGO (buy one get one free). Some users might not receive any offer during certain weeks.\n",
    "\n",
    "Not all users receive the same offer, and that is the challenge to solve with this data set.\n",
    "\n",
    "This data set contains three files. The first file describes the characteristics of each offer, including its duration and the amount a customer needs to spend to complete it (difficulty). The second file contains customer demographic data including their age, gender, income, and when they created an account on the Starbucks rewards mobile application. The third file describes customer purchases and when they received, viewed, and completed an offer. An offer is only successful when a customer both views an offer and meets or exceeds its difficulty within the offer's duration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem Statement / Metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The problem that I chose to solve was to build a model that predicts whether a customer will respond to an offer. My strategy for solving this problem has two steps. First, I will combine the offer portfolio, customer profile, and transaction data. Each row of this combined dataset will describe an offer's attributes, customer demographic data, and whether the offer was successful. Second, I will assess the accuracy and F1-score of a naive model that assumes all offers were successful. This provides me with a baseline for evaluating the performance of models that I construct. Accuracy measures how well a model correctly predicts whether an offer is successful. However, if the percentage of successful or unsuccessful offers is very low, accuracy is not a good measure of model performance. For this situation, evaluating a models' precision and recall provides better insight into its performance. I chose the F1-score metric because it is \"a weighted average of the precision and recall metrics\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import math\n",
    "import json\n",
    "import progressbar\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, f1_score\n",
    "from sklearn.metrics import fbeta_score, make_scorer\n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
    "\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in the json files\n",
    "portfolio = pd.read_json('data/portfolio.json', orient='records', lines=True)\n",
    "profile = pd.read_json('data/profile.json', orient='records', lines=True)\n",
    "transcript = pd.read_json('data/transcript.json', orient='records', lines=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Access Portfolio Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>channels</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>duration</th>\n",
       "      <th>id</th>\n",
       "      <th>offer_type</th>\n",
       "      <th>reward</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[email, mobile, social]</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>bogo</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[web, email, mobile, social]</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "      <td>bogo</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[web, email, mobile]</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3f207df678b143eea3cee63160fa8bed</td>\n",
       "      <td>informational</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[web, email, mobile]</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>bogo</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[web, email]</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>discount</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[web, email, mobile, social]</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>\n",
       "      <td>discount</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[web, email, mobile, social]</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>discount</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[email, mobile, social]</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5a8bc65990b245e5a138643cd4eb9837</td>\n",
       "      <td>informational</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[web, email, mobile, social]</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>f19421c1d4aa40978ebb69ca19b0e20d</td>\n",
       "      <td>bogo</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[web, email, mobile]</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>discount</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       channels  difficulty  duration  \\\n",
       "0       [email, mobile, social]          10         7   \n",
       "1  [web, email, mobile, social]          10         5   \n",
       "2          [web, email, mobile]           0         4   \n",
       "3          [web, email, mobile]           5         7   \n",
       "4                  [web, email]          20        10   \n",
       "5  [web, email, mobile, social]           7         7   \n",
       "6  [web, email, mobile, social]          10        10   \n",
       "7       [email, mobile, social]           0         3   \n",
       "8  [web, email, mobile, social]           5         5   \n",
       "9          [web, email, mobile]          10         7   \n",
       "\n",
       "                                 id     offer_type  reward  \n",
       "0  ae264e3637204a6fb9bb56bc8210ddfd           bogo      10  \n",
       "1  4d5c57ea9a6940dd891ad53e9dbe8da0           bogo      10  \n",
       "2  3f207df678b143eea3cee63160fa8bed  informational       0  \n",
       "3  9b98b8c7a33c4b65b9aebfe6a799e6d9           bogo       5  \n",
       "4  0b1e1539f2cc45b7b9fa7c272da2e1d7       discount       5  \n",
       "5  2298d6c36e964ae4a3e7e9706d1fb8c2       discount       3  \n",
       "6  fafdcd668e3743c1bb461111dcafc2a4       discount       2  \n",
       "7  5a8bc65990b245e5a138643cd4eb9837  informational       0  \n",
       "8  f19421c1d4aa40978ebb69ca19b0e20d           bogo       5  \n",
       "9  2906b810c7d4411798c6938adc9daaa5       discount       2  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10 entries, 0 to 9\n",
      "Data columns (total 6 columns):\n",
      "channels      10 non-null object\n",
      "difficulty    10 non-null int64\n",
      "duration      10 non-null int64\n",
      "id            10 non-null object\n",
      "offer_type    10 non-null object\n",
      "reward        10 non-null int64\n",
      "dtypes: int64(3), object(3)\n",
      "memory usage: 560.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "portfolio.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10,  0,  5, 20,  7])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio.difficulty.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7,  5,  4, 10,  3])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio.duration.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10,  0,  5,  3,  2])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio.reward.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['bogo', 'informational', 'discount'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio.offer_type.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean Portfolio Data:\n",
    "\n",
    "- portfolio: rename id col name to offer_id.\n",
    "- portfolio: one-hot encode channels\n",
    "- portfolio: one-hot encode offer_type column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# portfolio: rename id col name to offer_id.\n",
    "portfolio.rename(columns={'id': 'offer_id'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# portfolio: one-hot encode channels\n",
    "\n",
    "for index, row in portfolio.iterrows():\n",
    "    for channel in ['web', 'email', 'social', 'mobile']:\n",
    "        if channel in portfolio.loc[index, 'channels']:\n",
    "            portfolio.loc[index, channel] = 1\n",
    "        else:\n",
    "            portfolio.loc[index, channel] = 0\n",
    "portfolio.drop(columns='channels', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# portfolio: one-hot encode offer_type column\n",
    "\n",
    "for index, row in portfolio.iterrows():\n",
    "    for offertype in ['bogo', 'informational', 'discount']:\n",
    "        if offertype in portfolio.loc[index, 'offer_type']:\n",
    "            portfolio.loc[index, offertype] = 1\n",
    "        else:\n",
    "            portfolio.loc[index, offertype] = 0\n",
    "portfolio.drop(columns='offer_type', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "      <th>reward</th>\n",
       "      <th>web</th>\n",
       "      <th>email</th>\n",
       "      <th>social</th>\n",
       "      <th>mobile</th>\n",
       "      <th>bogo</th>\n",
       "      <th>informational</th>\n",
       "      <th>discount</th>\n",
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       "      <td>7</td>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <th>8</th>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   difficulty  duration                          offer_id  reward  web  email  \\\n",
       "0          10         7  ae264e3637204a6fb9bb56bc8210ddfd      10  0.0    1.0   \n",
       "1          10         5  4d5c57ea9a6940dd891ad53e9dbe8da0      10  1.0    1.0   \n",
       "2           0         4  3f207df678b143eea3cee63160fa8bed       0  1.0    1.0   \n",
       "3           5         7  9b98b8c7a33c4b65b9aebfe6a799e6d9       5  1.0    1.0   \n",
       "4          20        10  0b1e1539f2cc45b7b9fa7c272da2e1d7       5  1.0    1.0   \n",
       "5           7         7  2298d6c36e964ae4a3e7e9706d1fb8c2       3  1.0    1.0   \n",
       "6          10        10  fafdcd668e3743c1bb461111dcafc2a4       2  1.0    1.0   \n",
       "7           0         3  5a8bc65990b245e5a138643cd4eb9837       0  0.0    1.0   \n",
       "8           5         5  f19421c1d4aa40978ebb69ca19b0e20d       5  1.0    1.0   \n",
       "9          10         7  2906b810c7d4411798c6938adc9daaa5       2  1.0    1.0   \n",
       "\n",
       "   social  mobile  bogo  informational  discount  \n",
       "0     1.0     1.0   1.0            0.0       0.0  \n",
       "1     1.0     1.0   1.0            0.0       0.0  \n",
       "2     0.0     1.0   0.0            1.0       0.0  \n",
       "3     0.0     1.0   1.0            0.0       0.0  \n",
       "4     0.0     0.0   0.0            0.0       1.0  \n",
       "5     1.0     1.0   0.0            0.0       1.0  \n",
       "6     1.0     1.0   0.0            0.0       1.0  \n",
       "7     1.0     1.0   0.0            1.0       0.0  \n",
       "8     1.0     1.0   1.0            0.0       0.0  \n",
       "9     0.0     1.0   0.0            0.0       1.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Access Profile Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>became_member_on</th>\n",
       "      <th>gender</th>\n",
       "      <th>id</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>118</td>\n",
       "      <td>20170212</td>\n",
       "      <td>None</td>\n",
       "      <td>68be06ca386d4c31939f3a4f0e3dd783</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55</td>\n",
       "      <td>20170715</td>\n",
       "      <td>F</td>\n",
       "      <td>0610b486422d4921ae7d2bf64640c50b</td>\n",
       "      <td>112000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>118</td>\n",
       "      <td>20180712</td>\n",
       "      <td>None</td>\n",
       "      <td>38fe809add3b4fcf9315a9694bb96ff5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>75</td>\n",
       "      <td>20170509</td>\n",
       "      <td>F</td>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>100000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>118</td>\n",
       "      <td>20170804</td>\n",
       "      <td>None</td>\n",
       "      <td>a03223e636434f42ac4c3df47e8bac43</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  became_member_on gender                                id    income\n",
       "0  118          20170212   None  68be06ca386d4c31939f3a4f0e3dd783       NaN\n",
       "1   55          20170715      F  0610b486422d4921ae7d2bf64640c50b  112000.0\n",
       "2  118          20180712   None  38fe809add3b4fcf9315a9694bb96ff5       NaN\n",
       "3   75          20170509      F  78afa995795e4d85b5d9ceeca43f5fef  100000.0\n",
       "4  118          20170804   None  a03223e636434f42ac4c3df47e8bac43       NaN"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17000 entries, 0 to 16999\n",
      "Data columns (total 5 columns):\n",
      "age                 17000 non-null int64\n",
      "became_member_on    17000 non-null int64\n",
      "gender              14825 non-null object\n",
      "id                  17000 non-null object\n",
      "income              14825 non-null float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 664.1+ KB\n"
     ]
    }
   ],
   "source": [
    "profile.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([118,  55,  75,  68,  65,  58,  61,  26,  62,  49,  57,  40,  64,\n",
       "        78,  42,  56,  33,  46,  59,  67,  53,  22,  96,  69,  20,  45,\n",
       "        54,  39,  41,  79,  66,  29,  44,  63,  36,  76,  77,  30,  51,\n",
       "        27,  73,  74,  70,  89,  50,  90,  60,  19,  72,  52,  18,  71,\n",
       "        83,  43,  47,  32,  38,  34,  85,  48,  35,  82,  21,  24,  81,\n",
       "        25,  37,  23, 100,  28,  84,  80,  87,  86,  94,  31,  88,  95,\n",
       "        93,  91,  92,  98, 101,  97,  99])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile.age.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean Profile Data\n",
    "- profile: rename id col name to customer_id.\n",
    "- profile: change age value 118 to nan.\n",
    "- profile: create readable date format in became_member_on column.\n",
    "- profile: drop rows with no gender, income, age data\n",
    "- profile: convert gender values to numeric 0s and 1s\n",
    "- profile: add start year and start month columns (for further analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# access the duplicate values\n",
    "profile.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# profile: rename id col name customer_id.\n",
    "profile.rename(columns={'id': 'customer_id'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                 2175\n",
       "became_member_on    2175\n",
       "gender                 0\n",
       "customer_id         2175\n",
       "income                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile.where(profile.age==118).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# profile: change age value 118 to nan.\n",
    "profile.age.replace(118, np.nan, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# profile: create readable date format for became_member_on column.\n",
    "profile.became_member_on = profile['became_member_on'].astype(str).astype('datetime64[ns]', format = \"%Y%m%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2175"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(profile[profile['gender'].isnull()].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# profile: drop rows with no gender, income, age data\n",
    "profile = profile.drop(profile[profile['gender'].isnull()].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert gender values to numeric 0s and 1s\n",
    "profile['gender'] = profile['gender'].apply(lambda x: 1 if x == 'M' else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# profile: add start_year column\n",
    "profile['start_year'] = profile.became_member_on.dt.year\n",
    "profile[\"start_month\"] = profile.became_member_on.dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>became_member_on</th>\n",
       "      <th>gender</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>income</th>\n",
       "      <th>start_year</th>\n",
       "      <th>start_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55.0</td>\n",
       "      <td>2017-07-15</td>\n",
       "      <td>0</td>\n",
       "      <td>0610b486422d4921ae7d2bf64640c50b</td>\n",
       "      <td>112000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>75.0</td>\n",
       "      <td>2017-05-09</td>\n",
       "      <td>0</td>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>68.0</td>\n",
       "      <td>2018-04-26</td>\n",
       "      <td>1</td>\n",
       "      <td>e2127556f4f64592b11af22de27a7932</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>65.0</td>\n",
       "      <td>2018-02-09</td>\n",
       "      <td>1</td>\n",
       "      <td>389bc3fa690240e798340f5a15918d5c</td>\n",
       "      <td>53000.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>58.0</td>\n",
       "      <td>2017-11-11</td>\n",
       "      <td>1</td>\n",
       "      <td>2eeac8d8feae4a8cad5a6af0499a211d</td>\n",
       "      <td>51000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>61.0</td>\n",
       "      <td>2017-09-11</td>\n",
       "      <td>0</td>\n",
       "      <td>aa4862eba776480b8bb9c68455b8c2e1</td>\n",
       "      <td>57000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>26.0</td>\n",
       "      <td>2014-02-13</td>\n",
       "      <td>1</td>\n",
       "      <td>e12aeaf2d47d42479ea1c4ac3d8286c6</td>\n",
       "      <td>46000.0</td>\n",
       "      <td>2014</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>62.0</td>\n",
       "      <td>2016-02-11</td>\n",
       "      <td>0</td>\n",
       "      <td>31dda685af34476cad5bc968bdb01c53</td>\n",
       "      <td>71000.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>49.0</td>\n",
       "      <td>2014-11-13</td>\n",
       "      <td>1</td>\n",
       "      <td>62cf5e10845442329191fc246e7bcea3</td>\n",
       "      <td>52000.0</td>\n",
       "      <td>2014</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>57.0</td>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>6445de3b47274c759400cd68131d91b4</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>61.0</td>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>0</td>\n",
       "      <td>a448667f336b42c9a66fc5ffd5d73772</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>40.0</td>\n",
       "      <td>2016-05-04</td>\n",
       "      <td>0</td>\n",
       "      <td>440cf1fd7580490c971d8c651ed962af</td>\n",
       "      <td>71000.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>64.0</td>\n",
       "      <td>2017-09-09</td>\n",
       "      <td>1</td>\n",
       "      <td>4b0da7e80e5945209a1fdddfe813dbe0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>78.0</td>\n",
       "      <td>2017-06-16</td>\n",
       "      <td>0</td>\n",
       "      <td>c27e0d6ab72c455a8bb66d980963de60</td>\n",
       "      <td>71000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>42.0</td>\n",
       "      <td>2013-08-11</td>\n",
       "      <td>1</td>\n",
       "      <td>f806632c011441378d4646567f357a21</td>\n",
       "      <td>69000.0</td>\n",
       "      <td>2013</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>56.0</td>\n",
       "      <td>2018-04-28</td>\n",
       "      <td>0</td>\n",
       "      <td>d058f73bf8674a26a95227db098147b1</td>\n",
       "      <td>88000.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>33.0</td>\n",
       "      <td>2017-09-26</td>\n",
       "      <td>0</td>\n",
       "      <td>c6c9884912c645429f3333f912b55f44</td>\n",
       "      <td>52000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>46.0</td>\n",
       "      <td>2017-09-11</td>\n",
       "      <td>0</td>\n",
       "      <td>7429a044884842d6862f516c38b1156f</td>\n",
       "      <td>59000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>59.0</td>\n",
       "      <td>2015-01-21</td>\n",
       "      <td>1</td>\n",
       "      <td>ebe7ef46ea6f4963a7dd49f501b26779</td>\n",
       "      <td>41000.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>67.0</td>\n",
       "      <td>2014-08-23</td>\n",
       "      <td>1</td>\n",
       "      <td>5917d13f6fa04286abdf95f4d5cefb1e</td>\n",
       "      <td>96000.0</td>\n",
       "      <td>2014</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>53.0</td>\n",
       "      <td>2017-09-16</td>\n",
       "      <td>0</td>\n",
       "      <td>d1ede868e29245ea91818a903fec04c6</td>\n",
       "      <td>52000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>22.0</td>\n",
       "      <td>2017-09-25</td>\n",
       "      <td>1</td>\n",
       "      <td>1e9420836d554513ab90eba98552d0a9</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>96.0</td>\n",
       "      <td>2017-11-17</td>\n",
       "      <td>0</td>\n",
       "      <td>868317b9be554cb18e50bc68484749a2</td>\n",
       "      <td>89000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>40.0</td>\n",
       "      <td>2016-07-09</td>\n",
       "      <td>1</td>\n",
       "      <td>f082d80f0aac47a99173ba8ef8fc1909</td>\n",
       "      <td>33000.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>69.0</td>\n",
       "      <td>2016-08-14</td>\n",
       "      <td>0</td>\n",
       "      <td>102e9454054946fda62242d2e176fdce</td>\n",
       "      <td>57000.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>56.0</td>\n",
       "      <td>2014-05-11</td>\n",
       "      <td>1</td>\n",
       "      <td>8dabe2142cdf4013a6f4786edf8ab00f</td>\n",
       "      <td>68000.0</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>26.0</td>\n",
       "      <td>2017-08-12</td>\n",
       "      <td>0</td>\n",
       "      <td>9f30b375d7bd4c62a884ffe7034e09ee</td>\n",
       "      <td>63000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>75.0</td>\n",
       "      <td>2015-11-12</td>\n",
       "      <td>1</td>\n",
       "      <td>6e014185620b49bd98749f728747572f</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>20.0</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>0</td>\n",
       "      <td>02c083884c7d45b39cc68e1314fec56c</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>45.0</td>\n",
       "      <td>2018-07-07</td>\n",
       "      <td>1</td>\n",
       "      <td>c0d210398dee4a0895b24444a5fcd1d2</td>\n",
       "      <td>33000.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>7</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16963</th>\n",
       "      <td>62.0</td>\n",
       "      <td>2017-01-12</td>\n",
       "      <td>0</td>\n",
       "      <td>8c3fbf6b89b248bb99b7a6ca628f401c</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16964</th>\n",
       "      <td>52.0</td>\n",
       "      <td>2018-04-08</td>\n",
       "      <td>1</td>\n",
       "      <td>68213b08d99a4ae1b0dcb72aebd9aa35</td>\n",
       "      <td>62000.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16965</th>\n",
       "      <td>52.0</td>\n",
       "      <td>2018-04-09</td>\n",
       "      <td>1</td>\n",
       "      <td>e340cbf00a914f0c958577c0af36ed8a</td>\n",
       "      <td>34000.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16966</th>\n",
       "      <td>54.0</td>\n",
       "      <td>2015-01-26</td>\n",
       "      <td>1</td>\n",
       "      <td>10316535e185400fb093c371d62254e3</td>\n",
       "      <td>51000.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16967</th>\n",
       "      <td>54.0</td>\n",
       "      <td>2015-06-26</td>\n",
       "      <td>1</td>\n",
       "      <td>cec1cdc6b77e41abbdce8903c5522e7e</td>\n",
       "      <td>53000.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16968</th>\n",
       "      <td>74.0</td>\n",
       "      <td>2017-07-25</td>\n",
       "      <td>1</td>\n",
       "      <td>2c6f50c1c07a444896e2a4e31d724f92</td>\n",
       "      <td>87000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16970</th>\n",
       "      <td>67.0</td>\n",
       "      <td>2015-11-07</td>\n",
       "      <td>1</td>\n",
       "      <td>a2e6029e17b6466187a40b66e333a73e</td>\n",
       "      <td>94000.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16971</th>\n",
       "      <td>52.0</td>\n",
       "      <td>2018-05-22</td>\n",
       "      <td>0</td>\n",
       "      <td>6e71b66784844d1ab8376ac8ab096d4b</td>\n",
       "      <td>75000.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16972</th>\n",
       "      <td>44.0</td>\n",
       "      <td>2017-01-19</td>\n",
       "      <td>0</td>\n",
       "      <td>e2fd53ed790240c586b3188f23542cca</td>\n",
       "      <td>51000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16973</th>\n",
       "      <td>30.0</td>\n",
       "      <td>2015-10-12</td>\n",
       "      <td>1</td>\n",
       "      <td>08eb126ad33f447ca3ad076482445c05</td>\n",
       "      <td>57000.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16974</th>\n",
       "      <td>59.0</td>\n",
       "      <td>2016-01-13</td>\n",
       "      <td>0</td>\n",
       "      <td>f88e91a11d8f405b9a47ad3741415b83</td>\n",
       "      <td>71000.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16975</th>\n",
       "      <td>61.0</td>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>0</td>\n",
       "      <td>1b4df4b48bf64727a4c445909aea1251</td>\n",
       "      <td>81000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16976</th>\n",
       "      <td>39.0</td>\n",
       "      <td>2016-09-21</td>\n",
       "      <td>0</td>\n",
       "      <td>122c0c2a3c2540848f8c3fdc37e97639</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16978</th>\n",
       "      <td>29.0</td>\n",
       "      <td>2017-02-20</td>\n",
       "      <td>0</td>\n",
       "      <td>54bead4179c44b71acc4e8398181d183</td>\n",
       "      <td>58000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16979</th>\n",
       "      <td>63.0</td>\n",
       "      <td>2013-09-22</td>\n",
       "      <td>0</td>\n",
       "      <td>a00058cf10334a308c68e7631c529907</td>\n",
       "      <td>52000.0</td>\n",
       "      <td>2013</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16981</th>\n",
       "      <td>84.0</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>1</td>\n",
       "      <td>1966fa40d2f84620b2b1b9b64f8e0209</td>\n",
       "      <td>93000.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16983</th>\n",
       "      <td>72.0</td>\n",
       "      <td>2015-04-04</td>\n",
       "      <td>0</td>\n",
       "      <td>ff737c250d2343729ade04c4f6eb1001</td>\n",
       "      <td>65000.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16984</th>\n",
       "      <td>75.0</td>\n",
       "      <td>2016-07-16</td>\n",
       "      <td>0</td>\n",
       "      <td>392d23b2c958493197f647bedfe4f418</td>\n",
       "      <td>78000.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16985</th>\n",
       "      <td>26.0</td>\n",
       "      <td>2018-06-19</td>\n",
       "      <td>1</td>\n",
       "      <td>97ee6e7a12fe4064b260fa48bdd0330f</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16986</th>\n",
       "      <td>59.0</td>\n",
       "      <td>2017-07-31</td>\n",
       "      <td>0</td>\n",
       "      <td>bf3069b178fe40d789dd027901bc406b</td>\n",
       "      <td>63000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16987</th>\n",
       "      <td>57.0</td>\n",
       "      <td>2016-07-09</td>\n",
       "      <td>1</td>\n",
       "      <td>76ddbd6576844afe811f1a3c0fbb5bec</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16988</th>\n",
