demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Churn Prediction for Direct Debit \n",
    "\n",
    "The task is to create a model that predicts which companies are likely to churn at the start of 2015, given a dataset of 2014. The dataset contains the following columns:\n",
    "\n",
    "- `company_id` : anonymised unique ID for each company\n",
    "- `vertical`: a category that indicates the industry this company is part of\n",
    "- `incorporation_date`: date (YYYY-MM-DD) when this company has been set up\n",
    "- `2014-MM-01_payments`: aggregated count of payments for each company in the\n",
    "specified month (MM)\n",
    "- `2014-MM-01_mandates`: aggregated count of unique mandates for each\n",
    "company in the specified month (MM). A mandate is an authorisation that allows a company to collect Direct Debit payments from a given customer (not every mandate has been used to collect payments)\n",
    "\n",
    "# Outline\n",
    "\n",
    "This notebook presents the following outline:\n",
    "\n",
    "1) **[Data quality checks](#Data-Quality-checks)** \n",
    "\n",
    "2) **[Exploratory Data Analysis (EDA)](#Exploratory-Data-Analysis-%28EDA%29 )** \n",
    "\n",
    "* Payments and Mandates\n",
    "* Relationships wrt to time\n",
    "* Segmentations by vertical\n",
    "\n",
    "3) **[Churn Model](#Churn-Model)**\n",
    "\n",
    "* [Challenges](#Challenges)\n",
    "    * Nature of churn\n",
    "    * Data related\n",
    "    * Choosing a model\n",
    "\n",
    "* [Survival Analysis](#Survival-Analysis)\n",
    "    * Kaplan-Meier model\n",
    "        * Calculating Duration\n",
    "        * Visualisation of churn\n",
    "        * Train-test spli\n",
    "        * KM curves by vertical\n",
    "        * Statistical Significance\n",
    "        * Prioritising at-risk customers\n",
    "        \n",
    "        \n",
    "4) **[Ideas for Future Work](#Ideas-for-Future-work)**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plotting imports\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "_ = sns.set(style=\"whitegrid\", rc={\"figure.figsize\": (12, 6),\n",
    "#                                \"legend.fontsize\": \"large\",\n",
    "                               \"axes.titlesize\": \"large\",\n",
    "                               \"xtick.labelsize\": \"large\",\n",
    "                               \"ytick.labelsize\": \"large\",\n",
    "                               })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from survival_analysis import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company_id</th>\n",
       "      <th>2014-01-01_payments</th>\n",
       "      <th>2014-02-01_payments</th>\n",
       "      <th>2014-03-01_payments</th>\n",
       "      <th>2014-04-01_payments</th>\n",
       "      <th>2014-05-01_payments</th>\n",
       "      <th>2014-06-01_payments</th>\n",
       "      <th>2014-07-01_payments</th>\n",
       "      <th>2014-08-01_payments</th>\n",
       "      <th>2014-09-01_payments</th>\n",
       "      <th>...</th>\n",
       "      <th>2014-05-01_mandates</th>\n",
       "      <th>2014-06-01_mandates</th>\n",
       "      <th>2014-07-01_mandates</th>\n",
       "      <th>2014-08-01_mandates</th>\n",
       "      <th>2014-09-01_mandates</th>\n",
       "      <th>2014-10-01_mandates</th>\n",
       "      <th>2014-11-01_mandates</th>\n",
       "      <th>2014-12-01_mandates</th>\n",
       "      <th>vertical</th>\n",
       "      <th>incorporation_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <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>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>gym/fitness</td>\n",
       "      <td>2013-05-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <td>0.0</td>\n",
       "      <td>gym/fitness</td>\n",
       "      <td>2003-09-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>freelance developer</td>\n",
       "      <td>2008-10-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>gym/fitness</td>\n",
       "      <td>2005-06-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>accountancy</td>\n",
       "      <td>2013-09-18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   company_id  2014-01-01_payments  2014-02-01_payments  2014-03-01_payments  \\\n",
       "0           1                  0.0                  0.0                  0.0   \n",
       "1           2                  1.0                  1.0                  3.0   \n",
       "2           3                  6.0                  6.0                  6.0   \n",
       "3           4                  8.0                  4.0                  7.0   \n",
       "4           5                  0.0                  0.0                 39.0   \n",
       "\n",
       "   2014-04-01_payments  2014-05-01_payments  2014-06-01_payments  \\\n",
       "0                  1.0                  0.0                  1.0   \n",
       "1                  2.0                  2.0                  1.0   \n",
       "2                  6.0                  6.0                  7.0   \n",
       "3                  7.0                  1.0                  2.0   \n",
       "4                  0.0                 54.0                  0.0   \n",
       "\n",
       "   2014-07-01_payments  2014-08-01_payments  2014-09-01_payments  \\\n",
       "0                  0.0                  0.0                  0.0   \n",
       "1                  1.0                  1.0                  1.0   \n",
       "2                  8.0                  7.0                  8.0   \n",
       "3                  2.0                  4.0                  3.0   \n",
       "4                  0.0                 22.0                  0.0   \n",
       "\n",
       "          ...          2014-05-01_mandates  2014-06-01_mandates  \\\n",
       "0         ...                          0.0                  0.0   \n",
       "1         ...                          0.0                  0.0   \n",
       "2         ...                          2.0                  2.0   \n",
       "3         ...                          0.0                  0.0   \n",
       "4         ...                          8.0                  7.0   \n",
       "\n",
       "   2014-07-01_mandates  2014-08-01_mandates  2014-09-01_mandates  \\\n",
       "0                  0.0                  0.0                  0.0   \n",
       "1                  0.0                  0.0                  0.0   \n",
       "2                  0.0                  0.0                  0.0   \n",
       "3                  0.0                  0.0                  0.0   \n",
       "4                  0.0                  0.0                  0.0   \n",
       "\n",
       "   2014-10-01_mandates  2014-11-01_mandates  2014-12-01_mandates  \\\n",
       "0                  0.0                  0.0                  0.0   \n",
       "1                  0.0                  1.0                  0.0   \n",
       "2                  0.0                  0.0                  0.0   \n",
       "3                  0.0                  0.0                  0.0   \n",
       "4                  0.0                  0.0                  0.0   \n",
       "\n",
       "              vertical  incorporation_date  \n",
       "0          gym/fitness          2013-05-30  \n",
       "1          gym/fitness          2003-09-25  \n",
       "2  freelance developer          2008-10-22  \n",
       "3          gym/fitness          2005-06-28  \n",
       "4          accountancy          2013-09-18  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = get_data()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Quality checks\n",
    "\n",
    "Some very basic checks; in practice this part should be more thorough!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "company_id             0\n",
       "2014-01-01_payments    0\n",
       "2014-02-01_payments    0\n",
       "2014-03-01_payments    0\n",
       "2014-04-01_payments    0\n",
       "2014-05-01_payments    0\n",
       "2014-06-01_payments    0\n",
       "2014-07-01_payments    0\n",
       "2014-08-01_payments    0\n",
       "2014-09-01_payments    0\n",
       "2014-10-01_payments    0\n",
       "2014-11-01_payments    0\n",
       "2014-12-01_payments    0\n",
       "2014-01-01_mandates    0\n",
       "2014-02-01_mandates    0\n",
       "2014-03-01_mandates    0\n",
       "2014-04-01_mandates    0\n",
       "2014-05-01_mandates    0\n",
       "2014-06-01_mandates    0\n",
       "2014-07-01_mandates    0\n",
       "2014-08-01_mandates    0\n",
       "2014-09-01_mandates    0\n",
       "2014-10-01_mandates    0\n",
       "2014-11-01_mandates    0\n",
       "2014-12-01_mandates    0\n",
       "vertical               0\n",
       "incorporation_date     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing values across columns\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nice to see no missing values :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for duplicates\n",
    "df['company_id'].duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No duplicates either"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a227808d0>"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HgAFQe7ZdOw426gtnk9pdhsJCQ3RreoxmZcVqVEK4t8cDAFyn6dOnX/Z4n7dW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\nAAAgaBGGAQAAELQIwwAAAAha/z/P+u1E75zxMQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a22638be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check company_id is continuous\n",
    "df['company_id'].head(30).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some filtering seems to be present on the `company_id`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis (EDA)\n",
    "\n",
    "* For now we use the full dataset to do the exploration of this dataset, however under real conditions this should be done only on the train set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Payments and Mandates\n",
    "\n",
    "Let's look at the relationship between payments and mandates for a sample of companies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define payments and mandates columns\n",
    "cols_payments = [x for x in df if 'payments' in x]\n",
    "cols_mandates = [x for x in df if 'mandates' in x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1575b860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore their relationship for a sample of companies\n",
    "temp = df.sample(5, random_state=0)\n",
    "payments = temp[cols_payments].T\n",
    "payments.columns = [str(x) + '_payments' for x in payments.columns]\n",
    "mandates = temp[cols_mandates].T\n",
    "mandates.columns = [str(x) + '_mandates' for x in mandates.columns]\n",
    "ax = payments.plot(subplots=True, sharex=True, marker='o')\n",
    "ax = mandates.plot(subplots=True, ax=ax, rot=40)\n",
    "xticklabels = [x.split('_')[0] for x in cols_payments]\n",
    "_ = ax[-1].set_xticklabels(xticklabels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Mandates can be non-zero even when payments are zero\n",
    "* In the case of company 247 for example, where payments were always zero while mandates non-zero, could this be an indication of fraud?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Aggregation over all companies per month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1a2567b1d0>"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HRGc+wp9PriIq8yGiMh/CTL0rPE0HorumZbvdf2FhjCHlZTqCU8IRxovEy7I3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\nEElEQVQkNBbERERERCS0/w8jJc2+nA/ruAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a25648c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get mean number of payments vs mandates per month\n",
    "mandates = df[cols_mandates].T\n",
    "mandates.mean(axis=1).plot(rot=60, label='mandates', marker='o')\n",
    "payments = df[cols_payments].T\n",
    "ax = payments.mean(axis=1).plot(rot=60, label='payments', marker='o')\n",
    "ax.set_xticklabels(xticklabels)\n",
    "plt.legend()\n",
    "plt.title('Mean number of payments vs mandates per month')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2014-01-01_payments</th>\n",
       "      <th>2014-02-01_payments</th>\n",
       "      <th>2014-03-01_payments</th>\n",
       "      <th>2014-04-01_payments</th>\n",
       "      <th>2014-05-01_payments</th>\n",
       "      <th>2014-06-01_payments</th>\n",
       "      <th>2014-07-01_payments</th>\n",
       "      <th>2014-08-01_payments</th>\n",
       "      <th>2014-09-01_payments</th>\n",
       "      <th>2014-10-01_payments</th>\n",
       "      <th>2014-11-01_payments</th>\n",
       "      <th>2014-12-01_payments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>902.000000</td>\n",
       "      <td>902.000000</td>\n",
       "      <td>902.000000</td>\n",
       "      <td>902.000000</td>\n",
       "      <td>902.000000</td>\n",
       "      <td>902.000000</td>\n",
       "      <td>902.000000</td>\n",
       "      <td>902.000000</td>\n",
       "      <td>902.000000</td>\n",
       "      <td>902.000000</td>\n",
       "      <td>902.000000</td>\n",
       "      <td>902.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15.097561</td>\n",
       "      <td>16.037694</td>\n",
       "      <td>17.176275</td>\n",
       "      <td>24.080931</td>\n",
       "      <td>19.745011</td>\n",
       "      <td>19.865854</td>\n",
       "      <td>22.962306</td>\n",
       "      <td>20.892461</td>\n",
       "      <td>26.838137</td>\n",
       "      <td>27.863636</td>\n",
       "      <td>25.458980</td>\n",
       "      <td>27.323725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>64.310562</td>\n",
       "      <td>62.706482</td>\n",
       "      <td>70.075550</td>\n",
       "      <td>115.130265</td>\n",
       "      <td>68.920850</td>\n",
       "      <td>73.631569</td>\n",
       "      <td>87.531283</td>\n",
       "      <td>84.468913</td>\n",
       "      <td>96.521500</td>\n",
       "      <td>105.180086</td>\n",
       "      <td>99.606663</td>\n",
       "      <td>113.165793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>15.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1240.000000</td>\n",
       "      <td>1164.000000</td>\n",
       "      <td>1327.000000</td>\n",
       "      <td>2549.000000</td>\n",
       "      <td>1178.000000</td>\n",
       "      <td>1184.000000</td>\n",
       "      <td>1291.000000</td>\n",
       "      <td>1345.000000</td>\n",
       "      <td>1399.000000</td>\n",
       "      <td>1505.000000</td>\n",
       "      <td>1680.000000</td>\n",