       "      <td>64.0</td>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>f653cf2d8bba42d0a53c2937ee2e5893</td>\n",
       "      <td>51000.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16990</th>\n",
       "      <td>70.0</td>\n",
       "      <td>2016-03-10</td>\n",
       "      <td>0</td>\n",
       "      <td>79edb810789c447e8d212a324b44cc16</td>\n",
       "      <td>39000.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16992</th>\n",
       "      <td>21.0</td>\n",
       "      <td>2017-09-17</td>\n",
       "      <td>0</td>\n",
       "      <td>400d0536e8794cbb855b0d882d67cbda</td>\n",
       "      <td>72000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16993</th>\n",
       "      <td>60.0</td>\n",
       "      <td>2018-05-05</td>\n",
       "      <td>1</td>\n",
       "      <td>cb23b66c56f64b109d673d5e56574529</td>\n",
       "      <td>113000.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16995</th>\n",
       "      <td>45.0</td>\n",
       "      <td>2018-06-04</td>\n",
       "      <td>0</td>\n",
       "      <td>6d5f3a774f3d4714ab0c092238f3a1d7</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16996</th>\n",
       "      <td>61.0</td>\n",
       "      <td>2018-07-13</td>\n",
       "      <td>1</td>\n",
       "      <td>2cb4f97358b841b9a9773a7aa05a9d77</td>\n",
       "      <td>72000.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16997</th>\n",
       "      <td>49.0</td>\n",
       "      <td>2017-01-26</td>\n",
       "      <td>1</td>\n",
       "      <td>01d26f638c274aa0b965d24cefe3183f</td>\n",
       "      <td>73000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16998</th>\n",
       "      <td>83.0</td>\n",
       "      <td>2016-03-07</td>\n",
       "      <td>0</td>\n",
       "      <td>9dc1421481194dcd9400aec7c9ae6366</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16999</th>\n",
       "      <td>62.0</td>\n",
       "      <td>2017-07-22</td>\n",
       "      <td>0</td>\n",
       "      <td>e4052622e5ba45a8b96b59aba68cf068</td>\n",
       "      <td>82000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14825 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        age became_member_on  gender                       customer_id  \\\n",
       "1      55.0       2017-07-15       0  0610b486422d4921ae7d2bf64640c50b   \n",
       "3      75.0       2017-05-09       0  78afa995795e4d85b5d9ceeca43f5fef   \n",
       "5      68.0       2018-04-26       1  e2127556f4f64592b11af22de27a7932   \n",
       "8      65.0       2018-02-09       1  389bc3fa690240e798340f5a15918d5c   \n",
       "12     58.0       2017-11-11       1  2eeac8d8feae4a8cad5a6af0499a211d   \n",
       "13     61.0       2017-09-11       0  aa4862eba776480b8bb9c68455b8c2e1   \n",
       "14     26.0       2014-02-13       1  e12aeaf2d47d42479ea1c4ac3d8286c6   \n",
       "15     62.0       2016-02-11       0  31dda685af34476cad5bc968bdb01c53   \n",
       "16     49.0       2014-11-13       1  62cf5e10845442329191fc246e7bcea3   \n",
       "18     57.0       2017-12-31       1  6445de3b47274c759400cd68131d91b4   \n",
       "19     61.0       2018-05-01       0  a448667f336b42c9a66fc5ffd5d73772   \n",
       "20     40.0       2016-05-04       0  440cf1fd7580490c971d8c651ed962af   \n",
       "21     64.0       2017-09-09       1  4b0da7e80e5945209a1fdddfe813dbe0   \n",
       "22     78.0       2017-06-16       0  c27e0d6ab72c455a8bb66d980963de60   \n",
       "24     42.0       2013-08-11       1  f806632c011441378d4646567f357a21   \n",
       "25     56.0       2018-04-28       0  d058f73bf8674a26a95227db098147b1   \n",
       "27     33.0       2017-09-26       0  c6c9884912c645429f3333f912b55f44   \n",
       "28     46.0       2017-09-11       0  7429a044884842d6862f516c38b1156f   \n",
       "29     59.0       2015-01-21       1  ebe7ef46ea6f4963a7dd49f501b26779   \n",
       "30     67.0       2014-08-23       1  5917d13f6fa04286abdf95f4d5cefb1e   \n",
       "31     53.0       2017-09-16       0  d1ede868e29245ea91818a903fec04c6   \n",
       "32     22.0       2017-09-25       1  1e9420836d554513ab90eba98552d0a9   \n",
       "33     96.0       2017-11-17       0  868317b9be554cb18e50bc68484749a2   \n",
       "34     40.0       2016-07-09       1  f082d80f0aac47a99173ba8ef8fc1909   \n",
       "35     69.0       2016-08-14       0  102e9454054946fda62242d2e176fdce   \n",
       "37     56.0       2014-05-11       1  8dabe2142cdf4013a6f4786edf8ab00f   \n",
       "38     26.0       2017-08-12       0  9f30b375d7bd4c62a884ffe7034e09ee   \n",
       "40     75.0       2015-11-12       1  6e014185620b49bd98749f728747572f   \n",
       "41     20.0       2016-07-11       0  02c083884c7d45b39cc68e1314fec56c   \n",
       "42     45.0       2018-07-07       1  c0d210398dee4a0895b24444a5fcd1d2   \n",
       "...     ...              ...     ...                               ...   \n",
       "16963  62.0       2017-01-12       0  8c3fbf6b89b248bb99b7a6ca628f401c   \n",
       "16964  52.0       2018-04-08       1  68213b08d99a4ae1b0dcb72aebd9aa35   \n",
       "16965  52.0       2018-04-09       1  e340cbf00a914f0c958577c0af36ed8a   \n",
       "16966  54.0       2015-01-26       1  10316535e185400fb093c371d62254e3   \n",
       "16967  54.0       2015-06-26       1  cec1cdc6b77e41abbdce8903c5522e7e   \n",
       "16968  74.0       2017-07-25       1  2c6f50c1c07a444896e2a4e31d724f92   \n",
       "16970  67.0       2015-11-07       1  a2e6029e17b6466187a40b66e333a73e   \n",
       "16971  52.0       2018-05-22       0  6e71b66784844d1ab8376ac8ab096d4b   \n",
       "16972  44.0       2017-01-19       0  e2fd53ed790240c586b3188f23542cca   \n",
       "16973  30.0       2015-10-12       1  08eb126ad33f447ca3ad076482445c05   \n",
       "16974  59.0       2016-01-13       0  f88e91a11d8f405b9a47ad3741415b83   \n",
       "16975  61.0       2017-12-31       0  1b4df4b48bf64727a4c445909aea1251   \n",
       "16976  39.0       2016-09-21       0  122c0c2a3c2540848f8c3fdc37e97639   \n",
       "16978  29.0       2017-02-20       0  54bead4179c44b71acc4e8398181d183   \n",
       "16979  63.0       2013-09-22       0  a00058cf10334a308c68e7631c529907   \n",
       "16981  84.0       2016-06-29       1  1966fa40d2f84620b2b1b9b64f8e0209   \n",
       "16983  72.0       2015-04-04       0  ff737c250d2343729ade04c4f6eb1001   \n",
       "16984  75.0       2016-07-16       0  392d23b2c958493197f647bedfe4f418   \n",
       "16985  26.0       2018-06-19       1  97ee6e7a12fe4064b260fa48bdd0330f   \n",
       "16986  59.0       2017-07-31       0  bf3069b178fe40d789dd027901bc406b   \n",
       "16987  57.0       2016-07-09       1  76ddbd6576844afe811f1a3c0fbb5bec   \n",
       "16988  64.0       2018-01-04       1  f653cf2d8bba42d0a53c2937ee2e5893   \n",
       "16990  70.0       2016-03-10       0  79edb810789c447e8d212a324b44cc16   \n",
       "16992  21.0       2017-09-17       0  400d0536e8794cbb855b0d882d67cbda   \n",
       "16993  60.0       2018-05-05       1  cb23b66c56f64b109d673d5e56574529   \n",
       "16995  45.0       2018-06-04       0  6d5f3a774f3d4714ab0c092238f3a1d7   \n",
       "16996  61.0       2018-07-13       1  2cb4f97358b841b9a9773a7aa05a9d77   \n",
       "16997  49.0       2017-01-26       1  01d26f638c274aa0b965d24cefe3183f   \n",
       "16998  83.0       2016-03-07       0  9dc1421481194dcd9400aec7c9ae6366   \n",
       "16999  62.0       2017-07-22       0  e4052622e5ba45a8b96b59aba68cf068   \n",
       "\n",
       "         income  start_year  start_month  \n",
       "1      112000.0        2017            7  \n",
       "3      100000.0        2017            5  \n",
       "5       70000.0        2018            4  \n",
       "8       53000.0        2018            2  \n",
       "12      51000.0        2017           11  \n",
       "13      57000.0        2017            9  \n",
       "14      46000.0        2014            2  \n",
       "15      71000.0        2016            2  \n",
       "16      52000.0        2014           11  \n",
       "18      42000.0        2017           12  \n",
       "19      40000.0        2018            5  \n",
       "20      71000.0        2016            5  \n",
       "21     100000.0        2017            9  \n",
       "22      71000.0        2017            6  \n",
       "24      69000.0        2013            8  \n",
       "25      88000.0        2018            4  \n",
       "27      52000.0        2017            9  \n",
       "28      59000.0        2017            9  \n",
       "29      41000.0        2015            1  \n",
       "30      96000.0        2014            8  \n",
       "31      52000.0        2017            9  \n",
       "32      70000.0        2017            9  \n",
       "33      89000.0        2017           11  \n",
       "34      33000.0        2016            7  \n",
       "35      57000.0        2016            8  \n",
       "37      68000.0        2014            5  \n",
       "38      63000.0        2017            8  \n",
       "40      40000.0        2015           11  \n",
       "41      30000.0        2016            7  \n",
       "42      33000.0        2018            7  \n",
       "...         ...         ...          ...  \n",
       "16963   80000.0        2017            1  \n",
       "16964   62000.0        2018            4  \n",
       "16965   34000.0        2018            4  \n",
       "16966   51000.0        2015            1  \n",
       "16967   53000.0        2015            6  \n",
       "16968   87000.0        2017            7  \n",
       "16970   94000.0        2015           11  \n",
       "16971   75000.0        2018            5  \n",
       "16972   51000.0        2017            1  \n",
       "16973   57000.0        2015           10  \n",
       "16974   71000.0        2016            1  \n",
       "16975   81000.0        2017           12  \n",
       "16976   54000.0        2016            9  \n",
       "16978   58000.0        2017            2  \n",
       "16979   52000.0        2013            9  \n",
       "16981   93000.0        2016            6  \n",
       "16983   65000.0        2015            4  \n",
       "16984   78000.0        2016            7  \n",
       "16985   55000.0        2018            6  \n",
       "16986   63000.0        2017            7  \n",
       "16987   40000.0        2016            7  \n",
       "16988   51000.0        2018            1  \n",
       "16990   39000.0        2016            3  \n",
       "16992   72000.0        2017            9  \n",
       "16993  113000.0        2018            5  \n",
       "16995   54000.0        2018            6  \n",
       "16996   72000.0        2018            7  \n",
       "16997   73000.0        2017            1  \n",
       "16998   50000.0        2016            3  \n",
       "16999   82000.0        2017            7  \n",
       "\n",
       "[14825 rows x 7 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Access Transcript Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event</th>\n",
       "      <th>person</th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>offer received</td>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}</td>\n",
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       "      <th>1</th>\n",
       "      <td>offer received</td>\n",
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       "      <td>0</td>\n",
       "      <td>{'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}</td>\n",
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       "      <th>2</th>\n",
       "      <td>offer received</td>\n",
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       "      <td>0</td>\n",
       "      <td>{'offer id': '2906b810c7d4411798c6938adc9daaa5'}</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>offer received</td>\n",
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       "      <td>0</td>\n",
       "      <td>{'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>offer received</td>\n",
       "      <td>68617ca6246f4fbc85e91a2a49552598</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '4d5c57ea9a6940dd891ad53e9dbe8da0'}</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            event                            person  time  \\\n",
       "0  offer received  78afa995795e4d85b5d9ceeca43f5fef     0   \n",
       "1  offer received  a03223e636434f42ac4c3df47e8bac43     0   \n",
       "2  offer received  e2127556f4f64592b11af22de27a7932     0   \n",
       "3  offer received  8ec6ce2a7e7949b1bf142def7d0e0586     0   \n",
       "4  offer received  68617ca6246f4fbc85e91a2a49552598     0   \n",
       "\n",
       "                                              value  \n",
       "0  {'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}  \n",
       "1  {'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}  \n",
       "2  {'offer id': '2906b810c7d4411798c6938adc9daaa5'}  \n",
       "3  {'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}  \n",
       "4  {'offer id': '4d5c57ea9a6940dd891ad53e9dbe8da0'}  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['offer received', 'offer viewed', 'transaction', 'offer completed'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript.event.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 306534 entries, 0 to 306533\n",
      "Data columns (total 4 columns):\n",
      "event     306534 non-null object\n",
      "person    306534 non-null object\n",
      "time      306534 non-null int64\n",
      "value     306534 non-null object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 9.4+ MB\n"
     ]
    }
   ],
   "source": [
    "transcript.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean Transcript Data\n",
    "- transcript: rename person col name to customer_id.\n",
    "- transcript: create separate columns for amount and offer_id from value col.\n",
    "- transcript: drop transaction rows whose customer_id is not in profile:customer_id\n",
    "- transcript: convert time in hours to time in days\n",
    "- transcript: segregate offer and transaction data \n",
    "- transcript: finally drop duplicates if any"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transcript: rename person col name to customer_id.\n",
    "transcript.rename(columns={'person': 'customer_id'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# transcript: create separate columns for amount and offer_id from value col.\n",
    "\n",
    "def create_offer_id_col(val):\n",
    "    if list(val.keys())[0] in ['offer id', 'offer_id']:\n",
    "        return list(val.values())[0]\n",
    "    \n",
    "def create_amount_col(val):\n",
    "    if list(val.keys())[0] in ['amount']:\n",
    "        return list(val.values())[0]\n",
    "    \n",
    "transcript['offer_id'] = transcript.value.apply(create_offer_id_col)\n",
    "transcript['amount'] = transcript.value.apply(create_amount_col)\n",
    "\n",
    "# change amount column type to float\n",
    "transcript.amount.astype('float')\n",
    "\n",
    "# drop value column\n",
    "transcript.drop(columns=['value'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transcript: drop transaction rows whose customer_id is not in profile:customer_id\n",
    "transcript = transcript[transcript.customer_id.isin(profile.customer_id)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "transcript['time'] /= 24.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "374"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# access the duplicate values\n",
    "transcript.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the duplicated value, and reset the index\n",
    "transcript.drop_duplicates(inplace=True)\n",
    "\n",
    "transcript.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 272388 entries, 0 to 272387\n",
      "Data columns (total 5 columns):\n",
      "event          272388 non-null object\n",
      "customer_id    272388 non-null object\n",
      "time           272388 non-null float64\n",
      "offer_id       148431 non-null object\n",
      "amount         123957 non-null float64\n",
      "dtypes: float64(2), object(3)\n",
      "memory usage: 10.4+ MB\n"
     ]
    }
   ],
   "source": [
    "transcript.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['offer received', 'offer viewed', 'transaction', 'offer completed'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript.event.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>24115a61df25473e84a8a03f3c98de1a</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.27</td>\n",
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       "    <tr>\n",
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       "      <td>7195944f0cc34115b0a5e7b4a62055f2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>11.52</td>\n",
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       "      <td>13.93</td>\n",
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       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>11173</th>\n",
       "      <td>2481f1fcfbcb4b288e5a03af02d95373</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>1.43</td>\n",
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       "      <td>14.31</td>\n",
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       "      <td>6.92</td>\n",
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       "      <td>12.73</td>\n",
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       "      <td>2.60</td>\n",
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       "      <td>1.64</td>\n",
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       "    <tr>\n",
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       "      <td>29.75</td>\n",
       "      <td>17.35</td>\n",
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       "    <tr>\n",
       "      <th>272378</th>\n",
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       "      <td>4.42</td>\n",
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       "    <tr>\n",
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       "      <td>18.35</td>\n",
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       "    <tr>\n",
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       "      <td>1.59</td>\n",
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       "    <tr>\n",
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       "      <td>9.53</td>\n",
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       "      <td>3.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>123957 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             customer_id   time  amount\n",
       "11027   02c083884c7d45b39cc68e1314fec56c   0.00    0.83\n",
       "11030   9fa9ae8f57894cc9a3b8a9bbe0fc1b2f   0.00   34.56\n",
       "11032   54890f68699049c2a04d415abc25e717   0.00   13.23\n",
       "11040   b2f1cd155b864803ad8334cdf13c4bd2   0.00   19.51\n",
       "11041   fe97aa22dd3e48c8b143116a8403dd52   0.00   18.97\n",
       "11047   629fc02d56414d91bca360decdfa9288   0.00   33.90\n",
       "11051   a97e6f33219c432db82acfa0d19c602d   0.00   18.59\n",
       "11055   676506bad68e4161b9bbaffeb039626b   0.00   18.01\n",
       "11059   8f7dd3b2afe14c078eb4f6e6fe4ba97d   0.00   19.11\n",
       "11069   4cbe33c601a5407f8202086565c55111   0.00   36.19\n",
       "11072   b432b74402bb4981a4651c8df1670365   0.00    6.46\n",
       "11074   a04fcfd571034456aaa6d56c0a3fd9b6   0.00    5.02\n",
       "11078   227f2d69e46a4899b70d48182822cff6   0.00   28.39\n",
       "11082   bb0f25e23a4c4de6a645527c275cd594   0.00   28.08\n",
       "11086   c2c72ce6038644c797208046d1e3498a   0.00    0.75\n",
       "11103   d72d201be5794279aa716d8ad82b8d90   0.00   13.57\n",
       "11113   ad80753fc9e0485c9e6b1cc9478d827f   0.00   10.22\n",
       "11120   73ffefd41e9a4ca3ab26b2b3697c6eb7   0.00   31.42\n",
       "11123   3e621194f72e40d7a0b695ee9b7c38b7   0.00   27.89\n",
       "11125   1f5c961416e64c5d88098b02b1bdf246   0.00    4.20\n",
       "11131   3bcc51fdde354eb1949c813dbc905182   0.00   13.05\n",
       "11132   b860d355ef6e4c66b5d5a837c56ef32d   0.00   38.38\n",
       "11134   4ad3748475204cf99571183f05b5e2f7   0.00    4.08\n",
       "11136   99297ea01107436fa8c2e2bc86f55d89   0.00    5.78\n",
       "11138   24115a61df25473e84a8a03f3c98de1a   0.00   14.27\n",
       "11147   7195944f0cc34115b0a5e7b4a62055f2   0.00   11.52\n",
       "11150   afce4cf8194f4e90a3e92da941a23601   0.00   13.93\n",
       "11165   a2ffc22184d94d6fa21b924eb49c0d06   0.00   18.97\n",
       "11172   e243c277c5ef41d0b9afbf1eb76cc606   0.00    4.05\n",
       "11173   2481f1fcfbcb4b288e5a03af02d95373   0.00   30.03\n",
       "...                                  ...    ...     ...\n",
       "272355  5bc03149267147edbcd6a778742b708b  29.75   32.23\n",
       "272356  a62b73003f084ae4ae59c4b0e617f647  29.75   19.03\n",
       "272357  197a4afbf77245cfa84c0a4659f47c48  29.75   25.65\n",
       "272358  f0ffaed9279946f1bc29614a234f1ee1  29.75    1.43\n",
       "272359  93907d06a946424ba630487fe7aeafd1  29.75   28.03\n",
       "272360  588ceea482344c3ab6845c83aaed4ac0  29.75    1.08\n",
       "272361  a97208c5be42445d9949e82e0f70f622  29.75    5.80\n",
       "272362  8524d450673b4c24869b6c94380006de  29.75    4.89\n",
       "272364  8431c16f8e1d440880db371a68f82dd0  29.75    1.19\n",
       "272366  ba620885e51c4b0ea64a4f61daad494f  29.75   14.31\n",
       "272367  a1a8f40407c444cc848468275308958a  29.75    2.37\n",
       "272368  8d80970192fa496f99d6b45c470a4b60  29.75    6.92\n",
       "272369  bde275066f3c4fa0bff3093e3b866a2c  29.75   12.73\n",
       "272370  f1e4fd36e5a0446f83861308bddf6945  29.75    8.20\n",
       "272371  0b64be3b241c4407a5c9a71781173829  29.75    2.60\n",
       "272372  86d03d35d7e0434b935e7743e83be3a0  29.75    9.20\n",
       "272373  3408fd05c781401f8442fb6dbaaea9c7  29.75   11.70\n",
       "272374  1593d617fac246ef8e50dbb0ffd77f5f  29.75   40.67\n",
       "272375  f1b31d07b5d84f69a2d5f1d07843989e  29.75   31.13\n",
       "272376  2ce987015ec0404a97ba333e8e814090  29.75    1.64\n",
       "272377  2e33545f0a764d27b2ccff95fc8d72c4  29.75   17.35\n",
       "272378  d1c4500ace2e45e9a45d3cd2fccac8d8  29.75    4.42\n",
       "272379  b65affd9e07346a1906364a396950e3d  29.75   18.35\n",
       "272380  d613ca9c59dd42f497bdbf6178da54a7  29.75   25.14\n",
       "272381  eec70ab28af74a22a4aeb889c0317944  29.75   43.58\n",
       "272382  24f56b5e1849462093931b164eb803b5  29.75   22.64\n",
       "272384  b3a1272bc9904337b331bf348c3e8c17  29.75    1.59\n",
       "272385  68213b08d99a4ae1b0dcb72aebd9aa35  29.75    9.53\n",
       "272386  a00058cf10334a308c68e7631c529907  29.75    3.61\n",
       "272387  76ddbd6576844afe811f1a3c0fbb5bec  29.75    3.53\n",
       "\n",
       "[123957 rows x 3 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transcript:  segregate offer and transaction data \n",
    "\n",
    "# extract transactions from transcript data\n",
    "transaction_df = transcript[transcript.event=='transaction'].copy()\n",
    "transaction_df.drop(columns=['offer_id', 'event'], inplace=True)\n",
    "transaction_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transcript:  seggregate offer and transaction data \n",
    "\n",
    "# extract offer-related from transcript data\n",
    "offers_df = transcript[transcript.event!='transaction'].copy()\n",
    "\n",
    "# one-hot encode offer event\n",
    "offers_df['received'] = offers_df.event.apply(lambda x: 1 if x == 'offer received' else 0)\n",
    "offers_df['completed'] = offers_df.event.apply(lambda x: 1 if x == 'offer completed' else 0)\n",
    "offers_df['viewed'] = offers_df.event.apply(lambda x: 1 if x == 'offer viewed' else 0)\n",
    "\n",
    "offers_df.drop(columns=['event', 'amount'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "transaction        123957\n",
       "offer received      66501\n",
       "offer viewed        49860\n",
       "offer completed     32070\n",
       "Name: event, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript.event.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49860, 32070, 66501)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "offers_df.viewed.sum(), offers_df.completed.sum(), offers_df.received.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4b0da7e80e5945209a1fdddfe813dbe0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>c27e0d6ab72c455a8bb66d980963de60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3f207df678b143eea3cee63160fa8bed</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>d53717f5400c4e84affdaeda9dd926b3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>f806632c011441378d4646567f357a21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>d058f73bf8674a26a95227db098147b1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ebe7ef46ea6f4963a7dd49f501b26779</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1e9420836d554513ab90eba98552d0a9</td>\n",
       "      <td>0.00</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>868317b9be554cb18e50bc68484749a2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>f082d80f0aac47a99173ba8ef8fc1909</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>102e9454054946fda62242d2e176fdce</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>9f30b375d7bd4c62a884ffe7034e09ee</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6e014185620b49bd98749f728747572f</td>\n",
       "      <td>0.00</td>\n",
       "      <td>f19421c1d4aa40978ebb69ca19b0e20d</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>02c083884c7d45b39cc68e1314fec56c</td>\n",
       "      <td>0.00</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>c0d210398dee4a0895b24444a5fcd1d2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>57dd18ec5ddc46828afb81ec5977bef2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>be8a5d1981a2458d90b255ddc7e0d174</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5a8bc65990b245e5a138643cd4eb9837</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>a3b9b4bf115e4564933ea2e4bc936843</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>7584948ea6d04f94b8798624706261c2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>9c64321646bf40f6ad1bc98864eaa6b8</td>\n",
       "      <td>0.00</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>676506bad68e4161b9bbaffeb039626b</td>\n",
       "      <td>0.00</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>9fa9ae8f57894cc9a3b8a9bbe0fc1b2f</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>32fbf97e89274e8a935903f2ad095e1f</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>fe8264108d5b4f198453bbb1fa7ca6c9</td>\n",
       "      <td>0.00</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>b7a66e629b134079a9bc7120c7b9947b</td>\n",
       "      <td>0.00</td>\n",
       "      <td>f19421c1d4aa40978ebb69ca19b0e20d</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272187</th>\n",
       "      <td>30bd5034559c4890a78f4f668dd03fb0</td>\n",
       "      <td>29.75</td>\n",
       "      <td>3f207df678b143eea3cee63160fa8bed</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272209</th>\n",
       "      <td>d1a824d43588413981d90146cc781a13</td>\n",
       "      <td>29.75</td>\n",
       "      <td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272211</th>\n",
       "      <td>845c31e647b449479f30b36eac5c5856</td>\n",