       "      <td>1764.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       2014-01-01_payments  2014-02-01_payments  2014-03-01_payments  \\\n",
       "count           902.000000           902.000000           902.000000   \n",
       "mean             15.097561            16.037694            17.176275   \n",
       "std              64.310562            62.706482            70.075550   \n",
       "min               0.000000             0.000000             0.000000   \n",
       "25%               0.000000             0.000000             0.000000   \n",
       "50%               1.000000             2.000000             2.000000   \n",
       "75%               8.000000            10.000000            11.000000   \n",
       "max            1240.000000          1164.000000          1327.000000   \n",
       "\n",
       "       2014-04-01_payments  2014-05-01_payments  2014-06-01_payments  \\\n",
       "count           902.000000           902.000000           902.000000   \n",
       "mean             24.080931            19.745011            19.865854   \n",
       "std             115.130265            68.920850            73.631569   \n",
       "min               0.000000             0.000000             0.000000   \n",
       "25%               0.000000             1.000000             0.000000   \n",
       "50%               3.000000             3.000000             2.000000   \n",
       "75%              13.000000            13.000000            12.000000   \n",
       "max            2549.000000          1178.000000          1184.000000   \n",
       "\n",
       "       2014-07-01_payments  2014-08-01_payments  2014-09-01_payments  \\\n",
       "count           902.000000           902.000000           902.000000   \n",
       "mean             22.962306            20.892461            26.838137   \n",
       "std              87.531283            84.468913            96.521500   \n",
       "min               0.000000             0.000000             0.000000   \n",
       "25%               0.000000             0.000000             0.000000   \n",
       "50%               2.000000             2.000000             4.000000   \n",
       "75%              14.000000            12.000000            16.000000   \n",
       "max            1291.000000          1345.000000          1399.000000   \n",
       "\n",
       "       2014-10-01_payments  2014-11-01_payments  2014-12-01_payments  \n",
       "count           902.000000           902.000000           902.000000  \n",
       "mean             27.863636            25.458980            27.323725  \n",
       "std             105.180086            99.606663           113.165793  \n",
       "min               0.000000             0.000000             0.000000  \n",
       "25%               0.000000             0.000000             0.000000  \n",
       "50%               3.000000             2.000000             2.000000  \n",
       "75%              16.000000            14.000000            15.750000  \n",
       "max            1505.000000          1680.000000          1764.000000  "
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get descriptive stats for payments\n",
    "df[cols_payments].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Payments seem to have increased over 2014 i.e. there's an upwards trend\n",
    "* Mandates on the other hand have kept pretty static and are average lower than payments accross all companies\n",
    "* The spread of number of payments (std dev) also has increased\n",
    "* There seems to be some **outliers** in the distribution telling from the 75% quantile vs the max - these should have been investigated and removed if applicable, but due to short time we ignored them...\n",
    "* Hard to infer any correlation between mandates and payments given this sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Total payments per company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "temp = df.copy()\n",
    "temp['total_payments'] = temp[cols_payments].sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      902.000000\n",
       "mean       263.342572\n",
       "std        944.140773\n",
       "min          1.000000\n",
       "25%         12.000000\n",
       "50%         41.000000\n",
       "75%        155.500000\n",
       "max      14380.000000\n",
       "Name: total_payments, dtype: float64"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp['total_payments'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 75% of companies had less than 155 payments in 2014\n",
    "* There seems to be some large tails in the data, which can be see in the histogram below (one company had a max of 14,380 total payments). Let's investigate this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a266576d8>"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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77a4HAAAAaPN8DtkbN27U4MGDNWvWLH3xxRd21wUAAAC0Wf6+bJScnKzk5GTV19frww8/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\nAAAAgGH/H57z+ZrjQ2MJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a266747b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp['total_payments'].plot(kind='hist', bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company_id</th>\n",
       "      <th>vertical</th>\n",
       "      <th>incorporation_date</th>\n",
       "      <th>2014-01-01_payments</th>\n",
       "      <th>2014-02-01_payments</th>\n",
       "      <th>2014-03-01_payments</th>\n",
       "      <th>2014-04-01_payments</th>\n",
       "      <th>2014-05-01_payments</th>\n",
       "      <th>2014-06-01_payments</th>\n",
       "      <th>2014-07-01_payments</th>\n",
       "      <th>2014-08-01_payments</th>\n",
       "      <th>2014-09-01_payments</th>\n",
       "      <th>2014-10-01_payments</th>\n",
       "      <th>2014-11-01_payments</th>\n",
       "      <th>2014-12-01_payments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>456</td>\n",
       "      <td>gym/fitness</td>\n",
       "      <td>2007-09-24</td>\n",
       "      <td>876.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>1044.0</td>\n",
       "      <td>1096.0</td>\n",
       "      <td>1178.0</td>\n",
       "      <td>1168.0</td>\n",
       "      <td>1257.0</td>\n",
       "      <td>1214.0</td>\n",
       "      <td>1353.0</td>\n",
       "      <td>1431.0</td>\n",
       "      <td>1385.0</td>\n",
       "      <td>1508.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     company_id     vertical incorporation_date  2014-01-01_payments  \\\n",
       "415         456  gym/fitness         2007-09-24                876.0   \n",
       "\n",
       "     2014-02-01_payments  2014-03-01_payments  2014-04-01_payments  \\\n",
       "415                870.0               1044.0               1096.0   \n",
       "\n",
       "     2014-05-01_payments  2014-06-01_payments  2014-07-01_payments  \\\n",
       "415               1178.0               1168.0               1257.0   \n",
       "\n",
       "     2014-08-01_payments  2014-09-01_payments  2014-10-01_payments  \\\n",
       "415               1214.0               1353.0               1431.0   \n",
       "\n",
       "     2014-11-01_payments  2014-12-01_payments  \n",
       "415               1385.0               1508.0  "
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[temp['total_payments'] == temp['total_payments'].max()][cols_chars + cols_payments]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Seems be a genuine entry, not a data problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relationships wrt to time\n",
    "\n",
    "<span style=\"color:red\">WARNING: as this is only a subset of data, not necessarily sorted by `incorporation_date`, the below observations in this section could be biased! The below was just intended as a demonstration of the type of analysis I would do given the adequate dataset</span>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Payments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first look across all companies, how the payments evolve with time - this could be interprete as the average GC revenue over time (assuming GC revenue is proportional to number of payments processed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_payments</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>incorporation_date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-05-30</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-09-25</th>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-10-22</th>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-06-28</th>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-18</th>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    total_payments\n",
       "incorporation_date                \n",
       "2013-05-30                     2.0\n",
       "2003-09-25                    23.0\n",
       "2008-10-22                    86.0\n",
       "2005-06-28                    57.0\n",
       "2013-09-18                   115.0"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.index = temp['incorporation_date']\n",
    "temp[['total_payments']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1a20737470>"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P9BnodDrcf//9+Oyzz7Bz584Gtd/r2rUrTp8+Lb8fXbp0wb59+/DWW29F9fyaJ0R/+ctf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\nxhhjjDGmpv8H/EWrHIwDxJkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a206c9e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp['total_payments'].resample('M').mean().plot()\n",
    "plt.title('Mean number of payments per month timeline')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Only seasonal pattern observed; no clear growth/decline observed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1a20995b00>"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uW4vMc+adtW8oSTP84e3hiqO5VdB268f9/KbGZJmIiIj6Ka5ohiAAM6cMXYJh\nFDvVBz4ervj6bA10dtQFwVb16A3ILVTDz8sN04b5ZsAcZDIp7pgbBm23Hl+etv22gkyWiYiIqJ+i\nEdYrG0mlEixKCIamS4+cC3XmDI1G4FxZIzq79EiJnTxoJxNzu21uGKQS4GDmZYuc35SYLBMREVE/\nF64ly1GhXiPeJy0xBACnv7YGWcYSDAvUKxv5eLohaWYASqtbUVLVbLE4TIHJMhEREYl6DQKKK5oR\n5KuEh9JlxPuFTZ6IKYETkVdUh7ZOnRkjpKEYDAKyz9fCXSEfcRmNuSy/JRwAcOCbcovGcbOYLBMR\nEZGoUt0Gbbd+TIlWWmII9L0CvvrW9utUbdXFymY0tXVjbkwAZCN4ONOcVJF+8JukwJdnrqBT22PR\nWG4Gk2UiIiISjbZe+XoLVUGQSDhBiSUZu2CM16x9Q5FKJViWEoZuXS+O23B5DpNlIiIiEo105r6B\neHu4YfZ0XxSWN0F9tdPUodEwBEFAZkEtXOUyzI70tXQ4AIBbk0PhJJPgQGY5BME2J61hskxERESi\nwvImKN2cETTGWd/Ensu8uzzuKuvaUdvYiYRoP7g4yywdDgDAy90VqXGBqFC3ix/EbM2okuUtW7Zg\nyZIl4uv8/HxERUX1+1GpVOJ6vV6PjIwMpKSkICkpCRkZGdDpWPRPRERkjZrbuqC+qkF0+CRIpWNr\nOZYaNxlyZxmO51XZ7J1EW2XsgmGJWfuGsiw1DABwILPconGMldNINywoKMA777wDf39/cVlZWRki\nIyPxt7/9TVwmlf4v/96yZQtOnjyJHTt2oKenBxs3boSzszM2bNhgovCJiIjIVIoqxl6CYaRwdUZK\nbAC+PH0FJVUtiBxF+zm6OZkFtZBJJZgzM8DSofQTN80HQb4T8PW3NXhoZeyouqxYgxHdWdbpdHj2\n2Wf73TUGgJKSEkRERMDX11f88fb2BgB0d3dj9+7d2LBhA1QqFZKTk7Fp0ybs2bMHXV1dpr8SIiIi\nuimF5X39cG8mWQbYc9kS6ps0KKtuxazpPlC6OVs6nH4kEgmWpU5Bj96AozY4JfqIkuXt27cjNDQU\ny5Yt67e8tLQUU6dOHXCfwsJCaDQaJCUlicuSk5Oh0WhQWFh4EyETERGRORRevgqpVIKIEM+bOk58\npC88lHKcPHMF+l6DiaKjoWSdt54uGANZmhQCZycpDmba3oN+wybL58+fx969e/Hiiy/esK6kpARF\nRUVYuXIVRttsAAAgAElEQVQlFi5ciKeffhoNDQ0AgLq6OigUCri7u4vbK5VKuLm5Qa1Wm+4KiIiI\n6KbpenpRWt2KqYET4eoy4irNATnJpFioCkZrhw6ni+tNFCENJetcX26VHGNdJRhG7go5FsQHoaax\nE2dLGy0dzqgM+deg0+mwceNGPPPMM/D17d+CpL29HfX19eJDfFqtFlu3bsVDDz2Ejz76CFqtFnK5\n/IZjyuXyET/kl5eXN4pLIVvFcXYMHGfHwHG2XZUN3dD3GjBJ0TvkOI50jAMUff/X7ztyFlItJykx\np86uXhRcakSwtxzlpRdQboJjmuNveYpXNwDgH/89Df18b5Mf31yGTJbfeOMN+Pv74+67775hnbu7\nO/Ly8uDm5gaZrK89yeuvv44FCxYgJycHrq6uAybFOp0Obm5uIwouMTFxRNuR7crLy+M4OwCOs2Pg\nONu2imOlABqwODkaiaqgAbcZzRgnCAL+m/8FLl7RYkbMLChcrauO1p4cOVUBQajFrSnTkZgYcdPH\nM9ffcoIg4IuC4yiubsfUiBh4TXQ1+TnGaqgPB0OWYXzyySfIzs6GSqWCSqXC5s2bUVNTA5VKhdzc\nXCiVSjFRBgAfHx94enpCrVYjICAAGo0GHR0d4vqOjg5otdp+HTWIiIjI8oydMMYyc99AJBIJFieG\nQKc34JuztSY5Jg0s81oJhrXWKxv1PegXjl6DgMOnKi0dzogNmSzv2rULn376Kfbv34/9+/fj4Ycf\nhp+fH/bv3w+ZTAaVSoWamhpx+5qaGjQ3N2PatGmIjo6GQqHol6mfOnUKCoUC0dHR5rsiIiIiGhVB\nEFB4uQk+Hq7w9RrZt78jsTihb4KS4/m21wHBVmi79Th9sR6hAe4IHONEMuMpLTEYrnIZDmWVo9dg\nGw/6DZksBwUFISwsTPzx8vKCk5MTwsLCEBMTAz8/Pzz33HMoKirC2bNnsW7dOiQnJ2P27NlwdXXF\nqlWrkJ6ejtzcXOTk5CAjIwP3338/XFxsq78eERGRPVNf1aClo9tkd5WNArwnYEb4JJwtbcTVVq1J\nj0198ovr0aM3WN1EJINRuDpjUUIw6pu1NvPw55inu5bL5Xj77behUCiwevVqrFmzBuHh4di2bZu4\nzfr16zFv3jw88sgjeOyxx7BkyRI89dRTJgmciIiITMM4DfGMKaZNloG+O4mCAJzI50N+5mCctS/F\nRpJlAFiWEg4AOJhZbskwRmxUvWEeeOABPPDAA+LrkJAQvPHGG4NuL5fLkZ6ejvT09LFHSERERGZV\nVH7zM/cNZt7sILy5/xyO5VXhnrTpJj++I+vRG5BzQQ1fLzdMC/awdDgjNj3EExEhnsi5oEZDs9ak\npT/mMOY7y0RERGQfCsubIHeWYUqg6ROuiRPkmDPDH+W1bbhc02ry4zuyc2WN6OzSIyV2MiQSiaXD\nGZXlqeEwCMDn2RWWDmVYTJaJiIgcWKe2BxXqNkSGesJJZp60YPG16a9P5Feb5fiOyliCYSv1ytdb\nEB+ECa5O+Dy73OpneWSyTERE5MCKK5shCOYpwTBKmuGPCa5OOJ5fbTMdEKydwSAg+3wt3BVyzDRD\nrbm5ubo4IS0xBE1t3ci5YN0zOzNZJiIicmDmrFc2kjvLMG92EK62dqGgzLamOrZWF6ua0dTWjeQY\nf8jM9I2AuS1LDQcAHPim3KJxDMc2310iIiIyCWMnjKgw896dTEu81nM5j6UYpmDLJRhGYZMnYuaU\nSTh9sQG1jZ2WDmdQTJaJiIgcVK9BQHFFM4L9lJg4QW7Wc82c4g1fLzd8fbYG3T29Zj2XvRMEAZnn\nauEqlyE+ys/S4dyU5dfuLh/KKrdoHENhskxEROSgKtVt0HbrzVqCYSSVSrA4IRjabj1OFVh3jaq1\nq6xrR01jJxKi/eDiLLN0ODflllmBcFfIcSSnEj166/wQxWSZiIjIQRlLMEw9c99gjNNfH+P01zcl\nq8D2SzCM5M4y3JocitYOHTKvlZZYGybLREREDqpwHB7uu15owERMC/ZAflE9Wju6x+Wc9ijrXC1k\nUgnmzPC3dCgmsSwlDABwILPconEMhskyERGRgyoqb4K7whlBvspxO+fihBD0GgR8dYbTX49FfbMG\npdWtiJvuA6XCvHXm4yXQV4nZET4oKLuKqrp2S4dzAybLREREDqi5rQvqqxpEhU2CVDp+s78tVAVB\nKgGOsSvGmIglGHG2X4JxveWpUwAABzPLLRrHQJgsExEROaDxLsEwmjTRFbMjfFFc2Yyaho5xPbc9\nyDrX93Dk3JgAC0diWnNjA+Dl7oIvcqusrlsKk2UiIiIHZKlkGQDS5vRNf32c01+PSlunDucvNSIq\nzAveHm6WDseknGRS3DY3DJ3aHqsr0WGyTERE5ICKypsglUoQEeo57udOiZ0MF7kMx/OqIQic/nqk\nTp1XwyDYRxeMgdwxNwwSifU96MdkmYiIyMHoenpRWt2KqUEecJU7jfv53VyckBo7GbVXO1Fc0Tzu\n57dVxnrlFDurVzbym6RAYrQ/iiuacelKq6XDETFZJiIicjCl1S3Q9xosUoJhtPja9NfH8thzeSS6\nuvU4XVyPEH/3ce1eMt6W3xIOwLoe9GOyTERE5GCKjPXKYZZLluMjfOHp7oKTZ2rQozdYLA5bkVdc\nD53eYHddML4rMdofPp5uOJ5fBU1Xj6XDAcBkmYiIyOGID/dNsVyyLJNJsVAVhHaNDqeL6y0Wh63I\nOmc/s/YNRSaV4I6UMGi7e3HitHU86MdkmYiIyIEIgoCi8mb4eLrBx9OyHRXSEvq6YhxlKcaQ9L0G\n5FxQw8fTDdOCPSwdjtndlhwKqVSCg9+UW8UDoEyWiYiIHEjt1U60dHRbtF7ZaFqwB4L9lDh1Xo1O\nrXV85W6NzpU2orNLj5TYAEgk4zeBjKV4e7hhbkwALtW0oqSqxdLhMFkmIiJyJMZ65ehwLwtHAkgk\nEqQlhqBHb8A3Z2ssHY7VyrTTWfuGsjw1HABw4Jtyi8YBMFkmIiJyKIXlfa3arOHOMgAsSujrisEJ\nSgZmMAjILqiFu0KOmCnelg5n3MyO8MVk7wn48swVdGh0Fo2FyTIREZEDKSpvgotchimB1lH76j9J\ngZip3jhX1oiGZq2lw7E6F6ua0dTWjeQYf8hkjpO2SaUSLEsNg66n1+I17Y7zrhMRETm4Tm0PKtRt\niAzxgpMVJV6LE4IhCMCJ07y7/F2O0gVjIEuTQuEkk+JgpmUf9LOevxQiIiIyq+KKZgiCddQrX2/+\n7EA4yaQ4lldlFd0PrIUgCMgqqIWLXIb4KD9LhzPuPJQuuGXWZFTVdeD8pasWi2NUyfKWLVuwZMkS\n8bVer0dGRgZSUlKQlJSEjIwM6HT960p27NiB+