       "      <td>29.75</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272215</th>\n",
       "      <td>b73c8d5fe3c84a79bb3432387f0f4fb5</td>\n",
       "      <td>29.75</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272219</th>\n",
       "      <td>087d0fcdbf294f7494a96d8d6a56cf41</td>\n",
       "      <td>29.75</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272228</th>\n",
       "      <td>a3fa9c5ae07348f0ad9c6942a17e063b</td>\n",
       "      <td>29.75</td>\n",
       "      <td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272236</th>\n",
       "      <td>557e2c8128334f00bbe020595885b9c4</td>\n",
       "      <td>29.75</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272244</th>\n",
       "      <td>2d868dda028f445b8c7f592d33077229</td>\n",
       "      <td>29.75</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272246</th>\n",
       "      <td>0dc85918181e4c3eb0e412d37fa8a552</td>\n",
       "      <td>29.75</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272260</th>\n",
       "      <td>345f9431137a49b3963221a55567c4a8</td>\n",
       "      <td>29.75</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272272</th>\n",
       "      <td>6f3fe50d1f26409f94489a85c766f79e</td>\n",
       "      <td>29.75</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272273</th>\n",
       "      <td>eb1ebe497d654c17b2fa99e40eb3d2d0</td>\n",
       "      <td>29.75</td>\n",
       "      <td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272275</th>\n",
       "      <td>7f0d9541dc284bb5b390c863644f04cd</td>\n",
       "      <td>29.75</td>\n",
       "      <td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272278</th>\n",
       "      <td>b13eaeb7a91e415a972c9ebc7f63ac57</td>\n",
       "      <td>29.75</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272281</th>\n",
       "      <td>8ea3ffcc740848f99c9679e3bc3c9c80</td>\n",
       "      <td>29.75</td>\n",
       "      <td>f19421c1d4aa40978ebb69ca19b0e20d</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272286</th>\n",
       "      <td>5a3f7fcb450a4cb693ab5267b4d49b40</td>\n",
       "      <td>29.75</td>\n",
       "      <td>5a8bc65990b245e5a138643cd4eb9837</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272289</th>\n",
       "      <td>8771f96bc7f947c09a8f78ee2e14a1a7</td>\n",
       "      <td>29.75</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272296</th>\n",
       "      <td>0355c6a5fdbc429ea5b05e8a9ecd2eae</td>\n",
       "      <td>29.75</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272299</th>\n",
       "      <td>ec1761735b034ae8874bc979f0cd5862</td>\n",
       "      <td>29.75</td>\n",
       "      <td>5a8bc65990b245e5a138643cd4eb9837</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272305</th>\n",
       "      <td>f74033a8c6834303bd1b3ef0c98d11d8</td>\n",
       "      <td>29.75</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272308</th>\n",
       "      <td>84ad352984b04b73b6dbff8d719de321</td>\n",
       "      <td>29.75</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272314</th>\n",
       "      <td>9b51e8797290403b90d09d864dec4b94</td>\n",
       "      <td>29.75</td>\n",
       "      <td>3f207df678b143eea3cee63160fa8bed</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272319</th>\n",
       "      <td>f3e801caeafe4899b3b989b586e74ac7</td>\n",
       "      <td>29.75</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272331</th>\n",
       "      <td>452cdae2c68e4732a4177d3929562690</td>\n",
       "      <td>29.75</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272336</th>\n",
       "      <td>0c027f5f34dd4b9eba0a25785c611273</td>\n",
       "      <td>29.75</td>\n",
       "      <td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272343</th>\n",
       "      <td>84fb57a7fe8045a8bf6236738ee73a0f</td>\n",
       "      <td>29.75</td>\n",
       "      <td>5a8bc65990b245e5a138643cd4eb9837</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272350</th>\n",
       "      <td>abc4359eb34e4e2ca2349da2ddf771b6</td>\n",
       "      <td>29.75</td>\n",
       "      <td>3f207df678b143eea3cee63160fa8bed</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272363</th>\n",
       "      <td>8dda575c2a1d44b9ac8e8b07b93d1f8e</td>\n",
       "      <td>29.75</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272365</th>\n",
       "      <td>8431c16f8e1d440880db371a68f82dd0</td>\n",
       "      <td>29.75</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272383</th>\n",
       "      <td>24f56b5e1849462093931b164eb803b5</td>\n",
       "      <td>29.75</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>148431 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             customer_id   time  \\\n",
       "0       78afa995795e4d85b5d9ceeca43f5fef   0.00   \n",
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       "...                                  ...    ...   \n",
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       "\n",
       "                                offer_id  received  completed  viewed  \n",
       "0       9b98b8c7a33c4b65b9aebfe6a799e6d9         1          0       0  \n",
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       "...                                  ...       ...        ...     ...  \n",
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       "272305  0b1e1539f2cc45b7b9fa7c272da2e1d7         0          1       0  \n",
       "272308  9b98b8c7a33c4b65b9aebfe6a799e6d9         0          1       0  \n",
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       "272319  0b1e1539f2cc45b7b9fa7c272da2e1d7         0          1       0  \n",
       "272331  fafdcd668e3743c1bb461111dcafc2a4         0          1       0  \n",
       "272336  2298d6c36e964ae4a3e7e9706d1fb8c2         0          1       0  \n",
       "272343  5a8bc65990b245e5a138643cd4eb9837         0          0       1  \n",
       "272350  3f207df678b143eea3cee63160fa8bed         0          0       1  \n",
       "272363  0b1e1539f2cc45b7b9fa7c272da2e1d7         0          0       1  \n",
       "272365  fafdcd668e3743c1bb461111dcafc2a4         0          1       0  \n",
       "272383  fafdcd668e3743c1bb461111dcafc2a4         0          1       0  \n",
       "\n",
       "[148431 rows x 6 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "offers_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Profile data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>became_member_on</th>\n",
       "      <th>gender</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>income</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>2017-07-15</td>\n",
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       "      <td>0610b486422d4921ae7d2bf64640c50b</td>\n",
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       "    <tr>\n",
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       "      <td>2017-05-09</td>\n",
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       "      <td>2018-04-26</td>\n",
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       "      <td>e2127556f4f64592b11af22de27a7932</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "      <td>2018-02-09</td>\n",
       "      <td>1</td>\n",
       "      <td>389bc3fa690240e798340f5a15918d5c</td>\n",
       "      <td>53000.0</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
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       "      <td>2017-11-11</td>\n",
       "      <td>1</td>\n",
       "      <td>2eeac8d8feae4a8cad5a6af0499a211d</td>\n",
       "      <td>51000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
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       "      <td>2017-09-11</td>\n",
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       "      <td>aa4862eba776480b8bb9c68455b8c2e1</td>\n",
       "      <td>57000.0</td>\n",
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       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
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       "      <td>2014-02-13</td>\n",
       "      <td>1</td>\n",
       "      <td>e12aeaf2d47d42479ea1c4ac3d8286c6</td>\n",
       "      <td>46000.0</td>\n",
       "      <td>2014</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>62.0</td>\n",
       "      <td>2016-02-11</td>\n",
       "      <td>0</td>\n",
       "      <td>31dda685af34476cad5bc968bdb01c53</td>\n",
       "      <td>71000.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>49.0</td>\n",
       "      <td>2014-11-13</td>\n",
       "      <td>1</td>\n",
       "      <td>62cf5e10845442329191fc246e7bcea3</td>\n",
       "      <td>52000.0</td>\n",
       "      <td>2014</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>57.0</td>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>6445de3b47274c759400cd68131d91b4</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>61.0</td>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>0</td>\n",
       "      <td>a448667f336b42c9a66fc5ffd5d73772</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>40.0</td>\n",
       "      <td>2016-05-04</td>\n",
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       "      <td>440cf1fd7580490c971d8c651ed962af</td>\n",
       "      <td>71000.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
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       "      <td>100000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
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       "      <td>71000.0</td>\n",
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       "      <td>6</td>\n",
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       "    <tr>\n",
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       "      <td>2013-08-11</td>\n",
       "      <td>1</td>\n",
       "      <td>f806632c011441378d4646567f357a21</td>\n",
       "      <td>69000.0</td>\n",
       "      <td>2013</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>56.0</td>\n",
       "      <td>2018-04-28</td>\n",
       "      <td>0</td>\n",
       "      <td>d058f73bf8674a26a95227db098147b1</td>\n",
       "      <td>88000.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>33.0</td>\n",
       "      <td>2017-09-26</td>\n",
       "      <td>0</td>\n",
       "      <td>c6c9884912c645429f3333f912b55f44</td>\n",
       "      <td>52000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>46.0</td>\n",
       "      <td>2017-09-11</td>\n",
       "      <td>0</td>\n",
       "      <td>7429a044884842d6862f516c38b1156f</td>\n",
       "      <td>59000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>59.0</td>\n",
       "      <td>2015-01-21</td>\n",
       "      <td>1</td>\n",
       "      <td>ebe7ef46ea6f4963a7dd49f501b26779</td>\n",
       "      <td>41000.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>67.0</td>\n",
       "      <td>2014-08-23</td>\n",
       "      <td>1</td>\n",
       "      <td>5917d13f6fa04286abdf95f4d5cefb1e</td>\n",
       "      <td>96000.0</td>\n",
       "      <td>2014</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>53.0</td>\n",
       "      <td>2017-09-16</td>\n",
       "      <td>0</td>\n",
       "      <td>d1ede868e29245ea91818a903fec04c6</td>\n",
       "      <td>52000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>22.0</td>\n",
       "      <td>2017-09-25</td>\n",
       "      <td>1</td>\n",
       "      <td>1e9420836d554513ab90eba98552d0a9</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>96.0</td>\n",
       "      <td>2017-11-17</td>\n",
       "      <td>0</td>\n",
       "      <td>868317b9be554cb18e50bc68484749a2</td>\n",
       "      <td>89000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>40.0</td>\n",
       "      <td>2016-07-09</td>\n",
       "      <td>1</td>\n",
       "      <td>f082d80f0aac47a99173ba8ef8fc1909</td>\n",
       "      <td>33000.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>69.0</td>\n",
       "      <td>2016-08-14</td>\n",
       "      <td>0</td>\n",
       "      <td>102e9454054946fda62242d2e176fdce</td>\n",
       "      <td>57000.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>56.0</td>\n",
       "      <td>2014-05-11</td>\n",
       "      <td>1</td>\n",
       "      <td>8dabe2142cdf4013a6f4786edf8ab00f</td>\n",
       "      <td>68000.0</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>26.0</td>\n",
       "      <td>2017-08-12</td>\n",
       "      <td>0</td>\n",
       "      <td>9f30b375d7bd4c62a884ffe7034e09ee</td>\n",
       "      <td>63000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>75.0</td>\n",
       "      <td>2015-11-12</td>\n",
       "      <td>1</td>\n",
       "      <td>6e014185620b49bd98749f728747572f</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>20.0</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>0</td>\n",
       "      <td>02c083884c7d45b39cc68e1314fec56c</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>45.0</td>\n",
       "      <td>2018-07-07</td>\n",
       "      <td>1</td>\n",
       "      <td>c0d210398dee4a0895b24444a5fcd1d2</td>\n",
       "      <td>33000.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>7</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16963</th>\n",
       "      <td>62.0</td>\n",
       "      <td>2017-01-12</td>\n",
       "      <td>0</td>\n",
       "      <td>8c3fbf6b89b248bb99b7a6ca628f401c</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16964</th>\n",
       "      <td>52.0</td>\n",
       "      <td>2018-04-08</td>\n",
       "      <td>1</td>\n",
       "      <td>68213b08d99a4ae1b0dcb72aebd9aa35</td>\n",
       "      <td>62000.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16965</th>\n",
       "      <td>52.0</td>\n",
       "      <td>2018-04-09</td>\n",
       "      <td>1</td>\n",
       "      <td>e340cbf00a914f0c958577c0af36ed8a</td>\n",
       "      <td>34000.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16966</th>\n",
       "      <td>54.0</td>\n",
       "      <td>2015-01-26</td>\n",
       "      <td>1</td>\n",
       "      <td>10316535e185400fb093c371d62254e3</td>\n",
       "      <td>51000.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16967</th>\n",
       "      <td>54.0</td>\n",
       "      <td>2015-06-26</td>\n",
       "      <td>1</td>\n",
       "      <td>cec1cdc6b77e41abbdce8903c5522e7e</td>\n",
       "      <td>53000.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16968</th>\n",
       "      <td>74.0</td>\n",
       "      <td>2017-07-25</td>\n",
       "      <td>1</td>\n",
       "      <td>2c6f50c1c07a444896e2a4e31d724f92</td>\n",
       "      <td>87000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16970</th>\n",
       "      <td>67.0</td>\n",
       "      <td>2015-11-07</td>\n",
       "      <td>1</td>\n",
       "      <td>a2e6029e17b6466187a40b66e333a73e</td>\n",
       "      <td>94000.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16971</th>\n",
       "      <td>52.0</td>\n",
       "      <td>2018-05-22</td>\n",
       "      <td>0</td>\n",
       "      <td>6e71b66784844d1ab8376ac8ab096d4b</td>\n",
       "      <td>75000.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16972</th>\n",
       "      <td>44.0</td>\n",
       "      <td>2017-01-19</td>\n",
       "      <td>0</td>\n",
       "      <td>e2fd53ed790240c586b3188f23542cca</td>\n",
       "      <td>51000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16973</th>\n",
       "      <td>30.0</td>\n",
       "      <td>2015-10-12</td>\n",
       "      <td>1</td>\n",
       "      <td>08eb126ad33f447ca3ad076482445c05</td>\n",
       "      <td>57000.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16974</th>\n",
       "      <td>59.0</td>\n",
       "      <td>2016-01-13</td>\n",
       "      <td>0</td>\n",
       "      <td>f88e91a11d8f405b9a47ad3741415b83</td>\n",
       "      <td>71000.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16975</th>\n",
       "      <td>61.0</td>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>0</td>\n",
       "      <td>1b4df4b48bf64727a4c445909aea1251</td>\n",
       "      <td>81000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16976</th>\n",
       "      <td>39.0</td>\n",
       "      <td>2016-09-21</td>\n",
       "      <td>0</td>\n",
       "      <td>122c0c2a3c2540848f8c3fdc37e97639</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16978</th>\n",
       "      <td>29.0</td>\n",
       "      <td>2017-02-20</td>\n",
       "      <td>0</td>\n",
       "      <td>54bead4179c44b71acc4e8398181d183</td>\n",
       "      <td>58000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16979</th>\n",
       "      <td>63.0</td>\n",
       "      <td>2013-09-22</td>\n",
       "      <td>0</td>\n",
       "      <td>a00058cf10334a308c68e7631c529907</td>\n",
       "      <td>52000.0</td>\n",
       "      <td>2013</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16981</th>\n",
       "      <td>84.0</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>1</td>\n",
       "      <td>1966fa40d2f84620b2b1b9b64f8e0209</td>\n",
       "      <td>93000.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16983</th>\n",
       "      <td>72.0</td>\n",
       "      <td>2015-04-04</td>\n",
       "      <td>0</td>\n",
       "      <td>ff737c250d2343729ade04c4f6eb1001</td>\n",
       "      <td>65000.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16984</th>\n",
       "      <td>75.0</td>\n",
       "      <td>2016-07-16</td>\n",
       "      <td>0</td>\n",
       "      <td>392d23b2c958493197f647bedfe4f418</td>\n",
       "      <td>78000.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16985</th>\n",
       "      <td>26.0</td>\n",
       "      <td>2018-06-19</td>\n",
       "      <td>1</td>\n",
       "      <td>97ee6e7a12fe4064b260fa48bdd0330f</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16986</th>\n",
       "      <td>59.0</td>\n",
       "      <td>2017-07-31</td>\n",
       "      <td>0</td>\n",
       "      <td>bf3069b178fe40d789dd027901bc406b</td>\n",
       "      <td>63000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16987</th>\n",
       "      <td>57.0</td>\n",
       "      <td>2016-07-09</td>\n",
       "      <td>1</td>\n",
       "      <td>76ddbd6576844afe811f1a3c0fbb5bec</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16988</th>\n",
       "      <td>64.0</td>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>f653cf2d8bba42d0a53c2937ee2e5893</td>\n",
       "      <td>51000.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16990</th>\n",
       "      <td>70.0</td>\n",
       "      <td>2016-03-10</td>\n",
       "      <td>0</td>\n",
       "      <td>79edb810789c447e8d212a324b44cc16</td>\n",
       "      <td>39000.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16992</th>\n",
       "      <td>21.0</td>\n",
       "      <td>2017-09-17</td>\n",
       "      <td>0</td>\n",
       "      <td>400d0536e8794cbb855b0d882d67cbda</td>\n",
       "      <td>72000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16993</th>\n",
       "      <td>60.0</td>\n",
       "      <td>2018-05-05</td>\n",
       "      <td>1</td>\n",
       "      <td>cb23b66c56f64b109d673d5e56574529</td>\n",
       "      <td>113000.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16995</th>\n",
       "      <td>45.0</td>\n",
       "      <td>2018-06-04</td>\n",
       "      <td>0</td>\n",
       "      <td>6d5f3a774f3d4714ab0c092238f3a1d7</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16996</th>\n",
       "      <td>61.0</td>\n",
       "      <td>2018-07-13</td>\n",
       "      <td>1</td>\n",
       "      <td>2cb4f97358b841b9a9773a7aa05a9d77</td>\n",
       "      <td>72000.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16997</th>\n",
       "      <td>49.0</td>\n",
       "      <td>2017-01-26</td>\n",
       "      <td>1</td>\n",
       "      <td>01d26f638c274aa0b965d24cefe3183f</td>\n",
       "      <td>73000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16998</th>\n",
       "      <td>83.0</td>\n",
       "      <td>2016-03-07</td>\n",
       "      <td>0</td>\n",
       "      <td>9dc1421481194dcd9400aec7c9ae6366</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16999</th>\n",
       "      <td>62.0</td>\n",
       "      <td>2017-07-22</td>\n",
       "      <td>0</td>\n",
       "      <td>e4052622e5ba45a8b96b59aba68cf068</td>\n",
       "      <td>82000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14825 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        age became_member_on  gender                       customer_id  \\\n",
       "1      55.0       2017-07-15       0  0610b486422d4921ae7d2bf64640c50b   \n",
       "3      75.0       2017-05-09       0  78afa995795e4d85b5d9ceeca43f5fef   \n",
       "5      68.0       2018-04-26       1  e2127556f4f64592b11af22de27a7932   \n",
       "8      65.0       2018-02-09       1  389bc3fa690240e798340f5a15918d5c   \n",
       "12     58.0       2017-11-11       1  2eeac8d8feae4a8cad5a6af0499a211d   \n",
       "13     61.0       2017-09-11       0  aa4862eba776480b8bb9c68455b8c2e1   \n",
       "14     26.0       2014-02-13       1  e12aeaf2d47d42479ea1c4ac3d8286c6   \n",
       "15     62.0       2016-02-11       0  31dda685af34476cad5bc968bdb01c53   \n",
       "16     49.0       2014-11-13       1  62cf5e10845442329191fc246e7bcea3   \n",
       "18     57.0       2017-12-31       1  6445de3b47274c759400cd68131d91b4   \n",
       "19     61.0       2018-05-01       0  a448667f336b42c9a66fc5ffd5d73772   \n",
       "20     40.0       2016-05-04       0  440cf1fd7580490c971d8c651ed962af   \n",
       "21     64.0       2017-09-09       1  4b0da7e80e5945209a1fdddfe813dbe0   \n",
       "22     78.0       2017-06-16       0  c27e0d6ab72c455a8bb66d980963de60   \n",
       "24     42.0       2013-08-11       1  f806632c011441378d4646567f357a21   \n",
       "25     56.0       2018-04-28       0  d058f73bf8674a26a95227db098147b1   \n",
       "27     33.0       2017-09-26       0  c6c9884912c645429f3333f912b55f44   \n",
       "28     46.0       2017-09-11       0  7429a044884842d6862f516c38b1156f   \n",
       "29     59.0       2015-01-21       1  ebe7ef46ea6f4963a7dd49f501b26779   \n",
       "30     67.0       2014-08-23       1  5917d13f6fa04286abdf95f4d5cefb1e   \n",
       "31     53.0       2017-09-16       0  d1ede868e29245ea91818a903fec04c6   \n",
       "32     22.0       2017-09-25       1  1e9420836d554513ab90eba98552d0a9   \n",
       "33     96.0       2017-11-17       0  868317b9be554cb18e50bc68484749a2   \n",
       "34     40.0       2016-07-09       1  f082d80f0aac47a99173ba8ef8fc1909   \n",
       "35     69.0       2016-08-14       0  102e9454054946fda62242d2e176fdce   \n",
       "37     56.0       2014-05-11       1  8dabe2142cdf4013a6f4786edf8ab00f   \n",
       "38     26.0       2017-08-12       0  9f30b375d7bd4c62a884ffe7034e09ee   \n",
       "40     75.0       2015-11-12       1  6e014185620b49bd98749f728747572f   \n",
       "41     20.0       2016-07-11       0  02c083884c7d45b39cc68e1314fec56c   \n",
       "42     45.0       2018-07-07       1  c0d210398dee4a0895b24444a5fcd1d2   \n",
       "...     ...              ...     ...                               ...   \n",
       "16963  62.0       2017-01-12       0  8c3fbf6b89b248bb99b7a6ca628f401c   \n",
       "16964  52.0       2018-04-08       1  68213b08d99a4ae1b0dcb72aebd9aa35   \n",
       "16965  52.0       2018-04-09       1  e340cbf00a914f0c958577c0af36ed8a   \n",
       "16966  54.0       2015-01-26       1  10316535e185400fb093c371d62254e3   \n",
       "16967  54.0       2015-06-26       1  cec1cdc6b77e41abbdce8903c5522e7e   \n",
       "16968  74.0       2017-07-25       1  2c6f50c1c07a444896e2a4e31d724f92   \n",
       "16970  67.0       2015-11-07       1  a2e6029e17b6466187a40b66e333a73e   \n",
       "16971  52.0       2018-05-22       0  6e71b66784844d1ab8376ac8ab096d4b   \n",
       "16972  44.0       2017-01-19       0  e2fd53ed790240c586b3188f23542cca   \n",
       "16973  30.0       2015-10-12       1  08eb126ad33f447ca3ad076482445c05   \n",
       "16974  59.0       2016-01-13       0  f88e91a11d8f405b9a47ad3741415b83   \n",
       "16975  61.0       2017-12-31       0  1b4df4b48bf64727a4c445909aea1251   \n",
       "16976  39.0       2016-09-21       0  122c0c2a3c2540848f8c3fdc37e97639   \n",
       "16978  29.0       2017-02-20       0  54bead4179c44b71acc4e8398181d183   \n",
       "16979  63.0       2013-09-22       0  a00058cf10334a308c68e7631c529907   \n",
       "16981  84.0       2016-06-29       1  1966fa40d2f84620b2b1b9b64f8e0209   \n",
       "16983  72.0       2015-04-04       0  ff737c250d2343729ade04c4f6eb1001   \n",
       "16984  75.0       2016-07-16       0  392d23b2c958493197f647bedfe4f418   \n",
       "16985  26.0       2018-06-19       1  97ee6e7a12fe4064b260fa48bdd0330f   \n",
       "16986  59.0       2017-07-31       0  bf3069b178fe40d789dd027901bc406b   \n",
       "16987  57.0       2016-07-09       1  76ddbd6576844afe811f1a3c0fbb5bec   \n",
       "16988  64.0       2018-01-04       1  f653cf2d8bba42d0a53c2937ee2e5893   \n",
       "16990  70.0       2016-03-10       0  79edb810789c447e8d212a324b44cc16   \n",
       "16992  21.0       2017-09-17       0  400d0536e8794cbb855b0d882d67cbda   \n",
       "16993  60.0       2018-05-05       1  cb23b66c56f64b109d673d5e56574529   \n",
       "16995  45.0       2018-06-04       0  6d5f3a774f3d4714ab0c092238f3a1d7   \n",
       "16996  61.0       2018-07-13       1  2cb4f97358b841b9a9773a7aa05a9d77   \n",
       "16997  49.0       2017-01-26       1  01d26f638c274aa0b965d24cefe3183f   \n",
       "16998  83.0       2016-03-07       0  9dc1421481194dcd9400aec7c9ae6366   \n",
       "16999  62.0       2017-07-22       0  e4052622e5ba45a8b96b59aba68cf068   \n",
       "\n",
       "         income  start_year  start_month  \n",
       "1      112000.0        2017            7  \n",
       "3      100000.0        2017            5  \n",
       "5       70000.0        2018            4  \n",
       "8       53000.0        2018            2  \n",
       "12      51000.0        2017           11  \n",
       "13      57000.0        2017            9  \n",
       "14      46000.0        2014            2  \n",
       "15      71000.0        2016            2  \n",
       "16      52000.0        2014           11  \n",
       "18      42000.0        2017           12  \n",
       "19      40000.0        2018            5  \n",
       "20      71000.0        2016            5  \n",
       "21     100000.0        2017            9  \n",
       "22      71000.0        2017            6  \n",
       "24      69000.0        2013            8  \n",
       "25      88000.0        2018            4  \n",
       "27      52000.0        2017            9  \n",
       "28      59000.0        2017            9  \n",
       "29      41000.0        2015            1  \n",
       "30      96000.0        2014            8  \n",
       "31      52000.0        2017            9  \n",
       "32      70000.0        2017            9  \n",
       "33      89000.0        2017           11  \n",
       "34      33000.0        2016            7  \n",
       "35      57000.0        2016            8  \n",
       "37      68000.0        2014            5  \n",
       "38      63000.0        2017            8  \n",
       "40      40000.0        2015           11  \n",