fPnQ6VSYf369WhrazNN5ERERDQqYn9lK6lXNlIq\n5Eia6Y9KdTvKa5knGFXVteNKQycSovzg4iyzdDgWIT7ol1lusRhGnCwXFBTgnXfe6bdsy5YtOHny\nJHbs2IHt27fj6NGj2Lp1q7j+gw8+wK5du/DSSy/h/fffR1lZGTZt2mS66ImIiGjE/tcJw7qSZQBI\nuzb99dFc9lw2csQuGN8VM9UbIf5KfHO2Fq0d3RaJYUTJsk6nw7PPPguVSiUu6+7uxu7du7Fhwwao\nVCokJydj06ZN2LNnD7q6ugAAO3fuxKOPPooFCxYgLi4OmzdvxqFDh1BTw/YwRERE46nXIKC4sgkh\n/kq4K+SWDucGc2b4Q+nmjC9PV6PXwFIMoK9eWSaVIGmGv6VDsRiJRIJlqeHQ9xrwRU6lRWIYUbK8\nfft2hIaGYtmyZeKywsJCaDQaJCUlicuSk5Oh0WhQWFiIhoYGVFVV9VsfFRUFDw8P5Ofnm/ASiIiI\naDgVtW3QdvciOsz67ioDgLOTDPPjg9DU1o1zpQ2WDsfiGpq1KK1uRdx0Hyit8MPNeFqSGAK5swwH\nMytgsMAHqWGT5fPnz2Pv3r148cUX+y2vq6uDQqGAu7u7uEypVMLNzQ1qtRp1dXUAAD+//gXpvr6+\nUKvVJgidiIiIRspa65Wvt/haz+VjeeyKkXWtBCPFAbtgfJdSIcfC+CDUXu3EtyXj/0HKaaiVOp0O\nGzduxDPPPANfX99+67RaLeTyGz/pyOVy6HQ6sRTju9sY149EXl7eiLYj28ZxdgwcZ8fAcbZeX+f3\nJcu9nbXIy2sc83HMOcYGQYDnBBm+OlONlKm9kDs5btOuzzP7kkKFoQF5eU3jfn5r+1sO9+qrV959\n4AwMHeM7OcuQyfIbb7wBf39/3H333Tesc3V1HTDp1el0cHNzg4uLi/h6oPUjkZiYOKLtyHbl5eVx\nnB0Ax9kxcJyt246Dh+GucMYdaXMhkUjGdIzxGOM7Ggrx4ZGL0DlPRuq1O82Opq1Th8o9BxEV6oW0\nBcnjfn5r/FtOEAQcLTiB4ittCJ8+E94eI8slR2qoDwdDfmT75JNPkJ2dDZVKBZVKhc2bN6OmpgYq\nlQo+Pj7QaDTo6OgQt+/o6IBWq4W/vz8CAgIAAA0N/W+XNzQ0wN/fcQvViYiIxltTWxfqmjSIDp80\n5kR5vHD6a+DUeTUMBgEpDtwF47skEgmW3RIOg0HA4VPj+6DfkMnyrl278Omnn2L//v3Yv38/Hn74\nYfj5+WH//v2IjY2FQqHol4mfOnUKCoUC0dHR8PX1RUhICHJzc8X1xcXFaGtrQ3x8vPmuiIiIiPqx\nhXploxB/d0wP8UR+cT1a2i3TKszSstgybkCLVEFwc5HhUFbFuHZMGTJZDgoKQlhYmPjj5eUFJycn\nhIWFwdXVFatWrUJ6ejpyc3ORk5ODjIwM3H///WIJxurVq7Ft2zYcP34cBQUF2LhxI5YtW4bAwMBx\nuTgiIiKy7v7KA0lLCIbBIODLM453d7mrW4/TxfUI8XdHkK/S0uFYFYWrMxYnhKCxRYu8orpxO++Q\nNcvDWb9+Pbq6uvDII49AJpPhzjvvxFNPPSWuf/DBB9Hc3IyNGzdCr9dj8eLFN3TVICIiIvMqLG+C\nTCpBRIinpUMZkQWqILzzn/M4nleNlQumWTqccZVfXA+d3oCU2ABLh2KVlt8SjgOZ5TjwTTmSZ47P\nezSqZPmBBx7AAw88IL6Wy+VIT09Henr6gNtLJBKsW7cO69atu7koiYiIaEx0Pb0oq27B1CAPuMpv\n6h7ZuPFyd4Uq0hd5RfWorm9HsJ/78DvZCc7aN7QpgR6ICvNCXlEd6ps08JukMPs5HbcnCxERkQMo\nqWqBvlewiXrl6y1ODAEAHHegnsv6XgNyLtTBx9MN04Nt41sAS1iWEg5BAA5lV4zL+ZgsExER2TFb\nq1c2SokJgKtchmP51RAEx5j++lxpIzq1PUiJDbD6riWWND8+EBPcnPF5dgX0vQazn4/JMhERkR2z\npU4Y13N1ccItswJR36QRr8HesQvGyLjKnbB0Tgha2ruRXWD+WaGZLBMREdkpQRBQVNEEH083+Hia\ndhKH8WCc/toRSjEMBgFZBWq4K5wRM2V8Z6izRctSwwEABzPLzX4uJstERER2qvZqJ1o7dJhpY3eV\njWZF+MLL3QUnz1xBj77X0uGYVUlVM5raupA0MwAyGdOz4YT4uyN2mjfOlDSgpqFj+B1uAkeDiIjI\nThVets16ZSOZVIJFCcHo0PYgt7De0uGYVeY5lmCM1nLj3eUs8z7ox2SZiIjITtlqvfL10oxdMfKr\nLByJ+QiCgKyCWrjIZVBF+Vk6HJuRGjcZHko5jpyqhK7HfN88MFkmIiKyU0XlTXCRyxAeONHSoYzZ\nlMCJCA1wx6nzdejQ9lg6HLOoru/AlYZOJET5wcVZZulwbIazkwy3JoWiXaPDN2drzHYeJstERER2\nqEPbg8q6dkSFesHJhmtgJRIJFicEQ99rwNffXrF0OGZhLMFIiWUJxmjdkRIOADiQWW62c9juXw8R\nEREN6mJFMwTBduuVr7foWleMY3baFSOzoBYyqQTJM/0tHYrNmewzAapIX1y43IQKdZtZzsFkmYiI\nyA5dKL8KwLbrlY38vBSIm+aD85euor5JY+lwTKqhWYvSqhbETfOBUiG3dDg2afkt4QDM10aOyTIR\nEZEdMs7cFxXmZeFITGNxYt/d5ROn7evusnEikhR2wRizpJkBmDTRFcdyq9DVrTf58ZksExER2Zne\nXgMuVjYjxF8Jdzu5W3nLrEA4O0lxLK/Krqa/FpPl2AALR2K7nGRS3D43DJ1depw8Y/q6dibLRERE\ndqZC3Q5tdy9mhNvPTHBKN2ckzwxAVV0Hyq60Wjock2jr1KHg0lVEhXrB28P2Zli0JrfPDYNUYp4H\n/ZgsExER2ZnCy8Z6ZfsowTBKS7Sv6a9zLqhhMAiYy7vKN83Xyw1zZgSgpKoFpVUtJj02k2UiIiI7\nU1jeDMA+OmFcLyHaH+4KZ3x5uhq9vQZLh3PTOGufaYkP+mWVm/S4TJaJiIjsTGFFE9wVzgjyVVo6\nFJNydpJifnwQmtu78W1Jo6XDuSld3XqcLq5HiL8SwX7ulg7HLqii/ODn5YYT+dXoNOEENkyWiYiI\n7MjVVi3qmzSIDp8EiURi6XBMLi2hb/rrYzY+/XV+cT10egMnIjEhmVSCO1LC0aXrxfF805XqMFkm\nIiKyI0UVfSUY9tBfeSDR4V4I8FYg81wttGZoEzZejF0wWIJhWrclh0ImleBgZrnJuqYwWSYiIrIj\nhZf7+ivba7LcN/11CLp1vci+lnDaGn2vAacu1MHHwxXTgz0tHY5d8ZroipS4ySivbUPxtQ+ON4vJ\nMhERkR0pKm+CTCrB9BD7TcKME5TY6vTXBWWN6NT2ICV2sl2Wylja8tRwAKZrI8dkmYiIyE509/Si\n7EoLpgZ5wFXuZOlwzCbIV4nIUE+cuViP5rYuS4czasYuGJy1zzxmTfdBoM8EnDxzBe0a3U0fj8ky\nERGRnSitaoG+V7DbEozrpSWGwCAAX5phxjZzMhgEZBWo4a5wRuxU+5k0xppIJBIsSw1Hj96AL3Ju\n/kFQJstERER2orD8Wr3yFPtPlhfEB0EqleB4nm11xSitbkFTWxeSZgZAJmMaZi5L5oTA2UmKg5mX\nb/pBP44SERGRnSi6lixHh9l/suyhdEFClB9Kq1tRVddu6XBGTCzBYMs4s/JQumDe7EBcaejEubKb\n68nNZJmIiMgOCIKAwvIm+Hq5wcfTzdLhjIs08UE/27m7nHmuFnJnGVRRvpYOxe4ZH/Q7mFlxU8cZ\nUbJ8+fJlrF27FiqVCvPnz8crr7wCvb6vt2F+fj6ioqL6/ahUKnFfvV6PjIwMpKSkICkpCRkZGdDp\nbr7YmoiIiP6ntrETbZ06zHCAu8pGyTEBcHNxwon8ahgMpumpa05Vde240tCBxGg/u34A01rMCJ+E\nsAB3ZJ6rQXP72B8EHTZZ7unpwUMPPQQPDw/s27cPr7zyCj755BO88cYbAICysjJERkbiq6++En+O\nHDki7r9lyxacPHkSO3bswPbt23H06FFs3bp1zAETERHRjRypXtnIVe6EW2ZNRn2zVrx+a8YSjPEl\nkUiwPDUc+l4BR05Vjvk4wybLdXV1iIuLQ3p6OqZMmYK5c+di2bJlyM7OBgCUlJQgIiICvr6+4o+3\nd9/Tnd3d3di9ezc2bNgAlUqF5ORkbNq0CXv27EFXl+21eiEiIrJWxmQx2gE6YVxPnP7aBkoxsgpq\nIZVKkDzT39KhOIzFiSFwkctwMKtizN8+DJssBwcH49VXX4VSqQQAnD9/HocPH0ZKSgoAoLS0FFOn\nTh1w38LCQmg0GiQlJYnLkpOTodFoUFhYOKaAiYiI6EaF5U1wkcswZfJES4cyrmKn+2DSRFd8deYK\ndD29lg5nUI0tWpRUtSBumjeUCrmlw3EYE9ycsTA+CPVNGpy+WD+mY4zqAb8VK1bgnnvugaenJ9as\nWQOg785yUVERVq5ciYULF+Lpp59GQ0MDgL670gqFAu7u7uIxlEol3NzcoFarxxQwERER9deh7UGl\nuh1RoV4O145MJpVgcUIwOrv0yC2ss3Q4g8q6NjV3Kkswxt3yW8IBAAe+KR/T/qOqLn/55ZfR2tqK\njIwMPP744/jzn/+M+vp68SE+rVaLrVu34qGHHsJHH30ErVYLufzGT09yuXxED/nl5eWNJjyyURxn\nx8BxdgwcZ8soqekrbfRw6Tb7GFjjGPu59eUUH39xDi49tRaOZmCff9N3I1EhNCIvr9nC0QzPGsf5\nZkye5IxTF9Q4ejIbHorRPVw5qq1jYmIAAJs3b8aqVaugVquRl5cHNzc3yGQyAMDrr7+OBQsWICcn\nB66urgMmxTqdDm5uw7e1SUxMHE14ZIPy8vI4zg6A4+wYOM6WU9hQCKARS1JjkDjDfPWw1jzGB88c\nQ2ltOyJnxMHdysoc2jp1qNhzEJGhnkhbkGzpcIZlzeM8Vv9fTwVe/+cZ1Gk8sGRB9A3rh/pwMOx3\nNWq1Gp9//nm/ZREREQCApqYmKJVKMVEGAB8fH3h6ekKtViMgIAAajQYdHR3i+o6ODmi1Wvj7s7id\niIjIFAovGycj8bJwJJazOCEY+l4BX31bY+lQbpBzQQ2DQWAXDAtaqAqCwtUJh7Ir0NtrGNW+wybL\nly9fxhNPPNGvxvjcuXOQSCSQSCRQqVSoqfnfL2ZNTQ2am5sxbdo0REdHQ6FQ9MvWT506BYVCgejo\nG7N6IiIiGp3eXgMuVjYjxN/doR8cW5QQDIkEVjn9tVivHMdk2VLcXJyQlhiCq61dyBllbfuwyfKc\nOXMQHR2NZ555BhcvXkRWVhY2bdqEH/3oR4iPj4efnx+ee+45FBUV4ezZs1i3bh2Sk5Mxe/ZsuLq6\nYtWqVUhPT0dubi5ycnKQkZGB+++/Hy4uLmO+YCIiIupTXtuGLl0vZjhYy7jv8vF0Q9w0H1y43AT1\n1U5LhyPq0umRX9yAYD8lgv3ch9+BzGbZtRn9DmSWj2q/YZNlZ2dn/OUvf4GHhwfuv/9+rFu3DosW\nLcKmTZsgl8vx9ttvQ6FQYPXq1VizZg3Cw8Oxbds2cf/169dj3rx5eOSRR/DYY49hyZIleOqpp0YV\nJBEREQ2syDgZSbjjlmAYGae/PpFfbeFI/ud0cT10Pb28q2wFwidPxIzwSThdXD+qD1QjesAvICAA\nr7322oDrQkJCxNn8BiKXy5Geno709PQRB0VEREQjc0Gcuc/bwpFY3i2zArHjX2dxLK8aP7o1EhKJ\nxNIhcdY+K7MsNRyF5U04lFWB/1sxc0T7OFYzRiIiIjtTVN4Ed4UcgT4TLB2KxSlcnTE3djKuNHSg\ntLrF0uFA32vAqQt18PFwRUSIp6XDIQDzZgfCXeGMI6cq0aMf2YN+TJaJiIhs1NVWLeqbtZgRPskq\n7qJag8XXSjGO5Vm+FON82VV0anuQEjuZ42MlXJxlWJoUipaObvHBy+EwWSYiIrJRReV9k1tEs15Z\nlBDlB3eFHF+eroZ+lC3CTC2zgCUY1uiOlDAAwMHM8hFtz2SZiIjIRhVeq1eeyXplkZNMioWqILR2\n6HDmYoPF4jAYBGQV1ELp5oyYaRwfaxLs545Z031wtrQRVXXtw27PZJmIiMhGFZZfhUwqwXTWw/Zj\n7Ipx3IKlGKXVLbja2oXkmAA4yZhuWRtjG7lDWRXDbsvRIyIiskHdPb0oq27FtGAPuDjLht/BgUSG\nemGyzwRkFtRC09VjkRjYBcO6pcROhqfSBV/kVKK7p3fIbZksExER2aDSqhb0GgREO/hkJAORSCRI\nSwiGrqd3xA9xmVpWQS3kzjKoonwtcn4amrOTFLfNDUWHtgdff3tlyG2ZLBMREdmgQnEyEibLA1lk\nwa4YVXXtqK7vQEKUL1zlI5rSgizg9rlhkEiAA9+UD7kdk2UiIiIbVHiZyfJQAn2UiA7zwtmSBlxt\n1Y7ruY13szlrn3UL8J6A8MkTUVTRPOR2TJaJiIhsjCAIKCxvgp+XG7w93CwdjtVanBgCgwB8eXro\nr9lNLfNcLaRSCZJmBozreWl0vjxdjcs1bcNux2SZiIjIxtQ0dqJdo2O98jDmzw6ETCoZ164YjS1a\nlFS1IG6aN9wV8nE7L43eP78oGdF2TJaJiIhsDEswRsZD6YLEaH9cqmlFRe3wdxBNQSzBYBcMq1c5\ngh7LAJNlIiIim1NU0Zcs887y8NLmXOu5nD8+d5eNyfJcJstWL9TffUTbMVkmIiKyMRcuN8FVLsOU\nyRMtHYrVS5oZAIWrE47nV8NgEMx6rnaNDufKriIixBM+nqwlt3arlkaMaDsmy0RERDakQ6NDVV07\nIkO9IOPMcMNycZZh3qxANLZocf7SVbOeK+eCGgaDwC4YNmKhKhi/eiAR4cN86ORfGRERkQ0xtrli\nvfLILRZ7LleZ9Tyctc/2LFQF47X1aUNuw2SZiIjIhhSVs155tGKn+sDHwxVfn62BbpipjceqS6dH\nfnEDgv2UCBlhLSzZBibLRERENqSQyfKoSaUSLEoIhqZLj1MX1GY5x+niBuh6elmCYYeYLBMREdmI\n3l4DLlY2IzTAHUo3Z0uHY1PSEkMAwGw9l41dMFiCYX+YLBMREdmIy7Vt6NL1sl55DMImT8SUwInI\nLaxDa0e3SY+t7zXg1Hk1vD1cMT3Y06THJstjskxERGQjxHrlMCbLY5GWGIJeg4Cvz9aY9Ljny66i\nQ9uDlNjJkEolJj02WR6TZSIiIhthrFeeMYXJ8lgsVAVBIgGO5Zq2K0YmZ+2za0yWiYiIbERheRMm\nTpAj0GeCpUOxSd4ebpg93RdFFc2obew0yTEFQUBWQS2Ubs6ImeZtkmOSdWGyTEREZAMaW7RoaNZi\nRvgkSCT8qn+sjD2XTTX9dUlVC662diFppj+cOEmMXeKoEhER2YCiCraMM4XUuMmQO8twPK8KgnDz\n018bu2CwZZz9GlGyfPnyZaxduxYqlQrz58/HK6+8Ar1eDwDQ6/XIyMhASkoKkpKSkJGRAZ1O12//\nHZJoWeAAACAASURBVDt2YP78+VCpVFi/fj3a2tpMfyVERER2TKxXZrJ8UxSuzkiJDUBNYycuVjbf\n9PEyz9VC7iyDKsrPBNGRNRo2We7p6cFDDz0EDw8P7Nu3D6+88go++eQTvPHGGwCALVu24OTJk9ix\nYwe2b9+Oo0ePYuvWreL+H3zwAXbt2oWXXnoJ77//PsrKyrBp0ybzXREREZEdKipvgpNMgukhbE12\ns0zVc7mqrh3V9R1IiPKFq9zJFKGRFRo2Wa6rq0NcXBzS09MxZcoUzJ07F8uWLUN2dja6u7uxe/du\nbNiwASqVCsnJydi0aRP27NmDrq4uAMDOnTvx6KOPYsGCBYiLi8PmzZtx6NAh1NSYtm0LERGRverS\n6VFW3YppQZ5wcZZZOhybFx/pCw+lHF+euQJ9r2HMx2EJhmMYNlkODg7Gq6++CqVSCQA4f/48Dh8+\njJSUFBQWFkKj0SApKUncPjk5GRqNBoWFhWhoaEBVVVW/9VFRUfDw8EB+fr4ZLoeIiMj+lFa1oNcg\nsF7ZRJxkUixUBaOtU4fTxfVjPk5WQS2kUgmSZgaYMDqyNqN6wG/FihW455574OnpiTVr1qCurg4K\nhQLu7u7iNkqlEm5ublCr1airqwMA+Pn1r+Px9fWFWm2eudmJiIjsDeuVTW9xQl9XjGNjLMW42qrF\nxcoWxE71hrtCbsrQyMqMqsDm5ZdfRmtrKzIyMvD4449j5cqVkMtv/AWRy+XQ6XRiKcZ3tzGuH05e\nXt5owiMbxXF2DBxnx8BxNo+sbxsBALq2auTl1Vo0FnsZY0EQ4O3uhMxzV/B1lgBX59E1CDt1sQMA\nEOSht5v35Hr2eE1jNapkOSYmBgCwefNmrFq1CgkJCQMmvTqdDm5ubnBxcRFfD7R+OImJiaMJj2xQ\nXl4ex9kBcJwdA8fZPARBwCv7D8JvkgJpC5ItGou9jfGy5mJ8cLAIWqk/5iWGjmrfj3O+BgD86HvJ\n8PEcPqexJfY2ziMx1IeDYT9GqdVqfP755/2WRUREAOi7Q6zRaNDR0SGu6+jogFarhb+/PwIC+mp4\nGhoa+u3f0NAAf3//kV8BERGRg7rS0IF2jQ4zwliCYWr/K8UY3fTX7RodzpVdRUSIp90lynSjYZPl\ny5cv44knnuhXY3zu3DlIJBKsWLECCoWiXzZ+6tQpKBQKREdHw9fXFyEhIcjNzRXXFxcXo62tDfHx\n8Sa+FCIiIvtTJNYre1k4EvsT4D0BM8In4VxZIxpbtCPeL+dCHQwGgV0wHMSwZRhz5sxBdHQ0nnnm\nGWzatAlNTU34zW9+gx/96P9n787jqqjXB45/Dod9FRABQUAWEQUVXNBUwiW13C1bzDYrs0VTy2y5\nlpkt3rq5VWpqmea1NJcsNZfc9w1UFBREZFGQfd/P+f3hlX6kBCgw58Dzfr143cvMmTnPNDLnOTPP\n9/k+iouLC6NHj2bWrFnMmTMHrVbL7NmzGTNmTEUJxlNPPcWCBQtwcXGhefPmzJgxg0GDBtGyZct6\nPzghhGho+8MSWfdnNPEpubg5WjG6nw8hga5KhyX0WGTczYkzpBNG/ejT2ZXIuAz2hyUyqo9Pjba5\n1TKuu78ky01BtcmykZERixcv5uOPP2bMmDEYGhoydOhQpk2bBsCbb75JUVEREyZMQK1WM3ToUKZM\nmVKx/dNPP01mZiZvv/02ZWVlhIaGMnPmzHo7ICGEUMr+sEQ+//GvJ21x13MqfpeEWdytyLgMTI3V\neDhbKx1Ko9SzowvfbjrHnlM1S5aLSso4FXUDFwdLWjlaVft6of9qNMDPycmJhQsX3nGdsbExs2bN\nYtasWXdcr1KpmDx5MpMnT777KIUQQg+s+zO6yuWSLIu7kVtQQkJKLh19mqNW165bg6gZawtjuvg5\ncjQimSvXsmnd0uYfXx92MZWS0nIpwWhC5C9PCCHqSHxK7h2XJ1SxXIjqXLwqJRgNIbQW01/LrH1N\njyTLQghRR+ytTe+4XB7Virslk5E0jK5+jliYGrIvLJFyjbbK15WXazh+Phl7G1O8XZs1YIRCSZIs\nCyFEHcjOKyav8M6TLbX3tG/gaERjcasThq+0jatXxkZqenZ0IT27iIjLaVW+LiI2nbzCUoLbO2Fg\noGrACIWSJFkWQog6sOzXCAqLywkNcsXD2Rq1gQoXBwtMjAzYfjSOszGp1e9EiP+nrFzDxfhM3Jys\nsDQzUjqcRq9P5+p7Lh89JyUYTVGtZvATQghxuxMXktl7OhGfVs2Y/HhgpYFYZ6JTmbn0CJ98f5w5\nE3vj7iQdDUTNxF3LobikXEowGki71vY42Jpx+Ox1Jowqw9S4coqk1Wo5GnEdSzMj/L2aKxSlUILc\nWRZCiHtQUFTKN7+cwVCt4vXHAm/rWNDRx4FJjwWSX1TGh8uOkpFTpFCkQt9IvXLDMjBQERrkSmFx\nGSfOp9y2Pjohi7TsIrq2c8RQOpM0KXK2hRDiHqz4/QJp2UWM7tcG9yr64Pbp3IqnHvQjNbOQD5cd\npaCotIGjFPooSpLlBlcx/fXp20sxpAtG0yXJshBC3KVzMWlsOxKHm5MVo/u1+cfXju7nw8Du7sQm\nZTNn1UnKyzUNE6TQW5FXM7C2MMa5uYXSoTQZbk7WeLnacDrqBtl5xZXWHY24jrGhAYFtWigUnVCK\nJMtCCHEXikrKWLguHAMVvP5YIEaG/3w5ValUvDyqA0FtW3A66gbfrD+LVlt1iyrRtKVlFZKaWYif\nhx0qlXRdaEihQa0o12g5EJ5UsSzxRi4JKXkE+rbA1ESGezU1kiwLIcRdWLP9ItfT8hkW4kUbN9sa\nbaNWGzD9qS54utiw49jVKmf8E0LqlZUTEuiCgaryBCVHpAtGkybJshBC1NKl+Ew27YvByd6cJwe1\nrdW25qZGfPBCdxxszVi1LfIf21SJputWvbLM3Nfw7KxN6ejjwMX4TK6l5gE3SzAMDFR0a++kcHRC\nCZIsCyFELZSWaVi4NhyNFiY+2um29lI1YWdtyswXumNhasiCn8M4Ey09mEVlkXEZGKpVeLeSWeKU\n0KfLzemv95xKJD27kEvxWfh72mNlbqxwZEIJkiwLIUQtrN8TTdz1HAZ2d6eDt8Nd78fNyZr3ngsG\n4NMVx7l6PaeuQhR6rqikjNikbLxcmmFipFY6nCapu78zJsZq9p5OqJiIpLu/lGA0VZIsCyFEDV1N\nzuHnnRexszbluSHt73l/Ad7Nef3xIPKLypi57Cjp2YV1EKXQd9EJWZRrtPi1lhIMpZiZGOLZ0obk\n9AIWbzwHgIyzbLokWRZCiBoo12hZ+HM4ZeVaXn2kIxZ1NP1waJArTz/kR1qW9GAWN0m9svL2hyVW\nDLK8ZcnGc+wPS6xiC9GYSbIshBA18NuBWC7GZxLSyaXOB/k80teHQT08uHIthzkrT1ImPZibtFtJ\nWlv3mnVZEXWvqk410sGmaZJkWQghqpGcns+qbZFYmRszfmRAne9fpVIxYWQAXfwcOX3xBt/8ckZ6\nMDdRWq2WqLgMWtiZY29jpnQ4TVZ8Su4dlydUsVw0bpIsC0XsD0tk4hd7+HDNzf+VR1tCV2m1Whau\nDaektJzxIwOwsTSpl/dRqw1466kueLvasPN4PD/vulQv7yN0W+KNPHILSvFzlxIMJbk5Wt1xeasq\nlovGTZJl0eD2hyXy+Y+niLueg1YLcddz+PzHU5IwC52041g8Z2PS6NrOkfsDXer1vcxMDHn/+e60\nsDVj9R9R/Hkivl7fT+ieW/XKMrhPWaP7+dRquWjcJFkWDU5qwYS+SM8u5LvfIjAzMeSVhzs2yLTD\nttamzHyxBxZmRixcG86ZS9KDuSmRmft0Q0igK9PGdsbD2Rq1gQoPZ2umje1MSKCr0qEJBcgE56LB\nSS2Y0AdarZZF689SUFTGK490pHmzhqsfbeVoxb+e68aMJUf45IfjzHmtNx7O1g32/kI5UVczMDNR\n4+4kj/uVFhLoKsmxAOTOslCAi4PFHZdLLZjQJQfDr3HsfDIBXs0ZGOze4O/v79WcKU8EUlBUxsyl\nR0jLkh7MjV1uQQkJKXm0cbNFrZaPZyF0hfw1igal1WoxM7nzA41RfbwbOBoh7iw7r5glm85ibGjA\na492xMBAmdkIQgJdeXZwO9Kzi6QHcxMg/ZWF0E2SLIsGtT8siUvxWbi2sMTD2RoDFVia35zcISu3\nWOHohLhp2a8RZOeV8OQgP1o2t1Q0llF9vHnwPg/irufw6Q8npAdzI3arXrmdh73CkQgh/j9JlkWD\nycgpYvGGs5gYq3n/+e4sfLMP7z/hypK3+2NlbsyaHVHyqFko7sSFZPaeTsSnVTOGh3gqHQ4qlYqX\nRgTQtZ0j4ZdS+WpduPRgbqSi4jJRqaCNTEYihE6pUbKcnJzMpEmTCA4OpmfPnrz77rvk5OQA8Ntv\nv+Hr61vpZ8iQIRXb5uXlMW3aNLp06ULPnj356quv5ELfBGm1Wr5aF05eYSnPDWmPc/O/6patLYx5\nbkg7CovLWbY5QsEoRVNXUFTKN7+cwVCtYtJjgTpTN6pWG/DW2C54t2rGnycS+GnHRaVDEnWsrFzD\npYRM3BytsKyjqdSFEHWj2k+C8vJyXnnlFQoKCli5ciWLFi0iKiqK6dOnAxATE8P999/PwYMHK35W\nrVpVsf17771HfHw8q1at4qOPPmLlypWsXr26/o5I6KQ/T8Rz4kIKnXwceLCHx23r+3V1o627LYfO\nXOP0xRsNH6AQwIrfL5CWXcTofm10rvuEqYkh7z8fTAs7c/674yK7jksP5sbkyrVsikvKpV5ZCB1U\nbbJ84cIFzp8/z6effoqvry8dOnTgvffeY/fu3eTk5BATE4Ovry8ODg4VP7a2Nx8hJSUlsX37dmbN\nmoWfnx99+/Zl4sSJrFixor6PS+iQ1MxClv4agbmpIRMf63THwVIGBipeeaQjBipYvOEsJaXlCkQq\nmrJzl9PYdiQONycrnZ14wNbKlJkvdMfSzIiv1oUTJl8sG42KemWZjEQInVNtsuzq6srSpUtxcHCo\nWHarMX9xcTExMTF4et65ri88PBwrKyt8fX0rlnXt2pWEhARu3JCLfFOg1WpZ8HMYBUVlvDDMnxa2\n5lW+tnVLG4b08uR6Wj4b9sY0YJSiqSsuLWfh2nAMVDDp0U4YGaqVDqlKrRyt+Ne4YAwMVHz6wwmu\nXMtWOiRRB6LiMgHphCGELqo2Wba1tSUkJKTSshUrVuDu7o61tTUJCQkcOnSIQYMG0bdvX2bOnEle\nXh4AKSkpODo6Vtq2RYsWwM06aNH4bTsSR3h0Kl38HOnfza3a1z85qC121ias23WJ5PT8+g9QCOC/\nf0RxPS2fYSFe+LrrfrLS3tOeKU8EUVhcxofLjsrA2EYgMi4DG0tjnO3v3IdeCKGcWs/g9+2337Jj\nxw4WL17MlStXKC8vx8jIiLlz53Ljxg0+++wzpk6dyrfffkthYSHGxsaVtr/1e0lJSbXvderUqdqG\nJ3RIRl4Zy7amYGqsIsTXgNOnT9/xdX8/z338LVh/OIN/rzjImPvtG2SKYVH/dPXvOSm9hI37bmBr\nqaadY6HOxvl35sADgTbsDMtm+sI9jOvvgKmx8gMS9eW/ny7Jzi8jLasQX1fTKq+TukTOcdMg5/kv\ntUqWv/76axYsWMB7771HaGgoAMePH8fGxgYAPz8/bG1tGT16NFevXsXU1PS2pPjW72Zm1U8d27lz\n59qEJ3SIRqPl3UWHKC3TMunJzoQG3XnK0FOnTt12noOCtETfOMzZmDTKTF3o7u/cECGLenSn86wL\nSss0rJi3D60W3hgbTEcfh+o30iFBQVqMzc+x5dAVtp0p4YMXemBkqFzCrKvnWdcdCEsCkunR0ZPO\nnXWzXv4WOcdNQ1M8z//05aDGV9WPP/6YhQsX8sEHH/D0009XLL+VKN/i43PzDz05ORknJydSU1Mr\nrb9Vq3yrHEM0TpsPxHI+Np0eAc7cH+hSq21VKhUTRnXAUK3i203nKCouq6coRVO3fk80cddzGNjd\nXe8SZbj5t/LiiACC2ztxJjpNejDrqcirNwf3+cngPiF0Uo2S5fnz5/Pjjz/y2WefMWbMmIrl27Zt\no3v37hQX/zXz2vnz5zEwMMDDw4PAwECysrKIiflrsNaJEydo1apVpQGDonFJvJHLqq0XsLE05pWH\nO95VGUUrRytGhnqTmlnIz7su1UOUoqmLT87h550XsbM25bkh7ZUO566pDVS8ObYzbdyasftkAv/d\nLj2Y9U1kXAaGahXers2UDkUIcQc1ah23ePFixo0bR8+ePUlNTa34CQ4ORqVS8e677xIbG8vRo0d5\n7733GDFiBI6OjrRs2ZL+/fvz1ltvERERwZ49e1i4cCHjxo1riGMTCigv1zBvTRglZRpeebgjzaxM\n7npfj/ZvQwtbMzbtiyEhJbcOoxRNXblGy4Kfwykr1/LqIx2x0PNJIEyNDZkxrjuOdub8tPMiO49d\nVTokUUNFxWXEJmXj5doMYyPd7cIiRFNWbbK8fft2NBoNy5Yto1evXpV+0tLSWL58OWlpaTz88MNM\nnjyZ3r17M3PmzIrtP/nkE9zc3Bg7diwzZszg2WefrXR3WjQuG/bGcDE+k/sDXbmvQ8t72pepsSHj\nRwRQVq5l8Yaz8nhZ1JnfD8ZyMT6TkE4udGvvpHQ4daKZlQkzX+yOlbkRX/1yhtNR0p5TH0QnZqHR\naPGTlnFC6KxqB/hNmTKFKVOm/ONrfvjhhyrX2djYMG/evNpHJvRO3PUc/rs9CjtrE14aFVAn+wz2\nd6