       "41      30000.0        2016            7  \n",
       "42      33000.0        2018            7  \n",
       "...         ...         ...          ...  \n",
       "16963   80000.0        2017            1  \n",
       "16964   62000.0        2018            4  \n",
       "16965   34000.0        2018            4  \n",
       "16966   51000.0        2015            1  \n",
       "16967   53000.0        2015            6  \n",
       "16968   87000.0        2017            7  \n",
       "16970   94000.0        2015           11  \n",
       "16971   75000.0        2018            5  \n",
       "16972   51000.0        2017            1  \n",
       "16973   57000.0        2015           10  \n",
       "16974   71000.0        2016            1  \n",
       "16975   81000.0        2017           12  \n",
       "16976   54000.0        2016            9  \n",
       "16978   58000.0        2017            2  \n",
       "16979   52000.0        2013            9  \n",
       "16981   93000.0        2016            6  \n",
       "16983   65000.0        2015            4  \n",
       "16984   78000.0        2016            7  \n",
       "16985   55000.0        2018            6  \n",
       "16986   63000.0        2017            7  \n",
       "16987   40000.0        2016            7  \n",
       "16988   51000.0        2018            1  \n",
       "16990   39000.0        2016            3  \n",
       "16992   72000.0        2017            9  \n",
       "16993  113000.0        2018            5  \n",
       "16995   54000.0        2018            6  \n",
       "16996   72000.0        2018            7  \n",
       "16997   73000.0        2017            1  \n",
       "16998   50000.0        2016            3  \n",
       "16999   82000.0        2017            7  \n",
       "\n",
       "[14825 rows x 7 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check for any missing values in each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                 0\n",
       "became_member_on    0\n",
       "gender              0\n",
       "customer_id         0\n",
       "income              0\n",
       "start_year          0\n",
       "start_month         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compute gender distribution in customer's data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    8484\n",
       "0    6341\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile.gender.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stats of customer age and income attributes when income is specified"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>gender</th>\n",
       "      <th>income</th>\n",
       "      <th>start_year</th>\n",
       "      <th>start_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14825.000000</td>\n",
       "      <td>14825.000000</td>\n",
       "      <td>14825.000000</td>\n",
       "      <td>14825.000000</td>\n",
       "      <td>14825.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.393524</td>\n",
       "      <td>0.572277</td>\n",
       "      <td>65404.991568</td>\n",
       "      <td>2016.620169</td>\n",
       "      <td>6.695582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17.383705</td>\n",
       "      <td>0.494765</td>\n",
       "      <td>21598.299410</td>\n",
       "      <td>1.198245</td>\n",
       "      <td>3.488853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>42.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>49000.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>64000.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>66.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>80000.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>101.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>120000.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age        gender         income    start_year   start_month\n",
       "count  14825.000000  14825.000000   14825.000000  14825.000000  14825.000000\n",
       "mean      54.393524      0.572277   65404.991568   2016.620169      6.695582\n",
       "std       17.383705      0.494765   21598.299410      1.198245      3.488853\n",
       "min       18.000000      0.000000   30000.000000   2013.000000      1.000000\n",
       "25%       42.000000      0.000000   49000.000000   2016.000000      4.000000\n",
       "50%       55.000000      1.000000   64000.000000   2017.000000      7.000000\n",
       "75%       66.000000      1.000000   80000.000000   2017.000000     10.000000\n",
       "max      101.000000      1.000000  120000.000000   2018.000000     12.000000"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile[profile['income'].notnull()].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compute what percent of customers vs which year enrolled as a member"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017    37.767285\n",
       "2018    24.748735\n",
       "2016    20.397976\n",
       "2015    10.772344\n",
       "2014     4.465430\n",
       "2013     1.848229\n",
       "Name: start_year, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_count = profile['start_year'].value_counts()\n",
    "\n",
    "year_count *= 100 / year_count.sum()\n",
    "year_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compute what percent of customers vs which month enrolled as a member"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     1327.0\n",
       "2     1042.0\n",
       "3     1145.0\n",
       "4     1145.0\n",
       "5     1152.0\n",
       "6     1105.0\n",
       "7     1189.0\n",
       "8     1395.0\n",
       "9     1299.0\n",
       "10    1386.0\n",
       "11    1268.0\n",
       "12    1372.0\n",
       "Name: became_member_on, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "month_count = profile['became_member_on'].apply(lambda date: date.month).value_counts()\n",
    "\n",
    "month_count *= 100 / year_count.sum()\n",
    "month_count.sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Profile General Distribution — Age, Income, Members"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to be able to draw three subplots in a row\n",
    "fig, ax = plt.subplots(figsize=(15, 4), nrows=1, ncols=3)\n",
    "\n",
    "# plot a age distribution in data\n",
    "plt.sca(ax[0])\n",
    "plt.hist(profile['age'])\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Age Distribution')\n",
    "\n",
    "# plot a income distribution in data\n",
    "plt.sca(ax[1])\n",
    "plt.hist(profile['income'] * 1E-3 )\n",
    "plt.xlabel('Income [10K]')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Income Distribution');\n",
    "\n",
    "# plot a start_year distribution\n",
    "plt.sca(ax[2])\n",
    "plt.hist(profile['start_year'])\n",
    "plt.xlabel('start year')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Membership distribution in terms of joining year');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Age distribution plot depicts that the median age of a customer is 60 and most of our customers belong to age range between 40 to 70. Income distribution plot shows that the number of customers whose average salary is less than 70K is high than the other side considering 70K to be median of the income distribution. Membership distribution has interesting results - 2017 has the highest registered customers than any starting from 2013. The plot also shows that there is an increasing trend in the number of registrations except for 2017"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Income distribution as a function a gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extract male and female customers\n",
    "male_customers = profile[profile['gender'] == 0]\n",
    "female_customers = profile[profile['gender'] == 1]\n",
    "\n",
    "# to be able to draw two subplots in a row\n",
    "fig, ax = plt.subplots(figsize=(10, 4), nrows=1, ncols=2, sharex=True, sharey=True)\n",
    "\n",
    "# plot a male customers income distribution\n",
    "plt.sca(ax[0])\n",
    "sns.distplot(male_customers['income'] * 1E-3)\n",
    "plt.xlabel('Income [10K]')\n",
    "plt.ylabel('Income')\n",
    "plt.title('Male Customer Income')\n",
    "\n",
    "# plot a female customers income distribution\n",
    "plt.sca(ax[1])\n",
    "sns.distplot(female_customers['income'] * 1E-3)\n",
    "plt.xlabel('Income [10K]')\n",
    "plt.ylabel('Income')\n",
    "plt.title('Female Customer Income');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- plots conclude that minimum and maximum income for both male and female are approximately same but the count of male customers in low-income level is slightly higher than that of female customers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Age distribution as a function of gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# groupby start_year and gender to plot a graph\n",
    "membership_date = profile.groupby(['start_year', 'gender']).size()\n",
    "membership_date = membership_date.reset_index()\n",
    "membership_date.columns = ['start_year', 'gender', 'count']\n",
    "\n",
    "# plot a bar graph for age distribution as a function of gender in membership program\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.barplot(x='start_year', y='count', hue='gender', data=membership_date)\n",
    "plt.xlabel('Membership Start Year')\n",
    "plt.ylabel('Count');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results suggest that most customers joined the Starbucks rewards program in 2017 followed by 2018. Plots also depict that there are more male customers joining the program every year."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Transaction data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compute the event type distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "transaction        123957\n",
       "offer received      66501\n",
       "offer viewed        49860\n",
       "offer completed     32070\n",
       "Name: event, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "event_counts = transcript['event'].value_counts()\n",
    "event_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45.507511344112075, 54.492488655887925)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_transactions = event_counts.sum()\n",
    "\n",
    "transactions_perc = 100 * event_counts[0] / total_transactions\n",
    "offers_perc = 100 * event_counts[1:].sum() / total_transactions\n",
    "\n",
    "(transactions_perc, offers_perc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine offer, customer and transactions data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://progressbar-2.readthedocs.io/en/latest/#introduction\n",
    "\n",
    "def combine_data(profile, portfolio, offers_df, transaction_df):\n",
    "    data = []\n",
    "    customer_ids = offers_df['customer_id'].unique()\n",
    "    \n",
    "    widgets=[\n",
    "        ' [', progressbar.Timer(), '] ',\n",
    "        progressbar.Bar(),\n",
    "        ' (', progressbar.ETA(), ') ',\n",
    "    ]\n",
    "    \n",
    "    # loop through all customer ids in offers_df\n",
    "    for ind in progressbar.progressbar(range(len(customer_ids)), widgets=widgets):\n",
    "        \n",
    "        # get customer id from the list\n",
    "        cust_id = customer_ids[ind]\n",
    "        \n",
    "        # extract customer profile from profile data\n",
    "        customer = profile[profile['customer_id']==cust_id]\n",
    "        \n",
    "        # extract offers associated with the customer from offers_df\n",
    "        cust_offer_data = offers_df[offers_df['customer_id']==cust_id]\n",
    "        \n",
    "        # extract transactions associated with the customer from transactions_df\n",
    "        cust_transaction_data = transaction_df[transaction_df['customer_id']==cust_id]\n",
    "        \n",
    "        # extract received, completed, viewed offer data from customer offers\n",
    "        offer_received_data = cust_offer_data[cust_offer_data['received'] == 1]\n",
    "        offer_completed_data = cust_offer_data[cust_offer_data['completed'] == 1]\n",
    "        offer_viewed_data = cust_offer_data[cust_offer_data['viewed'] == 1]\n",
    "        \n",
    "        rows = []\n",
    "        # loop through each received offer\n",
    "        for i in range(offer_received_data.shape[0]):\n",
    "            \n",
    "            # fetch an offer id \n",
    "            offer_id = offer_received_data.iloc[i]['offer_id']\n",
    "            \n",
    "            # extract offer row from portfolio\n",
    "            offer_row = portfolio.loc[portfolio['offer_id'] == offer_id]\n",
    "            \n",
    "            # extract duration days of an offer from offer row\n",
    "            duration_days = offer_row['duration'].values[0]\n",
    "            \n",
    "            # initialize start and end time of an offer\n",
    "            start_time = offer_received_data.iloc[i]['time']\n",
    "            end_time = start_time + duration_days\n",
    "        \n",
    "            # seggregate offers completed within end date\n",
    "            off_completed_withintime = np.logical_and(\n",
    "                offer_completed_data['time'] >= start_time, offer_completed_data['time'] <= end_time)\n",
    "            \n",
    "            # seggregate offers viewed within end date\n",
    "            off_viewed_withintime = np.logical_and(\n",
    "                offer_viewed_data['time'] >= start_time, offer_viewed_data['time'] <=end_time)\n",
    "\n",
    "            # flag offer_successful to 1 if an offer is viewed and completed within end time else to 0\n",
    "            offer_successful = off_completed_withintime.sum() > 0 and off_viewed_withintime.sum() > 0\n",
    "            \n",
    "            # extract transactions occured within time\n",
    "            transaction_withintime = np.logical_and(\n",
    "                cust_transaction_data['time'] >= start_time, cust_transaction_data['time'] <= end_time)\n",
    "        \n",
    "            transaction_data = cust_transaction_data[transaction_withintime]\n",
    "            \n",
    "            # total amount spent by a customer from given offers\n",
    "            transaction_total_amount = transaction_data['amount'].sum()\n",
    "            \n",
    "            row = {\n",
    "                'offer_id': offer_id,\n",
    "                'customer_id': cust_id,\n",
    "                'time': start_time,\n",
    "                'total_amount': transaction_total_amount,\n",
    "                'offer_successful': int(offer_successful),\n",
    "            }\n",
    "                \n",
    "            row.update(offer_row.iloc[0,0:].to_dict())\n",
    "\n",
    "            row.update(customer.iloc[0,:].to_dict())\n",
    "\n",
    "            rows.append(row)\n",
    "        \n",
    "        data.extend(rows)\n",
    "    \n",
    "    data = pd.DataFrame(data)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " [Elapsed Time: 0:13:14] |##################################| (Time:  0:13:14) \n"
     ]
    }
   ],
   "source": [
    "data = combine_data(profile, portfolio, offers_df, transaction_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    35136\n",
       "1    31365\n",
       "Name: offer_successful, dtype: int64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.offer_successful.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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></th>\n",
       "      <th>time</th>\n",
       "      <th>offer_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>offer_successful</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>duration</th>\n",
       "      <th>reward</th>\n",
       "      <th>bogo</th>\n",
       "      <th>discount</th>\n",
       "      <th>...</th>\n",
       "      <th>email</th>\n",
       "      <th>mobile</th>\n",
       "      <th>social</th>\n",
       "      <th>web</th>\n",
       "      <th>became_member_on</th>\n",
       "      <th>gender</th>\n",
       "      <th>income</th>\n",
       "      <th>age</th>\n",
       "      <th>start_year</th>\n",
       "      <th>start_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>37.67</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-05-09</td>\n",
       "      <td>0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.0</td>\n",
       "      <td>5a8bc65990b245e5a138643cd4eb9837</td>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>49.39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-05-09</td>\n",
       "      <td>0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.0</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>48.28</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-05-09</td>\n",
       "      <td>0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21.0</td>\n",
       "      <td>f19421c1d4aa40978ebb69ca19b0e20d</td>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>48.28</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-05-09</td>\n",
       "      <td>0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>e2127556f4f64592b11af22de27a7932</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-04-26</td>\n",
       "      <td>1</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14.0</td>\n",
       "      <td>3f207df678b143eea3cee63160fa8bed</td>\n",
       "      <td>e2127556f4f64592b11af22de27a7932</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-04-26</td>\n",
       "      <td>1</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>17.0</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>e2127556f4f64592b11af22de27a7932</td>\n",
       "      <td>18.42</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-04-26</td>\n",
       "      <td>1</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>21.0</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>e2127556f4f64592b11af22de27a7932</td>\n",
       "      <td>18.42</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-04-26</td>\n",
       "      <td>1</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>f19421c1d4aa40978ebb69ca19b0e20d</td>\n",
       "      <td>389bc3fa690240e798340f5a15918d5c</td>\n",
       "      <td>9.54</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-02-09</td>\n",
       "      <td>1</td>\n",
       "      <td>53000.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.0</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>389bc3fa690240e798340f5a15918d5c</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-02-09</td>\n",
       "      <td>1</td>\n",
       "      <td>53000.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14.0</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>389bc3fa690240e798340f5a15918d5c</td>\n",
       "      <td>15.63</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-02-09</td>\n",
       "      <td>1</td>\n",
       "      <td>53000.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>17.0</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>389bc3fa690240e798340f5a15918d5c</td>\n",
       "      <td>15.63</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-02-09</td>\n",
       "      <td>1</td>\n",
       "      <td>53000.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>21.0</td>\n",
       "      <td>f19421c1d4aa40978ebb69ca19b0e20d</td>\n",
       "      <td>389bc3fa690240e798340f5a15918d5c</td>\n",
       "      <td>11.26</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-02-09</td>\n",
       "      <td>1</td>\n",
       "      <td>53000.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>24.0</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>389bc3fa690240e798340f5a15918d5c</td>\n",
       "      <td>11.26</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-02-09</td>\n",
       "      <td>1</td>\n",
       "      <td>53000.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3f207df678b143eea3cee63160fa8bed</td>\n",
       "      <td>2eeac8d8feae4a8cad5a6af0499a211d</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-11-11</td>\n",
       "      <td>1</td>\n",
       "      <td>51000.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7.0</td>\n",
       "      <td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>\n",
       "      <td>2eeac8d8feae4a8cad5a6af0499a211d</td>\n",
       "      <td>2.32</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-11-11</td>\n",
       "      <td>1</td>\n",
       "      <td>51000.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>14.0</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>2eeac8d8feae4a8cad5a6af0499a211d</td>\n",
       "      <td>13.30</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-11-11</td>\n",
       "      <td>1</td>\n",
       "      <td>51000.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>aa4862eba776480b8bb9c68455b8c2e1</td>\n",
       "      <td>12.33</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-09-11</td>\n",
       "      <td>0</td>\n",
       "      <td>57000.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>7.0</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "      <td>aa4862eba776480b8bb9c68455b8c2e1</td>\n",
       "      <td>12.33</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-09-11</td>\n",
       "      <td>0</td>\n",
       "      <td>57000.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>14.0</td>\n",
       "      <td>5a8bc65990b245e5a138643cd4eb9837</td>\n",
       "      <td>aa4862eba776480b8bb9c68455b8c2e1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-09-11</td>\n",
       "      <td>0</td>\n",
       "      <td>57000.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>17.0</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>aa4862eba776480b8bb9c68455b8c2e1</td>\n",
       "      <td>60.66</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-09-11</td>\n",
       "      <td>0</td>\n",
       "      <td>57000.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>24.0</td>\n",
       "      <td>f19421c1d4aa40978ebb69ca19b0e20d</td>\n",
       "      <td>aa4862eba776480b8bb9c68455b8c2e1</td>\n",
       "      <td>12.56</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-09-11</td>\n",
       "      <td>0</td>\n",
       "      <td>57000.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>31dda685af34476cad5bc968bdb01c53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-02-11</td>\n",
       "      <td>0</td>\n",
       "      <td>71000.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>7.0</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>31dda685af34476cad5bc968bdb01c53</td>\n",
       "      <td>69.50</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-02-11</td>\n",
       "      <td>0</td>\n",
       "      <td>71000.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>14.0</td>\n",
       "      <td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>\n",
       "      <td>31dda685af34476cad5bc968bdb01c53</td>\n",
       "      <td>50.76</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-02-11</td>\n",
       "      <td>0</td>\n",
       "      <td>71000.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>17.0</td>\n",
       "      <td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>\n",
       "      <td>31dda685af34476cad5bc968bdb01c53</td>\n",
       "      <td>90.75</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-02-11</td>\n",
       "      <td>0</td>\n",
       "      <td>71000.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>21.0</td>\n",
       "      <td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>\n",
       "      <td>31dda685af34476cad5bc968bdb01c53</td>\n",
       "      <td>52.56</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-02-11</td>\n",
       "      <td>0</td>\n",
       "      <td>71000.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>24.0</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>31dda685af34476cad5bc968bdb01c53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-02-11</td>\n",
       "      <td>0</td>\n",
       "      <td>71000.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.0</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>4b0da7e80e5945209a1fdddfe813dbe0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-09-09</td>\n",
       "      <td>1</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>4b0da7e80e5945209a1fdddfe813dbe0</td>\n",
       "      <td>109.28</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-09-09</td>\n",
       "      <td>1</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66471</th>\n",
       "      <td>21.0</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "      <td>8578196a074a4f328976e334fa9383a3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-06-10</td>\n",
       "      <td>1</td>\n",
       "      <td>58000.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66472</th>\n",
       "      <td>24.0</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>8578196a074a4f328976e334fa9383a3</td>\n",
       "      <td>4.62</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-06-10</td>\n",
       "      <td>1</td>\n",
       "      <td>58000.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66473</th>\n",
       "      <td>21.0</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>1b1ca1a1ece440c7aac02b9f7ccf2f5a</td>\n",
       "      <td>66.84</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2015-09-05</td>\n",
       "      <td>1</td>\n",
       "      <td>98000.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66474</th>\n",
       "      <td>24.0</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>1b1ca1a1ece440c7aac02b9f7ccf2f5a</td>\n",
       "      <td>66.84</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2015-09-05</td>\n",
       "      <td>1</td>\n",
       "      <td>98000.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66475</th>\n",
       "      <td>21.0</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>d957b9bd21fd4cacb39aab4b2834704b</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-04-18</td>\n",
       "      <td>0</td>\n",
       "      <td>84000.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66476</th>\n",
       "      <td>21.0</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>d4b620d3f4be4a7187245c6b571d0f91</td>\n",
       "      <td>44.56</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-03-31</td>\n",
       "      <td>0</td>\n",
       "      <td>81000.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66477</th>\n",
       "      <td>24.0</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "      <td>d4b620d3f4be4a7187245c6b571d0f91</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-03-31</td>\n",
       "      <td>0</td>\n",
       "      <td>81000.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66478</th>\n",
       "      <td>21.0</td>\n",
       "      <td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>\n",
       "      <td>c4e2af52bdc8414289e7b6c9d3d00dda</td>\n",
       "      <td>108.52</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-05-19</td>\n",
       "      <td>1</td>\n",
       "      <td>93000.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66479</th>\n",
       "      <td>24.0</td>\n",
       "      <td>f19421c1d4aa40978ebb69ca19b0e20d</td>\n",
       "      <td>c4e2af52bdc8414289e7b6c9d3d00dda</td>\n",