ZrO0dOXEhhX1hSlQMFhaip5PR8Vm2LxMrcmPEj6+bfqa5wbXGzB/O/Fh/ms5XH+ezV3ni62FS/\noVBM5JWb9crSX1kI3aV8nyHRKJSWaZi75jRl5VomPhqIlblx9RvV0PgRARgbGvDd5gjyC0vrbL+i\n6dFqtXy1LpziknLGjwzAxvLuy4R0VbvW9rwxpjNFJeV8uOwINzILlA5J/INbM/fJnWUhdJcky6JO\nrN11idikbB7o5kYXP8fqN6gFJ3sLHn2gDZm5xazeHlWn+xZNy87j8ZyJTqOLn2Otu7Tok54dWzJu\naHsycor5cNlR8uRLpk7SaLRcvJqBo505dtamSocjhKiCJMvinsUkZLH2z0s42JrxwnD/enmPUaHe\nuDhYsOVgLLFJMr2vqL307EK+2xyBmYnhXXdp0SfDQ7wY0qs18cm5fLriOKVlGqVDEn+TlJpHbkGp\n3FUWQsdJsizuSWlZOXN/Oo1Go+X1RwMxN62frgJGhmpeGtkBjRa+WX8GjUYG+4ma02q1LFp/lvyi\nMp4b2h4H2+onRdJ3KpWKF4YH0N3fibMxaSxYGyaDZHXMrRIMqVcWQrdJsizuyeo/oohPzuWh+zzo\n2KZ+e2cH+ragV8eWXLyayc7j8fX6XqJxOXjmGsfOJ+PvZc/AYHelw2kwagMVbzzZGV83W/aeSmT1\nH1LGpEui/pcst5PJSITQaZIsi7sWFZfBxr0xONmb82wDTerwwnB/zEzU/LDlPNl5xdVvIJq87Lxi\nlmw8i7GhARMf7YSBQeMuv/g7U2NDZjwfjLO9BT/vusT2o3FKhyT+JzIuAzMTNW5O1kqHIoT4B5Is\ni7tSVFLG3DWn0QKTHw/CzKTaLoR1wt7GjDED/cgtKGXl1sgGeU+h35ZtjiA7r4QnB/nRsrml0uEo\nwsbyVg9mY75Zf5aTkSlKh9Tk5eSXkHgjD183O9RN7AucEPpGkmVxV1ZtjeRaWj7DQ7xo72nfoO89\ntFdrPJyt2XHsKlFXMxr0vYV+ORmZwt5TiXi3asbwEE+lw1FUSwdL3n8+GEMDFXNWniAmMUvpkJq0\ni1elXlkIfSHJsqi1czFpbD4Qi2sLS8Y+6Nfg769WGzBhVAcAFv1ylvJyGeUvbldQVMrX68JRG6h4\n/bFA1Gq53LX1sGPqk50pLi1n1rKj3MiQHsxKkf7KQugP+fQQtVJQVMq8n8MwUMGUJ4IwMVIrEkd7\nT3v6dW1F7LVsthy+okgMQret2HKBtOwiRvdrg4ez1ITe0rNDS54f5k9mbjEzlx0lr6BE6ZCapMi4\nDFQq8HW3VToUIUQ1JFkWtfL97xe4kVHAw319aOOm7EX+uSHtsTQzYvUfUWTkFCkai9AtEZfT2HY4\njlaOVjza30fpcHTO8BAvhoV4kpCSyycrTlBaVq50SE1KWbmGS/FZuDtZY2FWP+02hRB1R5JlUWOn\no27wx5E4PJyteWKAr9LhYGNpwtOD21FQVMZ3m88rHY7QEcWl5SxcG45KBa8/1gkjQ2Wefui6cUP9\n6RHgzLnLacz/KVx6MDegK9eyKSktl3plIfSEJMuiRvIKS1mwNgy1gYopTwTpTAIyINgdn1bN2BeW\nyNmYVKXDETpgzfYorqXlM6y3F77ukoxUpaIHs7st+8ISWbVNuss0lMgrt+qVpQRDCH0gybKokaWb\nzpGeXcTjA3zxdLFROpwKagPV/6YuhkXrz8qUvk1cdEJmRe/vsYPaKh2OzjMxUjNjXDDOzS1Y92c0\n247EKR1SkyAz9wmhXyRZFtU6FnGd3ScT8Ha14ZG+ulf/6d2qGQ/28CDxRh6b9sUoHY5QSGmZhgU/\nh6PRwmujO2HaQL2/9d2tHszWFsYsXn9GejA3gKi4DJpZmuBsb6F0KEKIGpBkWfyjnPwSvvrlDEaG\nBkx+IghDHW2/9dSDfjSzNOHnXZekHVYTtWFPNHHXcxgQ7E5Hn/qder2xadnckhnPB2OoNrjZgzlB\nejDXl9TMQtKyi2jrYYtKJZORCKEPdDPzETpj8YazZOUWM3ZQW9x1eEpWS3NjnhvanuKScpb+ek7p\ncEQDi0/O4aedl7CzNuW5oQ0z9Xpj09bdjjfH3uzB/OHyo6TIl856ESX9lYXQO5IsiyodCE/iQHgS\nfh52DL/fW+lwqtWnsyvtPe05GpHMiQvJSocjGki5RsuCteGUlWt45eEOWEorrrvWI6AlLw4PICu3\nmJlLj0gP5npwIS4dkHplIfSJJMvijjJzi1i0/izGRmomPx6I2kD3HxeqVCpeHtUBtYGKJRvPUVwq\nvWObgi0HY7l4NZPenVwI9ndWOhy9N7S3JyPu9yLxRh6zvz8uPZjrWFRcBoZqA7xdmykdihCihiRZ\nFrfRarV8ve4MuQUlPDu4HS0dLJUOqcbcna0ZFuJFSkYB6/68pHQ4op4lp+ezclskVubGjB8RoHQ4\njcZzQ9pzXwdnzsemM29NGBqN9GCuC0XFZcRey8Hb1QZjhWY/FULUniTL4jZ7TiVy7HwyHbybM7hn\na6XDqbUnBvhib2PK+t0xXEvNUzocUU+0Wi1frQunuKSc8SP8aWZlonRIjYaBgYqpYzrj52HH/vAk\nVm69oHRIjUJ0QhYajVZKMITQM5Isi0rSsgr5duNZzEzUTHosEAM9KL/4OzMTQ14cEUBZuYbFG87K\nzGSN1M7j8ZyJTqOLnyP3B7kqHU6jY2Kk5l/jgmnZ3IL1e2LYeviK0iHpvUgZ3CeEXpJkWVTQarUs\nXBtOflEZzw/zx9HOXOmQ7tp9Ac4E+bYg7FIqh85eUzocUcfSswv5bnMEZiaG/5uURv++1OkDawtj\nZr7YAxtLY5ZsOMtxGTh7TyRZFkI/SbIsKuw4dpXTF28Q1LYFA4LdlQ7nnqhUKl4aFYCRoQHLfo2g\noKhU6ZBEHdFqtSxaf5b8ojKeG9IOB1szpUNq1JybWzBjXDCGhmr+veokl+IzlQ5JL2k0WqLiMnCy\nN8fW2lTpcIQQtSDJsgAgJaOA5ZsjsDA1ZOLoTo3iTl3L5pY83MeH9Owi1uy4qHQ4oo4cPHONY+eT\n8feyZ2B3D6XDaRJ83e2YNrYzpaXlfLT8GMnp+UqHpHeSUvPIKyyVemUh9FCNkuXk5GQmTZpEcHAw\nPXv25N133yUnJweAvLw8pk2bRpcuXejZsydfffVVpRrRsrIyZs+eTffu3enatSuzZ8+mpER6d+oS\njUbL/J/CKCwuZ/zIDjRv1nju1D3Szwcne3M2H4jl6vUcpcMR9ygnv4QlG89ibGjAxNGd9LKmXl91\n93dm/IgAsvKKmbn0KLnSg7lWpARDCP1VbbJcXl7OK6+8QkFBAStXrmTRokVERUUxffp0AN577z3i\n4+NZtWoVH330EStXrmT16tUV23/55ZccOHCARYsW8fXXX7N7927mzp1bf0ckam3LoSucu5xGcHsn\n+nRuXAOlTIzUvDSyAxqNlm/Wn5HBfnpu2a/nyM4r4clBbfWqpWFjMbiXJyNDvUlKzWP2d8cokV7m\nNRZ5RZJlIfRVtcnyhQsXOH/+PJ9++im+vr506NCB9957j927d5OUlMT27duZNWsWfn5+9O3bl4kT\nJ7JixQoAiouLWbNmDdOnTycwMJBu3brxr3/9i59++omioqL6PjZRA0mpeazYcgErc2NeHd04B0p1\n8XOkR4AzF65ksPtkgtLhiLt0MjKFPacS8W7VjOEhXkqH02Q9O7gdvTq25MKVDOauOS09mGsoMi4D\nMxND3JyslQ5FCFFL1SbLrq6uLF26FAcHh4pltxKq8PBwrKys8PX1rVjXtWtXEhISuHHjBpGRkRQU\nFNC1a9eK9d26daOgoIDIyMi6PA5xF8o1WuatOU1JaTmvPNIBW6vGO+jkheH+mBir+f738zKFrx4q\nKCrl61/OoDZQMenRTqjVMtxCKQYGKqY8EUS71nYcPHONH7ZID+bqZOcVk5Sah6+7rV7MhiqEqKza\nTxxbW1tCQkIqLVuxYgXu7u6kpKTg6OhYaV2LFi2Am3XOKSkpmJubY2VlVbHe0tISMzMzkpOlBZHS\nft0XQ9T/pgnu1dFF6XDqVQtbcx5/wJfsvBJWbpMvavrmhy0XSMsq5JF+PrRuaaN0OE2e8f96MLs4\nWLJhbwzzfzrNxC/28OGaRCZ+sYf9YYlKh6hTLv6vg4iUYAihnwxru8G3337Ljh07WLx4MefPdgEG\nhAAAIABJREFUn8fY2LjS+lu/l5SUUFhYeNv6W6+pySC/U6dO1TY8UUM3skpZ+UcKFqYG9PDSKPrf\nuqHeu5WllubWhmw7HIerVQEu9rf/2xT1527Pc9yNYrYeTsXBxhAfu3y5LuiQh3tYsmRbPrtO/FXe\nFHc9h89/PEVs7BUCPPS3V3td2hOeDYC6NL1R/PttDMcgqifn+S+1Spa//vprFixYwHvvvUdoaCiX\nL1++Lem99buZmRmmpqZ3TIpLSkowM6u+40Lnzp1rE56oobJyDdMW7KdcA1Oe6EKwv7NisZw6dapB\nz7O5XRrvLjrEngslfDGpuzwSbSB3e56LS8v59os9qFTw1jM9aOsud+Z0zbpDu7h+h1Zyp66U8ezD\ncg0HWHf0ICoVDOnXDQszI6XDuScNfc0WymiK5/mfvhzUuPDv448/ZuHChXzwwQc8/fTTADg5OZGa\nmlrpdTdu3ABulmM4OTlRUFBAXl5exfq8vDwKCwtvK98QDeeX3dHEJGbTt0srRRNlJQR4Nyc0yJWY\nhCy2H41TOhxRjTXbo7iWls+w3l6SKOuolMyCOy5PSMlt4Eh0U1m5huj4TNydrPU+URaiqapRsjx/\n/nx+/PFHPvvsM8aMGVOxPDAwkKysLGJiYiqWnThxglatWuHg4EDbtm0xNzevlK0fP34cc3Nz2rZt\nW4eHIWoqNimbn3ZcxN7GlBdHBCgdjiLGDW2PuakhK7dGkpVbrHQ4ogrRCZls3BuDo505YwfJ9UJX\nuTla3XG5i7T2A25ec0vKNFKvLIQeq1HruMWLFzNu3Dh69uxJampqxU+LFi3o378/b731FhEREezZ\ns4eFCxcybtw4AExNTRk9ejSzZs3i5MmTnDhxgtmzZzNmzBhMTEyqDU4GitSt0rJy5q45TblGy6RH\nA7Fsonc5bK1NeepBP/ILS/n+9/NKhyPuoKxcw4Kfw9FoYeLoTpia1Hp4hWggo/v53HF5Zl4RUVcz\nGjga3RP1v8lIZOY+IfRXtZ9A27dvR6PRsGzZMpYtW1Zp3W+//cYnn3zCBx98wNixY7G0tOTZZ5+t\ndPf5zTffpKioiAkTJqBWqxk6dChTpkypUXC3BooAhAQ2rskylLBmx0XirucwqIcHQW1bKB2Ooh68\nrzU7j8ez+2QCA4Ldae9pr3RI4v9ZvzuauOs5DAh2p2Mbh+o3EIq5dW1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On73GmEFtGXxf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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a20920a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mean number of payments per month\n",
    "monthly = temp['total_payments'].groupby(temp.index.month).mean()\n",
    "monthly.index = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
    "monthly.plot(marker='o')\n",
    "plt.title('Mean number of payments per month')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Seems payments do tend to increase during Sep and Nov (bi-modal dist.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1a26084be0>"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GKCwuxZmgBKmjEBERERFVqUEX1wMcO0NFWQknA+IghJA6DhERERHRSzXo4lpL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\nePrvhK++AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a25d0eb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mean number of payments per week\n",
    "weekly = temp['total_payments'].groupby(temp.index.dayofweek).mean()\n",
    "weekly.index = ['Mon', 'Tues', 'Wed', 'Thurs', 'Fri', 'Sat', 'Sun']\n",
    "weekly.plot(marker='o')\n",
    "plt.title('Mean number of payments as a function of day of the week')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Similarly, see peaks in mean no. of payments on Mondays and Fridays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### New users\n",
    "\n",
    "How about growth in terms of number of newly incorporated companies?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1a219f12b0>"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YpaKC4R/84AfYunUrvvSlL2HTpk04dOhQVtAr/h0IBAo+X3d3dzEvf0ro6elpuP3mPjeO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wrJnhPKtSQAH+A5GHV7RenDY0szwyo+w40+1Mq15QS2VBF2WqTHVyIWz8HlbLwAl0LRYk2DYa/b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\nIiIi8loshomIiIjIa7EYJiIiIiKv9T8BSeV29jlx5wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a20e922e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "monthly = temp['company_id'].resample('M').count()\n",