       "      <td>108.75</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-05-19</td>\n",
       "      <td>1</td>\n",
       "      <td>93000.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66480</th>\n",
       "      <td>21.0</td>\n",
       "      <td>3f207df678b143eea3cee63160fa8bed</td>\n",
       "      <td>9fcbff4f8d7241faa4ab8a9d19c8a812</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-10-13</td>\n",
       "      <td>1</td>\n",
       "      <td>94000.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66481</th>\n",
       "      <td>24.0</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "      <td>9fcbff4f8d7241faa4ab8a9d19c8a812</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-10-13</td>\n",
       "      <td>1</td>\n",
       "      <td>94000.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66482</th>\n",
       "      <td>21.0</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>ae4e5d8d13d74a2eba8ed2d049c1a0c1</td>\n",
       "      <td>6.71</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-10-15</td>\n",
       "      <td>0</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66483</th>\n",
       "      <td>24.0</td>\n",
       "      <td>3f207df678b143eea3cee63160fa8bed</td>\n",
       "      <td>ae4e5d8d13d74a2eba8ed2d049c1a0c1</td>\n",
       "      <td>5.51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-10-15</td>\n",
       "      <td>0</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66484</th>\n",
       "      <td>21.0</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>3f3494f3234e4206a279662bef81fe85</td>\n",
       "      <td>13.35</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-07-15</td>\n",
       "      <td>0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66485</th>\n",
       "      <td>24.0</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "      <td>3f3494f3234e4206a279662bef81fe85</td>\n",
       "      <td>15.83</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2014-07-15</td>\n",
       "      <td>0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66486</th>\n",
       "      <td>21.0</td>\n",
       "      <td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>\n",
       "      <td>2f4973b4196e40db82756cbb0b7f0884</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-08-07</td>\n",
       "      <td>0</td>\n",
       "      <td>49000.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66487</th>\n",
       "      <td>24.0</td>\n",
       "      <td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>\n",
       "      <td>2f4973b4196e40db82756cbb0b7f0884</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-08-07</td>\n",
       "      <td>0</td>\n",
       "      <td>49000.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66488</th>\n",
       "      <td>24.0</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>1eed89c11cc749a4bb93d8cef032821d</td>\n",
       "      <td>681.87</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-07-09</td>\n",
       "      <td>1</td>\n",
       "      <td>74000.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66489</th>\n",
       "      <td>24.0</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>97b2476046874de3975e8feee95a1f98</td>\n",
       "      <td>46.33</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-04-06</td>\n",
       "      <td>1</td>\n",
       "      <td>73000.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66490</th>\n",
       "      <td>24.0</td>\n",
       "      <td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>\n",
       "      <td>c297c0a707724e868c3212f721a20457</td>\n",
       "      <td>45.44</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-08-08</td>\n",
       "      <td>0</td>\n",
       "      <td>68000.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66491</th>\n",
       "      <td>24.0</td>\n",
       "      <td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>\n",
       "      <td>14b3fa95de4f404a8c46709ef16ec58f</td>\n",
       "      <td>45.84</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-09-18</td>\n",
       "      <td>0</td>\n",
       "      <td>61000.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66492</th>\n",
       "      <td>24.0</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>9acbb111ba2f452cbbafabaf49138377</td>\n",
       "      <td>1.96</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2014-07-20</td>\n",
       "      <td>1</td>\n",
       "      <td>44000.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66493</th>\n",
       "      <td>24.0</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>fde9c2ca1c184c3eab2c7161c5f6a6c7</td>\n",
       "      <td>57.86</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-08-13</td>\n",
       "      <td>1</td>\n",
       "      <td>52000.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2013</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66494</th>\n",
       "      <td>24.0</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>297a08c1d05b405496e2c42bf2ea21d6</td>\n",
       "      <td>45.44</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-09-21</td>\n",
       "      <td>1</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66495</th>\n",
       "      <td>24.0</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "      <td>3045af4e98794a04a5542d3eac939b1f</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-10-20</td>\n",
       "      <td>0</td>\n",
       "      <td>78000.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66496</th>\n",
       "      <td>24.0</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>3cc622f76e464bc1809a71e871579d6c</td>\n",
       "      <td>34.04</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-08-20</td>\n",
       "      <td>1</td>\n",
       "      <td>44000.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66497</th>\n",
       "      <td>24.0</td>\n",
       "      <td>5a8bc65990b245e5a138643cd4eb9837</td>\n",
       "      <td>01fe5ec668f241608eb2f7ec374cb1b7</td>\n",
       "      <td>19.92</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-08-02</td>\n",
       "      <td>1</td>\n",
       "      <td>64000.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66498</th>\n",
       "      <td>24.0</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>d825b707198046e39baa4fdcfa11d621</td>\n",
       "      <td>28.42</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-04-23</td>\n",
       "      <td>1</td>\n",
       "      <td>73000.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66499</th>\n",
       "      <td>24.0</td>\n",
       "      <td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>\n",
       "      <td>c207a8a8ef5c47ee970ca45f859e07eb</td>\n",
       "      <td>75.84</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-12-23</td>\n",
       "      <td>0</td>\n",
       "      <td>86000.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66500</th>\n",
       "      <td>24.0</td>\n",
       "      <td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>\n",
       "      <td>9a3f45cf29ef428b932492c7a5d6ac52</td>\n",
       "      <td>14.25</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-10-19</td>\n",
       "      <td>1</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>66501 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       time                          offer_id  \\\n",
       "0       0.0  9b98b8c7a33c4b65b9aebfe6a799e6d9   \n",
       "1       7.0  5a8bc65990b245e5a138643cd4eb9837   \n",
       "2      17.0  ae264e3637204a6fb9bb56bc8210ddfd   \n",
       "3      21.0  f19421c1d4aa40978ebb69ca19b0e20d   \n",
       "4       0.0  2906b810c7d4411798c6938adc9daaa5   \n",
       "5      14.0  3f207df678b143eea3cee63160fa8bed   \n",
       "6      17.0  9b98b8c7a33c4b65b9aebfe6a799e6d9   \n",
       "7      21.0  fafdcd668e3743c1bb461111dcafc2a4   \n",
       "8       0.0  f19421c1d4aa40978ebb69ca19b0e20d   \n",
       "9       7.0  9b98b8c7a33c4b65b9aebfe6a799e6d9   \n",
       "10     14.0  2906b810c7d4411798c6938adc9daaa5   \n",
       "11     17.0  9b98b8c7a33c4b65b9aebfe6a799e6d9   \n",
       "12     21.0  f19421c1d4aa40978ebb69ca19b0e20d   \n",
       "13     24.0  fafdcd668e3743c1bb461111dcafc2a4   \n",
       "14      0.0  3f207df678b143eea3cee63160fa8bed   \n",
       "15      7.0  2298d6c36e964ae4a3e7e9706d1fb8c2   \n",
       "16     14.0  fafdcd668e3743c1bb461111dcafc2a4   \n",
       "17      0.0  0b1e1539f2cc45b7b9fa7c272da2e1d7   \n",
       "18      7.0  4d5c57ea9a6940dd891ad53e9dbe8da0   \n",
       "19     14.0  5a8bc65990b245e5a138643cd4eb9837   \n",
       "20     17.0  ae264e3637204a6fb9bb56bc8210ddfd   \n",
       "21     24.0  f19421c1d4aa40978ebb69ca19b0e20d   \n",
       "22      0.0  0b1e1539f2cc45b7b9fa7c272da2e1d7   \n",
       "23      7.0  fafdcd668e3743c1bb461111dcafc2a4   \n",
       "24     14.0  2298d6c36e964ae4a3e7e9706d1fb8c2   \n",
       "25     17.0  2298d6c36e964ae4a3e7e9706d1fb8c2   \n",
       "26     21.0  2298d6c36e964ae4a3e7e9706d1fb8c2   \n",
       "27     24.0  fafdcd668e3743c1bb461111dcafc2a4   \n",
       "28      0.0  ae264e3637204a6fb9bb56bc8210ddfd   \n",
       "29      7.0  0b1e1539f2cc45b7b9fa7c272da2e1d7   \n",
       "...     ...                               ...   \n",
       "66471  21.0  4d5c57ea9a6940dd891ad53e9dbe8da0   \n",
       "66472  24.0  2906b810c7d4411798c6938adc9daaa5   \n",
       "66473  21.0  fafdcd668e3743c1bb461111dcafc2a4   \n",
       "66474  24.0  2906b810c7d4411798c6938adc9daaa5   \n",
       "66475  21.0  ae264e3637204a6fb9bb56bc8210ddfd   \n",
       "66476  21.0  0b1e1539f2cc45b7b9fa7c272da2e1d7   \n",
       "66477  24.0  4d5c57ea9a6940dd891ad53e9dbe8da0   \n",
       "66478  21.0  2298d6c36e964ae4a3e7e9706d1fb8c2   \n",
       "66479  24.0  f19421c1d4aa40978ebb69ca19b0e20d   \n",
       "66480  21.0  3f207df678b143eea3cee63160fa8bed   \n",
       "66481  24.0  4d5c57ea9a6940dd891ad53e9dbe8da0   \n",
       "66482  21.0  ae264e3637204a6fb9bb56bc8210ddfd   \n",
       "66483  24.0  3f207df678b143eea3cee63160fa8bed   \n",
       "66484  21.0  ae264e3637204a6fb9bb56bc8210ddfd   \n",
       "66485  24.0  4d5c57ea9a6940dd891ad53e9dbe8da0   \n",
       "66486  21.0  2298d6c36e964ae4a3e7e9706d1fb8c2   \n",
       "66487  24.0  2298d6c36e964ae4a3e7e9706d1fb8c2   \n",
       "66488  24.0  fafdcd668e3743c1bb461111dcafc2a4   \n",
       "66489  24.0  0b1e1539f2cc45b7b9fa7c272da2e1d7   \n",
       "66490  24.0  2298d6c36e964ae4a3e7e9706d1fb8c2   \n",
       "66491  24.0  2298d6c36e964ae4a3e7e9706d1fb8c2   \n",
       "66492  24.0  0b1e1539f2cc45b7b9fa7c272da2e1d7   \n",
       "66493  24.0  9b98b8c7a33c4b65b9aebfe6a799e6d9   \n",
       "66494  24.0  9b98b8c7a33c4b65b9aebfe6a799e6d9   \n",
       "66495  24.0  4d5c57ea9a6940dd891ad53e9dbe8da0   \n",
       "66496  24.0  2906b810c7d4411798c6938adc9daaa5   \n",
       "66497  24.0  5a8bc65990b245e5a138643cd4eb9837   \n",
       "66498  24.0  9b98b8c7a33c4b65b9aebfe6a799e6d9   \n",
       "66499  24.0  2298d6c36e964ae4a3e7e9706d1fb8c2   \n",
       "66500  24.0  2298d6c36e964ae4a3e7e9706d1fb8c2   \n",
       "\n",
       "                            customer_id  total_amount  offer_successful  \\\n",
       "0      78afa995795e4d85b5d9ceeca43f5fef         37.67                 1   \n",
       "1      78afa995795e4d85b5d9ceeca43f5fef         49.39                 0   \n",
       "2      78afa995795e4d85b5d9ceeca43f5fef         48.28                 1   \n",
       "3      78afa995795e4d85b5d9ceeca43f5fef         48.28                 1   \n",
       "4      e2127556f4f64592b11af22de27a7932          0.00                 0   \n",
       "5      e2127556f4f64592b11af22de27a7932          0.00                 0   \n",
       "6      e2127556f4f64592b11af22de27a7932         18.42                 1   \n",
       "7      e2127556f4f64592b11af22de27a7932         18.42                 1   \n",
       "8      389bc3fa690240e798340f5a15918d5c          9.54                 1   \n",
       "9      389bc3fa690240e798340f5a15918d5c          0.00                 0   \n",
       "10     389bc3fa690240e798340f5a15918d5c         15.63                 1   \n",
       "11     389bc3fa690240e798340f5a15918d5c         15.63                 1   \n",
       "12     389bc3fa690240e798340f5a15918d5c         11.26                 1   \n",
       "13     389bc3fa690240e798340f5a15918d5c         11.26                 1   \n",
       "14     2eeac8d8feae4a8cad5a6af0499a211d          0.00                 0   \n",
       "15     2eeac8d8feae4a8cad5a6af0499a211d          2.32                 0   \n",
       "16     2eeac8d8feae4a8cad5a6af0499a211d         13.30                 1   \n",
       "17     aa4862eba776480b8bb9c68455b8c2e1         12.33                 1   \n",
       "18     aa4862eba776480b8bb9c68455b8c2e1         12.33                 1   \n",
       "19     aa4862eba776480b8bb9c68455b8c2e1          0.00                 0   \n",
       "20     aa4862eba776480b8bb9c68455b8c2e1         60.66                 1   \n",
       "21     aa4862eba776480b8bb9c68455b8c2e1         12.56                 1   \n",
       "22     31dda685af34476cad5bc968bdb01c53          0.00                 0   \n",
       "23     31dda685af34476cad5bc968bdb01c53         69.50                 1   \n",
       "24     31dda685af34476cad5bc968bdb01c53         50.76                 1   \n",
       "25     31dda685af34476cad5bc968bdb01c53         90.75                 1   \n",
       "26     31dda685af34476cad5bc968bdb01c53         52.56                 1   \n",
       "27     31dda685af34476cad5bc968bdb01c53          0.00                 0   \n",
       "28     4b0da7e80e5945209a1fdddfe813dbe0          0.00                 0   \n",
       "29     4b0da7e80e5945209a1fdddfe813dbe0        109.28                 1   \n",
       "...                                 ...           ...               ...   \n",
       "66471  8578196a074a4f328976e334fa9383a3          0.00                 0   \n",
       "66472  8578196a074a4f328976e334fa9383a3          4.62                 0   \n",
       "66473  1b1ca1a1ece440c7aac02b9f7ccf2f5a         66.84                 1   \n",
       "66474  1b1ca1a1ece440c7aac02b9f7ccf2f5a         66.84                 0   \n",
       "66475  d957b9bd21fd4cacb39aab4b2834704b          0.00                 0   \n",
       "66476  d4b620d3f4be4a7187245c6b571d0f91         44.56                 1   \n",
       "66477  d4b620d3f4be4a7187245c6b571d0f91          0.00                 0   \n",
       "66478  c4e2af52bdc8414289e7b6c9d3d00dda        108.52                 1   \n",
       "66479  c4e2af52bdc8414289e7b6c9d3d00dda        108.75                 1   \n",
       "66480  9fcbff4f8d7241faa4ab8a9d19c8a812          0.00                 0   \n",
       "66481  9fcbff4f8d7241faa4ab8a9d19c8a812          0.00                 0   \n",
       "66482  ae4e5d8d13d74a2eba8ed2d049c1a0c1          6.71                 0   \n",
       "66483  ae4e5d8d13d74a2eba8ed2d049c1a0c1          5.51                 0   \n",
       "66484  3f3494f3234e4206a279662bef81fe85         13.35                 0   \n",
       "66485  3f3494f3234e4206a279662bef81fe85         15.83                 0   \n",
       "66486  2f4973b4196e40db82756cbb0b7f0884          0.00                 0   \n",
       "66487  2f4973b4196e40db82756cbb0b7f0884          0.00                 0   \n",
       "66488  1eed89c11cc749a4bb93d8cef032821d        681.87                 1   \n",
       "66489  97b2476046874de3975e8feee95a1f98         46.33                 1   \n",
       "66490  c297c0a707724e868c3212f721a20457         45.44                 1   \n",
       "66491  14b3fa95de4f404a8c46709ef16ec58f         45.84                 1   \n",
       "66492  9acbb111ba2f452cbbafabaf49138377          1.96                 0   \n",
       "66493  fde9c2ca1c184c3eab2c7161c5f6a6c7         57.86                 1   \n",
       "66494  297a08c1d05b405496e2c42bf2ea21d6         45.44                 1   \n",
       "66495  3045af4e98794a04a5542d3eac939b1f          0.00                 0   \n",
       "66496  3cc622f76e464bc1809a71e871579d6c         34.04                 0   \n",
       "66497  01fe5ec668f241608eb2f7ec374cb1b7         19.92                 0   \n",
       "66498  d825b707198046e39baa4fdcfa11d621         28.42                 0   \n",
       "66499  c207a8a8ef5c47ee970ca45f859e07eb         75.84                 1   \n",
       "66500  9a3f45cf29ef428b932492c7a5d6ac52         14.25                 1   \n",
       "\n",
       "       difficulty  duration  reward  bogo  discount     ...       email  \\\n",
       "0               5         7       5   1.0       0.0     ...         1.0   \n",
       "1               0         3       0   0.0       0.0     ...         1.0   \n",
       "2              10         7      10   1.0       0.0     ...         1.0   \n",
       "3               5         5       5   1.0       0.0     ...         1.0   \n",
       "4              10         7       2   0.0       1.0     ...         1.0   \n",
       "5               0         4       0   0.0       0.0     ...         1.0   \n",
       "6               5         7       5   1.0       0.0     ...         1.0   \n",
       "7              10        10       2   0.0       1.0     ...         1.0   \n",
       "8               5         5       5   1.0       0.0     ...         1.0   \n",
       "9               5         7       5   1.0       0.0     ...         1.0   \n",
       "10             10         7       2   0.0       1.0     ...         1.0   \n",
       "11              5         7       5   1.0       0.0     ...         1.0   \n",
       "12              5         5       5   1.0       0.0     ...         1.0   \n",
       "13             10        10       2   0.0       1.0     ...         1.0   \n",
       "14              0         4       0   0.0       0.0     ...         1.0   \n",
       "15              7         7       3   0.0       1.0     ...         1.0   \n",
       "16             10        10       2   0.0       1.0     ...         1.0   \n",
       "17             20        10       5   0.0       1.0     ...         1.0   \n",
       "18             10         5      10   1.0       0.0     ...         1.0   \n",
       "19              0         3       0   0.0       0.0     ...         1.0   \n",
       "20             10         7      10   1.0       0.0     ...         1.0   \n",
       "21              5         5       5   1.0       0.0     ...         1.0   \n",
       "22             20        10       5   0.0       1.0     ...         1.0   \n",
       "23             10        10       2   0.0       1.0     ...         1.0   \n",
       "24              7         7       3   0.0       1.0     ...         1.0   \n",
       "25              7         7       3   0.0       1.0     ...         1.0   \n",
       "26              7         7       3   0.0       1.0     ...         1.0   \n",
       "27             10        10       2   0.0       1.0     ...         1.0   \n",
       "28             10         7      10   1.0       0.0     ...         1.0   \n",
       "29             20        10       5   0.0       1.0     ...         1.0   \n",
       "...           ...       ...     ...   ...       ...     ...         ...   \n",
       "66471          10         5      10   1.0       0.0     ...         1.0   \n",
       "66472          10         7       2   0.0       1.0     ...         1.0   \n",
       "66473          10        10       2   0.0       1.0     ...         1.0   \n",
       "66474          10         7       2   0.0       1.0     ...         1.0   \n",
       "66475          10         7      10   1.0       0.0     ...         1.0   \n",
       "66476          20        10       5   0.0       1.0     ...         1.0   \n",
       "66477          10         5      10   1.0       0.0     ...         1.0   \n",
       "66478           7         7       3   0.0       1.0     ...         1.0   \n",
       "66479           5         5       5   1.0       0.0     ...         1.0   \n",
       "66480           0         4       0   0.0       0.0     ...         1.0   \n",
       "66481          10         5      10   1.0       0.0     ...         1.0   \n",
       "66482          10         7      10   1.0       0.0     ...         1.0   \n",
       "66483           0         4       0   0.0       0.0     ...         1.0   \n",
       "66484          10         7      10   1.0       0.0     ...         1.0   \n",
       "66485          10         5      10   1.0       0.0     ...         1.0   \n",
       "66486           7         7       3   0.0       1.0     ...         1.0   \n",
       "66487           7         7       3   0.0       1.0     ...         1.0   \n",
       "66488          10        10       2   0.0       1.0     ...         1.0   \n",
       "66489          20        10       5   0.0       1.0     ...         1.0   \n",
       "66490           7         7       3   0.0       1.0     ...         1.0   \n",
       "66491           7         7       3   0.0       1.0     ...         1.0   \n",
       "66492          20        10       5   0.0       1.0     ...         1.0   \n",
       "66493           5         7       5   1.0       0.0     ...         1.0   \n",
       "66494           5         7       5   1.0       0.0     ...         1.0   \n",
       "66495          10         5      10   1.0       0.0     ...         1.0   \n",
       "66496          10         7       2   0.0       1.0     ...         1.0   \n",
       "66497           0         3       0   0.0       0.0     ...         1.0   \n",
       "66498           5         7       5   1.0       0.0     ...         1.0   \n",
       "66499           7         7       3   0.0       1.0     ...         1.0   \n",
       "66500           7         7       3   0.0       1.0     ...         1.0   \n",
       "\n",
       "       mobile  social  web  became_member_on gender    income   age  \\\n",
       "0         1.0     0.0  1.0        2017-05-09      0  100000.0  75.0   \n",
       "1         1.0     1.0  0.0        2017-05-09      0  100000.0  75.0   \n",
       "2         1.0     1.0  0.0        2017-05-09      0  100000.0  75.0   \n",
       "3         1.0     1.0  1.0        2017-05-09      0  100000.0  75.0   \n",
       "4         1.0     0.0  1.0        2018-04-26      1   70000.0  68.0   \n",
       "5         1.0     0.0  1.0        2018-04-26      1   70000.0  68.0   \n",
       "6         1.0     0.0  1.0        2018-04-26      1   70000.0  68.0   \n",
       "7         1.0     1.0  1.0        2018-04-26      1   70000.0  68.0   \n",
       "8         1.0     1.0  1.0        2018-02-09      1   53000.0  65.0   \n",
       "9         1.0     0.0  1.0        2018-02-09      1   53000.0  65.0   \n",
       "10        1.0     0.0  1.0        2018-02-09      1   53000.0  65.0   \n",
       "11        1.0     0.0  1.0        2018-02-09      1   53000.0  65.0   \n",
       "12        1.0     1.0  1.0        2018-02-09      1   53000.0  65.0   \n",
       "13        1.0     1.0  1.0        2018-02-09      1   53000.0  65.0   \n",
       "14        1.0     0.0  1.0        2017-11-11      1   51000.0  58.0   \n",
       "15        1.0     1.0  1.0        2017-11-11      1   51000.0  58.0   \n",
       "16        1.0     1.0  1.0        2017-11-11      1   51000.0  58.0   \n",
       "17        0.0     0.0  1.0        2017-09-11      0   57000.0  61.0   \n",
       "18        1.0     1.0  1.0        2017-09-11      0   57000.0  61.0   \n",
       "19        1.0     1.0  0.0        2017-09-11      0   57000.0  61.0   \n",
       "20        1.0     1.0  0.0        2017-09-11      0   57000.0  61.0   \n",
       "21        1.0     1.0  1.0        2017-09-11      0   57000.0  61.0   \n",
       "22        0.0     0.0  1.0        2016-02-11      0   71000.0  62.0   \n",
       "23        1.0     1.0  1.0        2016-02-11      0   71000.0  62.0   \n",
       "24        1.0     1.0  1.0        2016-02-11      0   71000.0  62.0   \n",
       "25        1.0     1.0  1.0        2016-02-11      0   71000.0  62.0   \n",
       "26        1.0     1.0  1.0        2016-02-11      0   71000.0  62.0   \n",
       "27        1.0     1.0  1.0        2016-02-11      0   71000.0  62.0   \n",
       "28        1.0     1.0  0.0        2017-09-09      1  100000.0  64.0   \n",
       "29        0.0     0.0  1.0        2017-09-09      1  100000.0  64.0   \n",
       "...       ...     ...  ...               ...    ...       ...   ...   \n",
       "66471     1.0     1.0  1.0        2018-06-10      1   58000.0  48.0   \n",
       "66472     1.0     0.0  1.0        2018-06-10      1   58000.0  48.0   \n",
       "66473     1.0     1.0  1.0        2015-09-05      1   98000.0  48.0   \n",
       "66474     1.0     0.0  1.0        2015-09-05      1   98000.0  48.0   \n",
       "66475     1.0     1.0  0.0        2018-04-18      0   84000.0  82.0   \n",
       "66476     0.0     0.0  1.0        2016-03-31      0   81000.0  44.0   \n",
       "66477     1.0     1.0  1.0        2016-03-31      0   81000.0  44.0   \n",
       "66478     1.0     1.0  1.0        2016-05-19      1   93000.0  56.0   \n",
       "66479     1.0     1.0  1.0        2016-05-19      1   93000.0  56.0   \n",
       "66480     1.0     0.0  1.0        2017-10-13      1   94000.0  47.0   \n",
       "66481     1.0     1.0  1.0        2017-10-13      1   94000.0  47.0   \n",
       "66482     1.0     1.0  0.0        2017-10-15      0   54000.0  28.0   \n",
       "66483     1.0     0.0  1.0        2017-10-15      0   54000.0  28.0   \n",
       "66484     1.0     1.0  0.0        2014-07-15      0   60000.0  61.0   \n",
       "66485     1.0     1.0  1.0        2014-07-15      0   60000.0  61.0   \n",
       "66486     1.0     1.0  1.0        2017-08-07      0   49000.0  46.0   \n",
       "66487     1.0     1.0  1.0        2017-08-07      0   49000.0  46.0   \n",
       "66488     1.0     1.0  1.0        2017-07-09      1   74000.0  38.0   \n",
       "66489     0.0     0.0  1.0        2016-04-06      1   73000.0  25.0   \n",
       "66490     1.0     1.0  1.0        2017-08-08      0   68000.0  63.0   \n",
       "66491     1.0     1.0  1.0        2016-09-18      0   61000.0  68.0   \n",
       "66492     0.0     0.0  1.0        2014-07-20      1   44000.0  77.0   \n",
       "66493     1.0     0.0  1.0        2013-08-13      1   52000.0  59.0   \n",
       "66494     1.0     0.0  1.0        2016-09-21      1   54000.0  29.0   \n",
       "66495     1.0     1.0  1.0        2016-10-20      0   78000.0  58.0   \n",
       "66496     1.0     0.0  1.0        2017-08-20      1   44000.0  59.0   \n",
       "66497     1.0     1.0  0.0        2017-08-02      1   64000.0  44.0   \n",
       "66498     1.0     0.0  1.0        2018-04-23      1   73000.0  61.0   \n",
       "66499     1.0     1.0  1.0        2017-12-23      0   86000.0  52.0   \n",
       "66500     1.0     1.0  1.0        2016-10-19      1   70000.0  20.0   \n",
       "\n",
       "       start_year  start_month  \n",
       "0            2017            5  \n",
       "1            2017            5  \n",
       "2            2017            5  \n",
       "3            2017            5  \n",
       "4            2018            4  \n",
       "5            2018            4  \n",
       "6            2018            4  \n",
       "7            2018            4  \n",
       "8            2018            2  \n",
       "9            2018            2  \n",
       "10           2018            2  \n",
       "11           2018            2  \n",
       "12           2018            2  \n",
       "13           2018            2  \n",
       "14           2017           11  \n",
       "15           2017           11  \n",
       "16           2017           11  \n",
       "17           2017            9  \n",
       "18           2017            9  \n",
       "19           2017            9  \n",
       "20           2017            9  \n",
       "21           2017            9  \n",
       "22           2016            2  \n",
       "23           2016            2  \n",
       "24           2016            2  \n",
       "25           2016            2  \n",
       "26           2016            2  \n",
       "27           2016            2  \n",
       "28           2017            9  \n",
       "29           2017            9  \n",
       "...           ...          ...  \n",
       "66471        2018            6  \n",
       "66472        2018            6  \n",
       "66473        2015            9  \n",
       "66474        2015            9  \n",
       "66475        2018            4  \n",
       "66476        2016            3  \n",
       "66477        2016            3  \n",
       "66478        2016            5  \n",
       "66479        2016            5  \n",
       "66480        2017           10  \n",
       "66481        2017           10  \n",
       "66482        2017           10  \n",
       "66483        2017           10  \n",
       "66484        2014            7  \n",
       "66485        2014            7  \n",
       "66486        2017            8  \n",
       "66487        2017            8  \n",
       "66488        2017            7  \n",
       "66489        2016            4  \n",
       "66490        2017            8  \n",
       "66491        2016            9  \n",
       "66492        2014            7  \n",
       "66493        2013            8  \n",
       "66494        2016            9  \n",
       "66495        2016           10  \n",
       "66496        2017            8  \n",
       "66497        2017            8  \n",
       "66498        2018            4  \n",
       "66499        2017           12  \n",
       "66500        2016           10  \n",
       "\n",
       "[66501 rows x 21 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_list = ['time', 'offer_id', 'customer_id', 'total_amount', \n",
    "                'offer_successful', 'difficulty', 'duration', \n",
    "                'reward', 'bogo', 'discount', 'informational',\n",
    "                'email', 'mobile', 'social', 'web', 'became_member_on',\n",
    "                'gender', 'income', 'age', 'start_year', 'start_month']\n",
    "data[columns_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv('master_offer_analysis.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('master_offer_analysis.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>offer_id</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>age</th>\n",
       "      <th>bogo</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>discount</th>\n",
       "      <th>duration</th>\n",
       "      <th>gender</th>\n",
       "      <th>income</th>\n",
       "      <th>mobile</th>\n",
       "      <th>offer_successful</th>\n",
       "      <th>reward</th>\n",
       "      <th>social</th>\n",
       "      <th>start_month</th>\n",
       "      <th>start_year</th>\n",
       "      <th>web</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
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       "      <td>5</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>48.28</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>48.28</td>\n",
       "      <td>75.0</td>\n",
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       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0.00</td>\n",
       "      <td>68.0</td>\n",
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       "      <td>2</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           offer_id  total_amount   age  bogo  difficulty  \\\n",
       "0  9b98b8c7a33c4b65b9aebfe6a799e6d9         37.67  75.0   1.0           5   \n",
       "1  5a8bc65990b245e5a138643cd4eb9837         49.39  75.0   0.0           0   \n",
       "2  ae264e3637204a6fb9bb56bc8210ddfd         48.28  75.0   1.0          10   \n",
       "3  f19421c1d4aa40978ebb69ca19b0e20d         48.28  75.0   1.0           5   \n",
       "4  2906b810c7d4411798c6938adc9daaa5          0.00  68.0   0.0          10   \n",
       "\n",
       "   discount  duration  gender    income  mobile  offer_successful  reward  \\\n",
       "0       0.0         7       0  100000.0     1.0                 1       5   \n",
       "1       0.0         3       0  100000.0     1.0                 0       0   \n",
       "2       0.0         7       0  100000.0     1.0                 1      10   \n",
       "3       0.0         5       0  100000.0     1.0                 1       5   \n",
       "4       1.0         7       1   70000.0     1.0                 0       2   \n",
       "\n",
       "   social  start_month  start_year  web  \n",
       "0     0.0            5        2017  1.0  \n",
       "1     1.0            5        2017  0.0  \n",
       "2     1.0            5        2017  0.0  \n",
       "3     1.0            5        2017  1.0  \n",
       "4     0.0            4        2018  1.0  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_ordering = ['offer_id', 'total_amount']\n",
    "column_ordering.extend([elem for elem in data.columns if elem not in column_ordering])\n",
    "\n",
    "data = data[column_ordering]\n",
    "data = data.drop(columns=['time', 'customer_id', 'email', 'informational', 'became_member_on'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_percentage_success():\n",
    "    successful_count = data[['offer_id', 'offer_successful']].groupby(\n",
    "        'offer_id').sum().reset_index()\n",
    "\n",
    "    offer_count = data['offer_id'].value_counts()\n",
    "\n",
    "    offer_count = pd.DataFrame(list(zip(offer_count.index.values,\n",
    "                                        offer_count.values)),\n",
    "                               columns=['offer_id', 'count'])\n",
    "\n",
    "    successful_count = successful_count.sort_values('offer_id')\n",
    "\n",
    "    offer_count = offer_count.sort_values('offer_id')\n",
    "\n",
    "    percent_success = pd.merge(offer_count, successful_count, on=\"offer_id\")\n",
    "\n",
    "    percent_success['percent_success'] = (\n",
    "        100 * percent_success['offer_successful'] / percent_success['count'])\n",
    "\n",
    "    percent_success = pd.merge(percent_success, portfolio, on=\"offer_id\")\n",
    "\n",
    "    percent_success = percent_success.drop(columns=['offer_successful'])\n",
    "\n",
    "    percent_success = percent_success.sort_values('percent_success', ascending=False)\n",
    "\n",
    "    return percent_success.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>6655</td>\n",
       "      <td>72.742299</td>\n",
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       "      <td>6576</td>\n",
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       "    <tr>\n",
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       "      <td>6593</td>\n",
       "      <td>51.721523</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
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       "      <td>6685</td>\n",
       "      <td>48.287210</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
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       "      <td>6631</td>\n",
       "      <td>47.730357</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>6726</td>\n",
       "      <td>45.762712</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>7.721196</td>\n",
       "      <td>0</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
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       "      <td>6643</td>\n",
       "      <td>6.277284</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "                           offer_id  count  percent_success  difficulty  \\\n",
       "0  fafdcd668e3743c1bb461111dcafc2a4   6652        75.285628          10   \n",
       "1  2298d6c36e964ae4a3e7e9706d1fb8c2   6655        72.742299           7   \n",
       "2  f19421c1d4aa40978ebb69ca19b0e20d   6576        61.618005           5   \n",
       "3  ae264e3637204a6fb9bb56bc8210ddfd   6683        54.646117          10   \n",
       "4  4d5c57ea9a6940dd891ad53e9dbe8da0   6593        51.721523          10   \n",
       "5  9b98b8c7a33c4b65b9aebfe6a799e6d9   6685        48.287210           5   \n",
       "6  2906b810c7d4411798c6938adc9daaa5   6631        47.730357          10   \n",
       "7  0b1e1539f2cc45b7b9fa7c272da2e1d7   6726        45.762712          20   \n",
       "8  3f207df678b143eea3cee63160fa8bed   6657         7.721196           0   \n",
       "9  5a8bc65990b245e5a138643cd4eb9837   6643         6.277284           0   \n",
       "\n",
       "   duration  reward  web  email  social  mobile  bogo  informational  discount  \n",
       "0        10       2  1.0    1.0     1.0     1.0   0.0            0.0       1.0  \n",
       "1         7       3  1.0    1.0     1.0     1.0   0.0            0.0       1.0  \n",
       "2         5       5  1.0    1.0     1.0     1.0   1.0            0.0       0.0  \n",
       "3         7      10  0.0    1.0     1.0     1.0   1.0            0.0       0.0  \n",
       "4         5      10  1.0    1.0     1.0     1.0   1.0            0.0       0.0  \n",
       "5         7       5  1.0    1.0     0.0     1.0   1.0            0.0       0.0  \n",
       "6         7       2  1.0    1.0     0.0     1.0   0.0            0.0       1.0  \n",
       "7        10       5  1.0    1.0     0.0     0.0   0.0            0.0       1.0  \n",
       "8         4       0  1.0    1.0     0.0     1.0   0.0            1.0       0.0  \n",
       "9         3       0  0.0    1.0     1.0     1.0   0.0            1.0       0.0  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percent_success = calculate_percentage_success()\n",
    "percent_success"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate a plot that illustrates:\n",
    "\n",
    "- How many customers were provided with a specific offer?\n",
    "- Offer success rate (percentsuccess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Success percentage')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 4), nrows=1, ncols=2)\n",
    "\n",
    "ax[0].bar(percent_success.index + 1, percent_success['count'])\n",
    "ax[0].set_xticks(np.arange(0,10) + 1)\n",
    "ax[0].set_xlabel('Offer number')\n",
    "ax[0].set_ylabel('Count')\n",
    "\n",
    "ax[1].plot(percent_success.index + 1, percent_success['percent_success'])\n",
    "ax[1].set_xticks(np.arange(0,10) + 1)\n",
    "ax[1].set_xlabel('Offer number')\n",
    "ax[1].set_ylabel('Success percentage')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create train and test data sets "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# factors/features that influence the label variable\n",
    "features = data.drop(columns=['offer_successful'])\n",
    "\n",
    "# label variable to predict\n",
    "label = data.filter(['offer_successful'])\n",
    "\n",
    "# split data into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(features.values, label.values, test_size=0.3, random_state=42)\n",
    "\n",
    "# train-data: extract offer_id, total_amount and convert other features to float \n",
    "offer_id_train = X_train[:, 0]\n",
    "total_amount_train = X_train[:, 1]\n",
    "X_train = X_train[:, 2:].astype('float64')\n",
    "\n",
    "# test-data: extract offer_id, total_amount and convert other features to float \n",
    "offer_id_test = X_test[:, 0]\n",
    "total_amount_test = X_test[:, 1]\n",
    "X_test = X_test[:, 2:].astype('float64')\n",
    "\n",
    "# convert train and test labels to array\n",
    "y_train = y_train.ravel()\n",
    "y_test = y_test.ravel()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate Naive Predictor Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive predictor accuracy: 0.471\n",
      "Naive predictor f1-score: 0.640\n"
     ]
    }
   ],
   "source": [
    "naive_predictor_accuracy = accuracy_score(y_train, np.ones(len(y_train)))\n",
    "naive_predictor_f1score = f1_score(y_train, np.ones(len(y_train)))\n",
    "\n",
    "print(\"Naive predictor accuracy: %.3f\" % (naive_predictor_accuracy))\n",
    "print(\"Naive predictor f1-score: %.3f\" % (naive_predictor_f1score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Construct Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 6 candidates, totalling 18 fits\n",
      "[CV] penalty=l1, C=1.0 ...............................................\n",
      "[CV] penalty=l1, C=1.0 ...............................................\n",
      "[CV] penalty=l1, C=1.0 ...............................................\n",
      "[CV] ................................ penalty=l1, C=1.0, total=  10.0s\n",
      "[CV] penalty=l2, C=1.0 ...............................................\n",
      "[CV] ................................ penalty=l1, C=1.0, total=  10.1s\n",
      "[CV] penalty=l2, C=1.0 ...............................................\n",
      "[CV] ................................ penalty=l2, C=1.0, total=   0.4s\n",
      "[CV] penalty=l2, C=1.0 ...............................................\n",
      "[CV] ................................ penalty=l2, C=1.0, total=   0.4s\n",
      "[CV] penalty=l1, C=0.1 ...............................................\n",
      "[CV] ................................ penalty=l2, C=1.0, total=   0.4s\n",
      "[CV] penalty=l1, C=0.1 ...............................................\n",
      "[CV] ................................ penalty=l1, C=0.1, total=   2.5s\n",
      "[CV] penalty=l1, C=0.1 ...............................................\n",
      "[CV] ................................ penalty=l1, C=0.1, total=   3.1s\n",
      "[CV] penalty=l2, C=0.1 ...............................................\n",
      "[CV] ................................ penalty=l2, C=0.1, total=   0.4s\n",
      "[CV] penalty=l2, C=0.1 ...............................................\n",
      "[CV] ................................ penalty=l2, C=0.1, total=   0.4s\n",
      "[CV] penalty=l2, C=0.1 ...............................................\n",
      "[CV] ................................ penalty=l2, C=0.1, total=   0.4s\n",
      "[CV] penalty=l1, C=0.01 ..............................................\n",
      "[CV] ............................... penalty=l1, C=0.01, total=   0.9s\n",
      "[CV] penalty=l1, C=0.01 ..............................................\n",
      "[CV] ................................ penalty=l1, C=0.1, total=   3.2s\n",
      "[CV] penalty=l1, C=0.01 ..............................................\n",
      "[CV] ............................... penalty=l1, C=0.01, total=   0.9s\n",
      "[CV] penalty=l2, C=0.01 ..............................................\n",
      "[CV] ............................... penalty=l2, C=0.01, total=   0.4s\n",
      "[CV] penalty=l2, C=0.01 ..............................................\n",
      "[CV] ............................... penalty=l1, C=0.01, total=   1.2s\n",
      "[CV] penalty=l2, C=0.01 ..............................................\n",
      "[CV] ............................... penalty=l2, C=0.01, total=   0.4s\n",
      "[CV] ............................... penalty=l2, C=0.01, total=   0.4s\n",
      "[CV] ................................ penalty=l1, C=1.0, total=  22.5s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=3)]: Done  18 out of  18 | elapsed:   22.6s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, error_score='raise',\n",
       "          estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=42, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False),\n",
       "          fit_params=None, iid=True, n_iter=6, n_jobs=3,\n",
       "          param_distributions={'penalty': ['l1', 'l2'], 'C': [1.0, 0.1, 0.01]},\n",
       "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "          return_train_score='warn',\n",
       "          scoring=make_scorer(fbeta_score, beta=0.5), verbose=2)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scorer = make_scorer(fbeta_score, beta=0.5)\n",
    "\n",
    "# instantiate a logistic regression classifer object\n",
    "lr_clf = LogisticRegression(random_state=42, solver='liblinear')\n",
    "\n",
    "# construct a params dict to tune the model\n",
    "grid_params = {\n",
    "    'penalty': ['l1', 'l2'],\n",
    "    'C': [1.0, 0.1, 0.01]}\n",
    "\n",
    "lr_random = RandomizedSearchCV(\n",
    "    estimator = lr_clf, param_distributions = grid_params, \n",
    "    scoring=scorer, n_iter = 6, cv = 3, verbose=2, \n",
    "    random_state=42, n_jobs = 3)\n",
    "\n",
    "# fit train data to the model\n",
    "lr_random.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_model_performance(clf, X_train, y_train):\n",
    "    class_name = re.sub(\"[<>']\", '', str(clf.__class__))\n",
    "    class_name = class_name.split(' ')[1]\n",
    "    class_name = class_name.split('.')[-1]\n",
    "\n",
    "    y_pred_rf = clf.predict(X_train)\n",
    "\n",
    "    clf_accuracy = accuracy_score(y_train, y_pred_rf)\n",
    "    clf_f1_score = f1_score(y_train, y_pred_rf)\n",
    "    \n",
    "    print(\"%s model accuracy: %.3f\" % (class_name, clf_accuracy))\n",
    "    print(\"%s model f1-score: %.3f\" % (class_name, clf_f1_score))\n",
    "    \n",
    "    return clf_accuracy, clf_f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression model accuracy: 0.698\n",
      "LogisticRegression model f1-score: 0.693\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.6978947368421052, 0.6933693827268168)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# evaluate logistic regression model's performance\n",
    "evaluate_model_performance(lr_random.best_estimator_, X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Results\n",
    "- Results suggest that a logistic regression model's accuracy and f1-score is better than the naive predictor\n",
    "- Accuracy\n",
    "    - Naive predictor: 0.471\n",
    "    - Logistic regression: 0.697\n",
    "- F1-score\n",
    "    - Naive predictor: 0.640\n",
    "    - Logistic regression: 0.693"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate Test Data Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomizedSearchCV model accuracy: 0.702\n",
      "RandomizedSearchCV model f1-score: 0.698\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.7017693348704326, 0.6976318731578413)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate_model_performance(lr_random, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Construct Random Forest Classifier Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n",
      "[CV] n_estimators=10, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=9 \n",
      "[CV] n_estimators=10, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=9 \n",
      "[CV] n_estimators=10, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=9 \n",
      "[CV]  n_estimators=10, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=9, total=   0.2s\n",
      "[CV]  n_estimators=10, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=9, total=   0.2s\n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=10 \n",
      "[CV]  n_estimators=10, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=9, total=   0.2s\n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=10 \n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=10 \n",
      "[CV]  n_estimators=200, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=10, total=   3.2s\n",
      "[CV] n_estimators=10, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=6 \n",
      "[CV]  n_estimators=200, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=10, total=   3.2s\n",
      "[CV]  n_estimators=200, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=10, total=   3.2s\n",
      "[CV] n_estimators=10, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=6 \n",
      "[CV] n_estimators=10, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=6 \n",
      "[CV]  n_estimators=10, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=6, total=   0.2s\n",
      "[CV] n_estimators=250, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=6 \n",
      "[CV]  n_estimators=10, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=6, total=   0.2s\n",
      "[CV]  n_estimators=10, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=6, total=   0.1s\n",
      "[CV] n_estimators=250, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=6 \n",
      "[CV] n_estimators=250, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=6 \n",
      "[CV]  n_estimators=250, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=6, total=   2.9s\n",
      "[CV]  n_estimators=250, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=6, total=   2.9s\n",
      "[CV] n_estimators=200, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=8 \n",
      "[CV]  n_estimators=250, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=6, total=   2.9s\n",
      "[CV] n_estimators=200, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=8 \n",
      "[CV] n_estimators=200, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=8 \n",
      "[CV]  n_estimators=200, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=8, total=   2.7s\n",
      "[CV] n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=3 \n",
      "[CV]  n_estimators=200, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=8, total=   2.7s\n",
      "[CV]  n_estimators=200, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=8, total=   2.7s\n",
      "[CV] n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=3 \n",
      "[CV] n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=3 \n",
      "[CV]  n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=3, total=   1.6s\n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=7 \n",
      "[CV]  n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=3, total=   1.7s\n",
      "[CV]  n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=3, total=   1.7s\n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=7 \n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=7 \n",
      "[CV]  n_estimators=200, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=7, total=   2.5s\n",
      "[CV]  n_estimators=200, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=7, total=   2.5s\n",
      "[CV] n_estimators=10, min_samples_split=10, min_samples_leaf=4, max_features=auto, max_depth=3 \n",
      "[CV] n_estimators=10, min_samples_split=10, min_samples_leaf=4, max_features=auto, max_depth=3 \n",
      "[CV]  n_estimators=200, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=7, total=   2.5s\n",
      "[CV] n_estimators=10, min_samples_split=10, min_samples_leaf=4, max_features=auto, max_depth=3 \n",
      "[CV]  n_estimators=10, min_samples_split=10, min_samples_leaf=4, max_features=auto, max_depth=3, total=   0.1s\n",
      "[CV]  n_estimators=10, min_samples_split=10, min_samples_leaf=4, max_features=auto, max_depth=3, total=   0.1s\n",
      "[CV] n_estimators=250, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=None \n",
      "[CV] n_estimators=250, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=None \n",
      "[CV]  n_estimators=10, min_samples_split=10, min_samples_leaf=4, max_features=auto, max_depth=3, total=   0.1s\n",
      "[CV] n_estimators=250, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=None \n",
      "[CV]  n_estimators=250, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=None, total=   7.1s\n",
      "[CV]  n_estimators=250, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=None, total=   7.0s\n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=250, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=None, total=   7.1s\n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=10 \n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=200, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=10, total=   3.2s\n",
      "[CV] n_estimators=300, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=9 \n",
      "[CV]  n_estimators=200, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=10, total=   3.2s\n",
      "[CV]  n_estimators=200, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=10, total=   3.2s\n",
      "[CV] n_estimators=300, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=9 \n",
      "[CV] n_estimators=300, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=9 \n",
      "[CV]  n_estimators=300, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=9, total=   4.5s\n",
      "[CV]  n_estimators=300, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=9, total=   4.4s\n",
      "[CV] n_estimators=100, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=3 \n",
      "[CV]  n_estimators=300, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=9, total=   4.4s\n",
      "[CV] n_estimators=100, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=3 \n",
      "[CV] n_estimators=100, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=3 \n",
      "[CV]  n_estimators=100, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=3, total=   0.9s\n",
      "[CV]  n_estimators=100, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=3, total=   0.8s\n",
      "[CV] n_estimators=100, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=7 \n",
      "[CV]  n_estimators=100, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=3, total=   0.8s\n",
      "[CV] n_estimators=100, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=7 \n",
      "[CV] n_estimators=100, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=7 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=3)]: Done  35 tasks      | elapsed:   35.8s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=100, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=7, total=   1.3s\n",
      "[CV] n_estimators=250, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=9 \n",
      "[CV]  n_estimators=100, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=7, total=   1.3s\n",
      "[CV]  n_estimators=100, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=7, total=   1.3s\n",
      "[CV] n_estimators=250, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=9 \n",
      "[CV] n_estimators=250, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=9 \n",
      "[CV]  n_estimators=250, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=9, total=   3.7s\n",
      "[CV]  n_estimators=250, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=9, total=   3.7s\n",
      "[CV]  n_estimators=250, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=9, total=   3.7s\n",
      "[CV] n_estimators=150, min_samples_split=10, min_samples_leaf=4, max_features=sqrt, max_depth=8 \n",
      "[CV] n_estimators=150, min_samples_split=10, min_samples_leaf=4, max_features=sqrt, max_depth=8 \n",
      "[CV] n_estimators=150, min_samples_split=10, min_samples_leaf=4, max_features=sqrt, max_depth=8 \n",
      "[CV]  n_estimators=150, min_samples_split=10, min_samples_leaf=4, max_features=sqrt, max_depth=8, total=   2.0s\n",
      "[CV] n_estimators=200, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=10 \n",
      "[CV]  n_estimators=150, min_samples_split=10, min_samples_leaf=4, max_features=sqrt, max_depth=8, total=   2.1s\n",
      "[CV] n_estimators=200, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=10 \n",
      "[CV]  n_estimators=150, min_samples_split=10, min_samples_leaf=4, max_features=sqrt, max_depth=8, total=   2.1s\n",
      "[CV] n_estimators=200, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=10 \n",
      "[CV]  n_estimators=200, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=10, total=   3.2s\n",
      "[CV]  n_estimators=200, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=10, total=   3.2s\n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=8 \n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=8 \n",
      "[CV]  n_estimators=200, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=10, total=   3.2s\n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=8 \n",
      "[CV]  n_estimators=200, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=8, total=   2.7s\n",
      "[CV] n_estimators=50, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=9 \n",
      "[CV]  n_estimators=200, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=8, total=   2.7s\n",
      "[CV]  n_estimators=200, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=8, total=   2.7s\n",
      "[CV] n_estimators=50, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=9 \n",
      "[CV] n_estimators=50, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=9 \n",
      "[CV]  n_estimators=50, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=9, total=   0.8s\n",
      "[CV]  n_estimators=50, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=9, total=   0.8s\n",
      "[CV] n_estimators=50, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=8 \n",
      "[CV]  n_estimators=50, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=9, total=   0.8s\n",
      "[CV] n_estimators=50, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=8 \n",
      "[CV] n_estimators=50, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=8 \n",
      "[CV]  n_estimators=50, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=8, total=   0.7s\n",
      "[CV] n_estimators=10, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=6 \n",
      "[CV]  n_estimators=50, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=8, total=   0.7s\n",
      "[CV]  n_estimators=50, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=8, total=   0.7s\n",
      "[CV] n_estimators=10, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=6 \n",
      "[CV] n_estimators=10, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=6 \n",
      "[CV]  n_estimators=10, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=6, total=   0.1s\n",
      "[CV] n_estimators=150, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=3 \n",
      "[CV]  n_estimators=10, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=6, total=   0.1s\n",
      "[CV] n_estimators=150, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=3 \n",
      "[CV]  n_estimators=10, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=6, total=   0.2s\n",
      "[CV] n_estimators=150, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=3 \n",
      "[CV]  n_estimators=150, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=3, total=   1.2s\n",
      "[CV]  n_estimators=150, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=3, total=   1.2s\n",
      "[CV]  n_estimators=150, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=3, total=   1.2s\n",
      "[CV] n_estimators=150, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=5 \n",
      "[CV] n_estimators=150, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=5 \n",
      "[CV] n_estimators=150, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=5 \n",
      "[CV]  n_estimators=150, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=5, total=   1.6s\n",
      "[CV]  n_estimators=150, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=5, total=   1.6s\n",
      "[CV] n_estimators=150, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=150, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=5, total=   1.6s\n",
      "[CV] n_estimators=150, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=10 \n",
      "[CV] n_estimators=150, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=150, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=10, total=   2.4s\n",
      "[CV]  n_estimators=150, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=10, total=   2.4s\n",
      "[CV]  n_estimators=150, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=10, total=   2.4s\n",
      "[CV] n_estimators=100, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=4 \n",
      "[CV] n_estimators=100, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=4 \n",
      "[CV] n_estimators=100, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=4 \n",
      "[CV]  n_estimators=100, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=4, total=   0.9s\n",
      "[CV]  n_estimators=100, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=4, total=   0.9s\n",
      "[CV] n_estimators=200, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=4 \n",
      "[CV]  n_estimators=100, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=4, total=   0.9s\n",
      "[CV] n_estimators=200, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=4 \n",
      "[CV] n_estimators=200, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=4 \n",
      "[CV]  n_estimators=200, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=4, total=   1.8s\n",
      "[CV]  n_estimators=200, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=4, total=   1.8s\n",
      "[CV] n_estimators=100, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=3 \n",
      "[CV] n_estimators=100, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=3 \n",
      "[CV]  n_estimators=200, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=4, total=   1.9s\n",
      "[CV] n_estimators=100, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=3 \n",
      "[CV]  n_estimators=100, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=3, total=   0.9s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=100, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=3, total=   0.9s\n",
      "[CV] n_estimators=300, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=5 \n",
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      "[CV]  n_estimators=100, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=3, total=   0.8s\n",
      "[CV] n_estimators=300, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=5 \n",
      "[CV]  n_estimators=300, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=5, total=   3.1s\n",
      "[CV] n_estimators=100, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=5 \n",
      "[CV]  n_estimators=300, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=5, total=   3.2s\n",
      "[CV] n_estimators=100, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=5 \n",
      "[CV]  n_estimators=300, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=5, total=   3.1s\n",
      "[CV] n_estimators=100, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=5 \n",
      "[CV]  n_estimators=100, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=5, total=   1.2s\n",
      "[CV] n_estimators=50, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=5 \n",
      "[CV]  n_estimators=100, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=5, total=   1.2s\n",
      "[CV] n_estimators=50, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=5 \n",
      "[CV]  n_estimators=100, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=5, total=   1.2s\n",
      "[CV] n_estimators=50, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=5 \n",
      "[CV]  n_estimators=50, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=5, total=   0.6s\n",
      "[CV] n_estimators=250, min_samples_split=5, min_samples_leaf=2, max_features=sqrt, max_depth=3 \n",
      "[CV]  n_estimators=50, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=5, total=   0.6s\n",
      "[CV] n_estimators=250, min_samples_split=5, min_samples_leaf=2, max_features=sqrt, max_depth=3 \n",
      "[CV]  n_estimators=50, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=5, total=   0.6s\n",
      "[CV] n_estimators=250, min_samples_split=5, min_samples_leaf=2, max_features=sqrt, max_depth=3 \n",
      "[CV]  n_estimators=250, min_samples_split=5, min_samples_leaf=2, max_features=sqrt, max_depth=3, total=   2.3s\n",
      "[CV] n_estimators=50, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=3 \n",
      "[CV]  n_estimators=250, min_samples_split=5, min_samples_leaf=2, max_features=sqrt, max_depth=3, total=   2.3s\n",
      "[CV]  n_estimators=250, min_samples_split=5, min_samples_leaf=2, max_features=sqrt, max_depth=3, total=   2.2s\n",
      "[CV] n_estimators=50, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=3 \n",
      "[CV] n_estimators=50, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=3 \n",
      "[CV]  n_estimators=50, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=3, total=   0.5s\n",
      "[CV]  n_estimators=50, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=3, total=   0.5s\n",
      "[CV] n_estimators=10, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=6 \n",
      "[CV]  n_estimators=50, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=3, total=   0.5s\n",
      "[CV] n_estimators=10, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=6 \n",
      "[CV] n_estimators=10, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=6 \n",
      "[CV]  n_estimators=10, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=6, total=   0.1s\n",
      "[CV]  n_estimators=10, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=6, total=   0.2s\n",
      "[CV] n_estimators=10, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=9 \n",
      "[CV]  n_estimators=10, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=6, total=   0.1s\n",
      "[CV] n_estimators=10, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=9 \n",
      "[CV] n_estimators=10, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=9 \n",
      "[CV]  n_estimators=10, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=9, total=   0.2s\n",
      "[CV]  n_estimators=10, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=9, total=   0.2s\n",
      "[CV] n_estimators=200, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=7 \n",
      "[CV]  n_estimators=10, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=9, total=   0.2s\n",
      "[CV] n_estimators=200, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=7 \n",
      "[CV] n_estimators=200, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=7 \n",
      "[CV]  n_estimators=200, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=7, total=   2.7s\n",
      "[CV]  n_estimators=200, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=7, total=   2.7s\n",
      "[CV]  n_estimators=200, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=7, total=   2.7s\n",
      "[CV] n_estimators=50, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=10 \n",
      "[CV] n_estimators=50, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=10 \n",
      "[CV] n_estimators=50, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=10 \n",
      "[CV]  n_estimators=50, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=10, total=   0.8s\n",
      "[CV]  n_estimators=50, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=10, total=   0.8s\n",
      "[CV] n_estimators=100, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=3 \n",
      "[CV]  n_estimators=50, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=10, total=   0.8s\n",
      "[CV] n_estimators=100, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=3 \n",
      "[CV] n_estimators=100, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=3 \n",
      "[CV]  n_estimators=100, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=3, total=   0.8s\n",
      "[CV] n_estimators=250, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=100, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=3, total=   0.9s\n",
      "[CV]  n_estimators=100, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=3, total=   0.8s\n",
      "[CV] n_estimators=250, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=10 \n",
      "[CV] n_estimators=250, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=250, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=10, total=   4.0s\n",
      "[CV]  n_estimators=250, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=10, total=   4.1s\n",
      "[CV]  n_estimators=250, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=10, total=   4.1s\n",
      "[CV] n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=None \n",
      "[CV] n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=None \n",
      "[CV] n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=None \n",
      "[CV]  n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=None, total=   4.6s\n",
      "[CV]  n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=None, total=   4.6s\n",
      "[CV]  n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=None, total=   4.6s\n",
      "[CV] n_estimators=100, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=6 \n",
      "[CV] n_estimators=100, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=6 \n",
      "[CV] n_estimators=100, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=6 \n",
      "[CV]  n_estimators=100, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=6, total=   1.2s\n",
      "[CV] n_estimators=10, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=7 \n",
      "[CV]  n_estimators=100, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=6, total=   1.1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=100, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=6, total=   1.2s\n",
      "[CV] n_estimators=10, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=7 \n",
      "[CV] n_estimators=10, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=7 \n",
      "[CV]  n_estimators=10, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=7, total=   0.2s\n",
      "[CV] n_estimators=100, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=5 \n",
      "[CV]  n_estimators=10, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=7, total=   0.2s\n",
      "[CV]  n_estimators=10, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=7, total=   0.2s\n",
      "[CV] n_estimators=100, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=5 \n",
      "[CV] n_estimators=100, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=5 \n",
      "[CV]  n_estimators=100, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=5, total=   1.1s\n",
      "[CV]  n_estimators=100, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=5, total=   1.1s\n",
      "[CV]  n_estimators=100, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=5, total=   1.1s\n",
      "[CV] n_estimators=100, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=6 \n",
      "[CV] n_estimators=100, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=6 \n",
      "[CV] n_estimators=100, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=6 \n",
      "[CV]  n_estimators=100, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=6, total=   1.2s\n",
      "[CV]  n_estimators=100, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=6, total=   1.2s\n",
      "[CV]  n_estimators=100, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=6, total=   1.2s\n",
      "[CV] n_estimators=300, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=10 \n",
      "[CV] n_estimators=300, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=10 \n",
      "[CV] n_estimators=300, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=10 \n",
      "[CV]  n_estimators=300, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=10, total=   4.9s\n",
      "[CV]  n_estimators=300, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=10, total=   4.9s\n",
      "[CV]  n_estimators=300, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=10, total=   4.9s\n",
      "[CV] n_estimators=150, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=4 \n",
      "[CV] n_estimators=150, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=4 \n",
      "[CV] n_estimators=150, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=4 \n",
      "[CV]  n_estimators=150, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=4, total=   1.4s\n",
      "[CV]  n_estimators=150, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=4, total=   1.4s\n",
      "[CV]  n_estimators=150, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=4, total=   1.4s\n",
      "[CV] n_estimators=250, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=9 \n",
      "[CV] n_estimators=250, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=9 \n",
      "[CV] n_estimators=250, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=9 \n",
      "[CV]  n_estimators=250, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=9, total=   4.0s\n",
      "[CV] n_estimators=10, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=None \n",
      "[CV]  n_estimators=250, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=9, total=   4.0s\n",
      "[CV]  n_estimators=250, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=9, total=   4.0s\n",
      "[CV] n_estimators=10, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=None \n",
      "[CV] n_estimators=10, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=None \n",
      "[CV]  n_estimators=10, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=None, total=   0.3s\n",
      "[CV] n_estimators=200, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=3 \n",
      "[CV]  n_estimators=10, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=None, total=   0.3s\n",
      "[CV]  n_estimators=10, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=None, total=   0.3s\n",
      "[CV] n_estimators=200, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=3 \n",
      "[CV] n_estimators=200, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=3 \n",
      "[CV]  n_estimators=200, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=3, total=   1.7s\n",
      "[CV]  n_estimators=200, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=3, total=   1.7s\n",
      "[CV] n_estimators=300, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=7 \n",
      "[CV]  n_estimators=200, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=3, total=   1.7s\n",
      "[CV] n_estimators=300, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=7 \n",
      "[CV] n_estimators=300, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=7 \n",
      "[CV]  n_estimators=300, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=7, total=   3.7s\n",
      "[CV]  n_estimators=300, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=7, total=   3.8s\n",
      "[CV] n_estimators=200, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=None \n",
      "[CV]  n_estimators=300, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=7, total=   3.8s\n",
      "[CV] n_estimators=200, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=None \n",
      "[CV] n_estimators=200, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=None \n",
      "[CV]  n_estimators=200, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=None, total=   5.0s\n",
      "[CV]  n_estimators=200, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=None, total=   5.0s\n",
      "[CV] n_estimators=300, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=8 \n",
      "[CV]  n_estimators=200, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=None, total=   5.0s\n",
      "[CV] n_estimators=300, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=8 \n",
      "[CV] n_estimators=300, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=8 \n",
      "[CV]  n_estimators=300, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=8, total=   4.1s\n",
      "[CV]  n_estimators=300, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=8, total=   4.1s\n",
      "[CV]  n_estimators=300, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=8, total=   4.1s\n",
      "[CV] n_estimators=250, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
      "[CV] n_estimators=250, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
      "[CV] n_estimators=250, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=250, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   2.6s\n",
      "[CV]  n_estimators=250, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   2.7s\n",
      "[CV]  n_estimators=250, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   2.6s\n",
      "[CV] n_estimators=10, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=6 \n",
      "[CV] n_estimators=10, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=6 \n",
      "[CV] n_estimators=10, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=6 \n",
      "[CV]  n_estimators=10, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=6, total=   0.1s\n",
      "[CV]  n_estimators=10, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=6, total=   0.1s\n",
      "[CV] n_estimators=150, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=4 \n",
      "[CV]  n_estimators=10, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=6, total=   0.1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] n_estimators=150, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=4 \n",
      "[CV] n_estimators=150, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=4 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=3)]: Done 156 tasks      | elapsed:  2.2min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=150, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=4, total=   1.4s\n",
      "[CV]  n_estimators=150, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=4, total=   1.4s\n",
      "[CV] n_estimators=100, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=150, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=4, total=   1.4s\n",
      "[CV] n_estimators=100, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=5 \n",
      "[CV] n_estimators=100, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=100, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=5, total=   1.1s\n",
      "[CV]  n_estimators=100, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=5, total=   1.1s\n",
      "[CV] n_estimators=150, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=9 \n",
      "[CV]  n_estimators=100, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=5, total=   1.1s\n",
      "[CV] n_estimators=150, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=9 \n",
      "[CV] n_estimators=150, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=9 \n",
      "[CV]  n_estimators=150, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=9, total=   2.2s\n",
      "[CV]  n_estimators=150, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=9, total=   2.2s\n",
      "[CV]  n_estimators=150, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=9, total=   2.2s\n",
      "[CV] n_estimators=10, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=3 \n",
      "[CV] n_estimators=10, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=3 \n",
      "[CV] n_estimators=10, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=3 \n",
      "[CV]  n_estimators=10, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=3, total=   0.1s\n",
      "[CV]  n_estimators=10, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=3, total=   0.1s\n",
      "[CV] n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=10 \n",
      "[CV] n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=10, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=3, total=   0.1s\n",
      "[CV] n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=10, total=   3.2s\n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=4, max_features=auto, max_depth=None \n",
      "[CV]  n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=10, total=   3.2s\n",
      "[CV]  n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=10, total=   3.2s\n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=4, max_features=auto, max_depth=None \n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=4, max_features=auto, max_depth=None \n",
      "[CV]  n_estimators=200, min_samples_split=10, min_samples_leaf=4, max_features=auto, max_depth=None, total=   4.5s\n",
      "[CV]  n_estimators=200, min_samples_split=10, min_samples_leaf=4, max_features=auto, max_depth=None, total=   4.5s\n",
      "[CV]  n_estimators=200, min_samples_split=10, min_samples_leaf=4, max_features=auto, max_depth=None, total=   4.5s\n",
      "[CV] n_estimators=150, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=4 \n",
      "[CV] n_estimators=150, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=4 \n",
      "[CV] n_estimators=150, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=4 \n",
      "[CV]  n_estimators=150, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=4, total=   1.7s\n",
      "[CV]  n_estimators=150, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=4, total=   1.7s\n",
      "[CV] n_estimators=100, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=7 \n",
      "[CV]  n_estimators=150, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=4, total=   1.7s\n",
      "[CV] n_estimators=100, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=7 \n",
      "[CV] n_estimators=100, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=7 \n",
      "[CV]  n_estimators=100, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=7, total=   1.4s\n",
      "[CV]  n_estimators=100, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=7, total=   1.4s\n",
      "[CV]  n_estimators=100, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=7, total=   1.4s\n",
      "[CV] n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=9 \n",
      "[CV] n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=9 \n",
      "[CV] n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=9 \n",
      "[CV]  n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=9, total=   3.4s\n",
      "[CV] n_estimators=10, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=None \n",
      "[CV]  n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=9, total=   3.4s\n",
      "[CV]  n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=9, total=   3.4s\n",
      "[CV] n_estimators=10, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=None \n",
      "[CV] n_estimators=10, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=None \n",
      "[CV]  n_estimators=10, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=None, total=   0.3s\n",
      "[CV] n_estimators=100, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=3 \n",
      "[CV]  n_estimators=10, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=None, total=   0.3s\n",
      "[CV]  n_estimators=10, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=None, total=   0.2s\n",
      "[CV] n_estimators=100, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=3 \n",
      "[CV] n_estimators=100, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=3 \n",
      "[CV]  n_estimators=100, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=3, total=   0.9s\n",
      "[CV] n_estimators=250, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=6 \n",
      "[CV]  n_estimators=100, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=3, total=   0.9s\n",
      "[CV]  n_estimators=100, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=3, total=   0.9s\n",
      "[CV] n_estimators=250, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=6 \n",
      "[CV] n_estimators=250, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=6 \n",
      "[CV]  n_estimators=250, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=6, total=   2.9s\n",
      "[CV]  n_estimators=250, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=6, total=   2.9s\n",
      "[CV]  n_estimators=250, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=6, total=   2.9s\n",
      "[CV] n_estimators=250, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=None \n",
      "[CV] n_estimators=250, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=None \n",
      "[CV] n_estimators=250, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=None \n",
      "[CV]  n_estimators=250, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=None, total=   5.9s\n",
      "[CV]  n_estimators=250, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=None, total=   5.9s\n",
      "[CV]  n_estimators=250, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=None, total=   6.0s\n",
      "[CV] n_estimators=250, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=10 \n",
      "[CV] n_estimators=250, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=10 \n",
      "[CV] n_estimators=250, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=250, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=10, total=   4.2s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] n_estimators=50, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=250, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=10, total=   4.2s\n",
      "[CV] n_estimators=50, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=250, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=10, total=   4.3s\n",
      "[CV] n_estimators=50, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=50, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=5, total=   0.5s\n",
      "[CV] n_estimators=250, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=7 \n",
      "[CV]  n_estimators=50, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=5, total=   0.5s\n",
      "[CV] n_estimators=250, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=7 \n",
      "[CV]  n_estimators=50, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=5, total=   0.5s\n",
      "[CV] n_estimators=250, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=7 \n",
      "[CV]  n_estimators=250, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=7, total=   3.2s\n",
      "[CV] n_estimators=300, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=6 \n",
      "[CV]  n_estimators=250, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=7, total=   3.2s\n",
      "[CV]  n_estimators=250, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=7, total=   3.2s\n",
      "[CV] n_estimators=300, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=6 \n",
      "[CV] n_estimators=300, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=6 \n",
      "[CV]  n_estimators=300, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=6, total=   3.6s\n",
      "[CV]  n_estimators=300, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=6, total=   3.6s\n",
      "[CV]  n_estimators=300, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=6, total=   3.6s\n",
      "[CV] n_estimators=50, min_samples_split=5, min_samples_leaf=2, max_features=sqrt, max_depth=7 \n",
      "[CV] n_estimators=50, min_samples_split=5, min_samples_leaf=2, max_features=sqrt, max_depth=7 \n",
      "[CV] n_estimators=50, min_samples_split=5, min_samples_leaf=2, max_features=sqrt, max_depth=7 \n",
      "[CV]  n_estimators=50, min_samples_split=5, min_samples_leaf=2, max_features=sqrt, max_depth=7, total=   0.7s\n",
      "[CV]  n_estimators=50, min_samples_split=5, min_samples_leaf=2, max_features=sqrt, max_depth=7, total=   0.7s\n",
      "[CV] n_estimators=250, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=7 \n",
      "[CV]  n_estimators=50, min_samples_split=5, min_samples_leaf=2, max_features=sqrt, max_depth=7, total=   0.7s\n",
      "[CV] n_estimators=250, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=7 \n",
      "[CV] n_estimators=250, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=7 \n",
      "[CV]  n_estimators=250, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=7, total=   3.2s\n",
      "[CV] n_estimators=300, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=250, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=7, total=   3.2s\n",
      "[CV]  n_estimators=250, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=7, total=   3.2s\n",
      "[CV] n_estimators=300, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=5 \n",
      "[CV] n_estimators=300, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=300, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=5, total=   3.2s\n",
      "[CV]  n_estimators=300, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=5, total=   3.1s\n",
      "[CV]  n_estimators=300, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=5, total=   3.2s\n",
      "[CV] n_estimators=200, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=5 \n",
      "[CV] n_estimators=200, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=5 \n",
      "[CV] n_estimators=200, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=200, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=5, total=   2.1s\n",
      "[CV] n_estimators=300, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=6 \n",
      "[CV]  n_estimators=200, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=5, total=   2.1s\n",
      "[CV]  n_estimators=200, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=5, total=   2.2s\n",
      "[CV] n_estimators=300, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=6 \n",
      "[CV] n_estimators=300, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=6 \n",
      "[CV]  n_estimators=300, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=6, total=   3.7s\n",
      "[CV]  n_estimators=300, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=6, total=   3.7s\n",
      "[CV] n_estimators=300, min_samples_split=10, min_samples_leaf=4, max_features=sqrt, max_depth=7 \n",
      "[CV] n_estimators=300, min_samples_split=10, min_samples_leaf=4, max_features=sqrt, max_depth=7 \n",
      "[CV]  n_estimators=300, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=6, total=   3.7s\n",
      "[CV] n_estimators=300, min_samples_split=10, min_samples_leaf=4, max_features=sqrt, max_depth=7 \n",
      "[CV]  n_estimators=300, min_samples_split=10, min_samples_leaf=4, max_features=sqrt, max_depth=7, total=   4.0s\n",
      "[CV]  n_estimators=300, min_samples_split=10, min_samples_leaf=4, max_features=sqrt, max_depth=7, total=   4.0s\n",
      "[CV]  n_estimators=300, min_samples_split=10, min_samples_leaf=4, max_features=sqrt, max_depth=7, total=   4.0s\n",
      "[CV] n_estimators=300, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
      "[CV] n_estimators=300, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
      "[CV] n_estimators=300, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=300, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   3.4s\n",
      "[CV]  n_estimators=300, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   3.3s\n",
      "[CV] n_estimators=300, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=4 \n",
      "[CV]  n_estimators=300, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   3.4s\n",
      "[CV] n_estimators=300, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=4 \n",
      "[CV] n_estimators=300, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=4 \n",
      "[CV]  n_estimators=300, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=4, total=   2.9s\n",
      "[CV]  n_estimators=300, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=4, total=   2.9s\n",
      "[CV] n_estimators=150, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=8 \n",
      "[CV] n_estimators=150, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=8 \n",
      "[CV]  n_estimators=300, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=4, total=   2.9s\n",
      "[CV] n_estimators=150, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=8 \n",
      "[CV]  n_estimators=150, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=8, total=   2.1s\n",
      "[CV] n_estimators=250, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=None \n",
      "[CV]  n_estimators=150, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=8, total=   2.1s\n",
      "[CV] n_estimators=250, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=None \n",
      "[CV]  n_estimators=150, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=8, total=   2.1s\n",
      "[CV] n_estimators=250, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=None \n",
      "[CV]  n_estimators=250, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=None, total=   5.9s\n",
      "[CV] n_estimators=100, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=4 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=250, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=None, total=   5.9s\n",
      "[CV]  n_estimators=250, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=None, total=   5.9s\n",
      "[CV] n_estimators=100, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=4 \n",
      "[CV] n_estimators=100, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=4 \n",
      "[CV]  n_estimators=100, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=4, total=   1.0s\n",
      "[CV] n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=5 \n",
      "[CV]  n_estimators=100, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=4, total=   1.0s\n",
      "[CV]  n_estimators=100, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=4, total=   1.0s\n",
      "[CV] n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=5 \n",
      "[CV] n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=5 \n",
      "[CV]  n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=5, total=   2.2s\n",
      "[CV] n_estimators=150, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=3 \n",
      "[CV]  n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=5, total=   2.2s\n",
      "[CV]  n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=5, total=   2.2s\n",
      "[CV] n_estimators=150, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=3 \n",
      "[CV] n_estimators=150, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=3 \n",
      "[CV]  n_estimators=150, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=3, total=   1.3s\n",
      "[CV] n_estimators=150, min_samples_split=5, min_samples_leaf=2, max_features=sqrt, max_depth=4 \n",
      "[CV]  n_estimators=150, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=3, total=   1.3s\n",
      "[CV]  n_estimators=150, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=3, total=   1.3s\n",
      "[CV] n_estimators=150, min_samples_split=5, min_samples_leaf=2, max_features=sqrt, max_depth=4 \n",
      "[CV] n_estimators=150, min_samples_split=5, min_samples_leaf=2, max_features=sqrt, max_depth=4 \n",
      "[CV]  n_estimators=150, min_samples_split=5, min_samples_leaf=2, max_features=sqrt, max_depth=4, total=   1.5s\n",
      "[CV] n_estimators=50, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=None \n",
      "[CV]  n_estimators=150, min_samples_split=5, min_samples_leaf=2, max_features=sqrt, max_depth=4, total=   1.5s\n",
      "[CV]  n_estimators=150, min_samples_split=5, min_samples_leaf=2, max_features=sqrt, max_depth=4, total=   1.5s\n",
      "[CV] n_estimators=50, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=None \n",
      "[CV] n_estimators=50, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=None \n",
      "[CV]  n_estimators=50, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=None, total=   1.5s\n",
      "[CV] n_estimators=150, min_samples_split=10, min_samples_leaf=4, max_features=auto, max_depth=8 \n",
      "[CV]  n_estimators=50, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=None, total=   1.5s\n",
      "[CV]  n_estimators=50, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=None, total=   1.5s\n",
      "[CV] n_estimators=150, min_samples_split=10, min_samples_leaf=4, max_features=auto, max_depth=8 \n",
      "[CV] n_estimators=150, min_samples_split=10, min_samples_leaf=4, max_features=auto, max_depth=8 \n",
      "[CV]  n_estimators=150, min_samples_split=10, min_samples_leaf=4, max_features=auto, max_depth=8, total=   2.1s\n",
      "[CV] n_estimators=10, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=10 \n",
      "[CV]  n_estimators=150, min_samples_split=10, min_samples_leaf=4, max_features=auto, max_depth=8, total=   2.1s\n",
      "[CV]  n_estimators=150, min_samples_split=10, min_samples_leaf=4, max_features=auto, max_depth=8, total=   2.1s\n",
      "[CV] n_estimators=10, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=10 \n",
      "[CV] n_estimators=10, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=10 \n",
      "[CV]  n_estimators=10, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=10, total=   0.2s\n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=8 \n",
      "[CV]  n_estimators=10, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=10, total=   0.2s\n",
      "[CV]  n_estimators=10, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=10, total=   0.2s\n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=8 \n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=8 \n",
      "[CV]  n_estimators=200, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=8, total=   2.8s\n",
      "[CV] n_estimators=10, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=4 \n",
      "[CV]  n_estimators=200, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=8, total=   2.7s\n",
      "[CV] n_estimators=10, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=4 \n",
      "[CV]  n_estimators=200, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=8, total=   2.8s\n",
      "[CV] n_estimators=10, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=4 \n",
      "[CV]  n_estimators=10, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=4, total=   0.1s\n",
      "[CV] n_estimators=300, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=4 \n",
      "[CV]  n_estimators=10, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=4, total=   0.1s\n",
      "[CV]  n_estimators=10, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=4, total=   0.1s\n",
      "[CV] n_estimators=300, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=4 \n",
      "[CV] n_estimators=300, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=4 \n",
      "[CV]  n_estimators=300, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=4, total=   2.8s\n",
      "[CV] n_estimators=250, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=5 \n",
      "[CV]  n_estimators=300, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=4, total=   2.8s\n",
      "[CV] n_estimators=250, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=5 \n",
      "[CV]  n_estimators=300, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=4, total=   2.9s\n",
      "[CV] n_estimators=250, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=5 \n",
      "[CV]  n_estimators=250, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=5, total=   2.6s\n",
      "[CV]  n_estimators=250, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=5, total=   2.6s\n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=5 \n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=250, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=5, total=   2.6s\n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=200, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=5, total=   2.1s\n",
      "[CV]  n_estimators=200, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=5, total=   2.1s\n",
      "[CV] n_estimators=100, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=9 \n",
      "[CV] n_estimators=100, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=9 \n",
      "[CV]  n_estimators=200, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=5, total=   2.1s\n",
      "[CV] n_estimators=100, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=9 \n",
      "[CV]  n_estimators=100, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=9, total=   1.5s\n",
      "[CV]  n_estimators=100, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=9, total=   1.5s\n",
      "[CV] n_estimators=100, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=5 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] n_estimators=100, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=100, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=9, total=   1.5s\n",
      "[CV] n_estimators=100, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=100, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=5, total=   1.1s\n",
      "[CV]  n_estimators=100, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=5, total=   1.1s\n",
      "[CV] n_estimators=10, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=4 \n",
      "[CV] n_estimators=10, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=4 \n",
      "[CV]  n_estimators=100, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=5, total=   1.1s\n",
      "[CV] n_estimators=10, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=4 \n",
      "[CV]  n_estimators=10, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=4, total=   0.1s\n",
      "[CV] n_estimators=250, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=7 \n",
      "[CV]  n_estimators=10, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=4, total=   0.1s\n",
      "[CV] n_estimators=250, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=7 \n",
      "[CV]  n_estimators=10, min_samples_split=10, min_samples_leaf=1, max_features=auto, max_depth=4, total=   0.1s\n",
      "[CV] n_estimators=250, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=7 \n",
      "[CV]  n_estimators=250, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=7, total=   3.2s\n",
      "[CV] n_estimators=100, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=6 \n",
      "[CV]  n_estimators=250, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=7, total=   3.2s\n",
      "[CV]  n_estimators=250, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=7, total=   3.2s\n",
      "[CV] n_estimators=100, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=6 \n",
      "[CV] n_estimators=100, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=6 \n",
      "[CV]  n_estimators=100, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=6, total=   1.2s\n",
      "[CV]  n_estimators=100, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=6, total=   1.2s\n",
      "[CV] n_estimators=50, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=5 \n",
      "[CV]  n_estimators=100, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=6, total=   1.2s\n",
      "[CV] n_estimators=50, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=5 \n",
      "[CV] n_estimators=50, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=5 \n",
      "[CV]  n_estimators=50, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=5, total=   0.5s\n",
      "[CV] n_estimators=150, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=50, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=5, total=   0.5s\n",
      "[CV]  n_estimators=50, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=5, total=   0.5s\n",
      "[CV] n_estimators=150, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=5 \n",
      "[CV] n_estimators=150, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=150, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=5, total=   1.6s\n",
      "[CV] n_estimators=50, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=150, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=5, total=   1.6s\n",
      "[CV]  n_estimators=150, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=5, total=   1.6s\n",
      "[CV] n_estimators=50, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=10 \n",
      "[CV] n_estimators=50, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=50, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=10, total=   0.9s\n",
      "[CV]  n_estimators=50, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=10, total=   0.8s\n",
      "[CV]  n_estimators=50, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=10, total=   0.8s\n",
      "[CV] n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=5 \n",
      "[CV] n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=5 \n",
      "[CV] n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=5, total=   2.1s\n",
      "[CV]  n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=5, total=   2.1s\n",
      "[CV]  n_estimators=200, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=5, total=   2.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=3)]: Done 300 out of 300 | elapsed:  4.3min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, error_score='raise',\n",
       "          estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=42, verbose=0, warm_start=False),\n",
       "          fit_params=None, iid=True, n_iter=100, n_jobs=3,\n",
       "          param_distributions={'n_estimators': [10, 50, 100, 150, 200, 250, 300], 'max_features': ['auto', 'sqrt'], 'max_depth': [3, 4, 5, 6, 7, 8, 9, 10, None], 'min_samples_split': [2, 5, 10], 'min_samples_leaf': [1, 2, 4]},\n",
       "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "          return_train_score='warn',\n",
       "          scoring=make_scorer(fbeta_score, beta=0.5), verbose=2)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# instantiate a random forest classifier obj\n",
    "rf_clf = RandomForestClassifier(random_state=42)\n",
    "\n",
    "# Number of trees in random forest\n",
    "n_estimators = [10, 50, 100, 150, 200, 250, 300]\n",
    "\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.arange(3, 11)]\n",
    "max_depth.append(None)\n",
    "\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 10]\n",
    "\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 4]\n",
    "\n",
    "# Create the random grid\n",
    "grid_params = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf}\n",
    "\n",
    "# tune the classifer\n",
    "rf_random = RandomizedSearchCV(estimator = rf_clf,\n",
    "                               param_distributions = grid_params,\n",
    "                               scoring=scorer,\n",
    "                               n_iter = 100,\n",
    "                               cv = 3,\n",
    "                               verbose=2,\n",
    "                               random_state=42,\n",
    "                               n_jobs = 3)\n",
    "\n",
    "# fit train data to the classifier\n",
    "rf_random.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier model accuracy: 0.763\n",
      "RandomForestClassifier model f1-score: 0.753\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.7625563909774437, 0.7530552514578074)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# evaluate random forest classifier model's performance\n",
    "evaluate_model_performance(rf_random.best_estimator_, X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Results\n",
    "- Results suggest that a random forest model's accuracy and f1-score is better than the naive predictor\n",
    "- Accuracy\n",
    "    - Naive predictor: 0.471\n",
    "    - Random forest: 0.762\n",
    "- F1-score\n",
    "    - Naive predictor: 0.640\n",
    "    - Random forest: 0.753"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate Test Data Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier model accuracy: 0.740\n",
      "RandomForestClassifier model f1-score: 0.730\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.7396621723221893, 0.7303219106957425)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate_model_performance(rf_random.best_estimator_, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NOTE: Few things to consider while constructing these models - all features were converted to numericals to fit and train above models. Bias and variance are two characteristics of a machine learning model. Bias refers to inherent model assumptions regarding the decision boundary between different classes. On the other hand, variance refers a model's sensitivity to changes in its inputs. These can influence our results sometimes so models have to be tested throughly against bias and variance. Also, while splitting train and test datasets and tuning parameters to fit a model, we will have to make sure that data doesn't overfit the model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The problem that I chose to solve was to build a model that predicts whether a customer will respond to an offer. My strategy for solving this problem has mainly two steps. First, I combined offer portfolio, customer profile, and transaction data. Second, I assessed the accuracy and F1-score of a naive model that assumes all offers were successful. Third, I compared the performance of logistic regression and random forest models. This analysis suggests that a random forest model has the best training data accuracy and F1-score. Analysis suggests that random forest model has a training data accuracy of 0.762 and an F1-score of 0.753. The test data set accuracy of 0.740 and F1-score of 0.730 suggests that the random forest model I constructed did not overfit the training data.\n",
    "\n",
    "However, the performance of a random forest model can be still improved by analysing features which impacts an offer’s success rate as a function of offer difficulty, duration, and reward. These additional features should provide a random forest classifier with the opportunity to construct a better decision boundary that separates successful and unsuccessful customer offers.\n",
    "\n",
    "Also, initially it seemed like we had a lot of data to work, but once NaN values and duplicate columns were dropped and the data were combined into one single dataset, it felt as though the models might have benefited from more data. With more data, the classification models may have been able to produce better accuracy and F1-score results.\n",
    "\n",
    "Additionally, better predictions may have been deducted if there were more customer metrics. For this analysis, I feel we had limited information about customer available to us — just age, gender, and income. To find optimal customer demographics, it would be nice to have a few more features of a customer. These additional features may aid in providing better classification model results."
   ]
  }
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