    "monthly.plot()\n",
    "plt.title('New users per month')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Here we do see an upwards trend starting in 2011"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1a25d68898>"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dkZRVgat5VYMRGhER6bj/7b4MhVKF5XODYWbSqwV/NeZGaQW7VugjJsc0YFJy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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a25ccd748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Total new users over the months\n",
    "monthly2 = monthly.groupby(monthly.index.month).mean()\n",
    "monthly2.index = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
    "monthly2.plot(marker='o')\n",
    "plt.title('Mean new users as a function of month in the year')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Seems new users peak in March and October - could this be because of e.g. a promotion/campaign?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Segmentations by vertical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1a24af36a0>"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BTExMhWP9/PwqbJs9e7b0dceOHREdHQ0AGDt2LMaOHQsA2LFjxyvHqqamJl0Q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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a265ccfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped = df.groupby('vertical')\n",
    "temp = grouped[cols_payments].sum().T\n",
    "ax = temp.plot(kind='bar')\n",
    "_ = ax.set_xticklabels(xticklabels)\n",
    "plt.title('Total payments per month by vertical')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Gym/fitness vertical registers highest share and growth. Other two verticals register little growth and in terms of share seem very similar\n",
    "* Spike in accountancy in April could be due to end/start of new tax year?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['total_payments'] = df[cols_payments].sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1a277d8208>"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IiIiIRI6FjoiIiEjkWOiIiIiIRI6FjoiIiEjkWOiIiIiIRI6FjoiIiEjkWOiI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\n6IiIiIhEjoWOiIiISORY6IiIiIhE7v8DqJvX7n6rO7YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a27600710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = grouped['total_payments'].sum()\n",
    "temp.plot(kind='bar', figsize=(10, 3))\n",
    "plt.title('Total payments in 2014 by vertical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a25357c50>"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IiE09NzdXBw4cUNeuXSVJoaGhNvXS0lJ9+eWXCg0NrbIuSV988YVVBwAAAByV3b5xdMSI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\nERGZZP4DcWpW5kYfmxMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a23c78a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='vertical', y='total_payments', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* This box plot shows that for all verticals the distributions are right-skewed and suffer from quite a few **outliers** (in fact box plot is hardly visible as most of the mass of the distribution is within less than 155, as we saw earlier. As mentioned earlier, these should have been investigated and removed if applicable but here we didn't "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Churn Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "\n",
    "## Challenges\n",
    "\n",
    "The following are typical challenges in churn models:\n",
    "\n",
    "### Nature of churn\n",
    "\n",
    "* **Defining the churn event** - for non-subscription based businesses there is no clear way to know when they have churned or not. Typically simple assumptions are made about this, e.g. if a user hasn't used the servce for $n$ periods of time, then it has churned - however this fails in cases when customers have different seasonalities or burst-like behaviour\n",
    "* **Churn event censorship:** customers that are considered non-churners up to the point when the model is trained are said to be 'right-censored' as they were only partially observed. The churn event censorship is problematic for conventional supervised machine learning methods that require dataset labels to be fully observable. The **survival models** are therefore an attractive alternative\n",
    "* **Type of churn** - for example this could be voluntary or involuntary, e.g. involuntary churners could be from terminations by the company due to fraudulent activity, or for other reasons independent of the company. Hence, having a separate model for each churn type is typically better\n",
    "* **Churn rate** - as the customer base size changes this also changes\n",
    "* **Changes in behaviour** - a model could cease to perform in the future due to changes in customer behaviour\n",
    "\n",
    "### Data related\n",
    "\n",
    "* **Low churn rate (class imbalance):** churn is normally a rare event presuming the business is in a good shape. This leads to 'class imbalance' where the number of churners is much smaller than non-churners, so the model performs poorly. There are a number of approaches to alleviate class imbalance, e.g. down/up sampling\n",
    "\n",
    "### Choosing a model\n",
    "\n",
    "\n",
    "* **Binary classification:** Although quite commonly used, this approach ignores churn event censorship and is sensitive to class imabalance, unless handled properly, so it makes it and inferior choice vs survival analysis\n",
    "\n",
    "\n",
    "* **Survival analysis:** these are the peferred choice when modelling time to event datasets. They do not label customers as churner and non-churners. Instead they provide a survival curve, which can be used to compute the expected time to churn event for each customer. \n",
    "Accordingly, a customer can be regarded as churner if their predicted time to churn is close (based on a preset threshold) to their current length of tenure. \n",
    "The are a few model options in this space:\n",
    "    * **Kaplan-Meier (KM)** - this is the simplest model as it is non-parametric. Given the duration until the churn event (or censorship) for a set of customers, it provides the `Survival Function`, i.e. their probability of survival over time\n",
    "    * **Survival regression** - take the KM idea to the next level by incorporating additional features (covariates) in the modelling process. There are variations of this based on how the covariates are related to the survival curve. Popular choices are the Cox and Aalen model.\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Survival Analysis\n",
    "\n",
    "\n",
    "Based on the above points, our choice will be a **Survival Analysis** model. First we will try the simplest, which is the **Kaplan-Meier** model. If time allows, we will then try the Survival regression.\n",
    "\n",
    "\n",
    "Most of the code used here can be found in the script `survival_analysis.py`, where we used the  python library `lifelines` - see more info on this here http://lifelines.readthedocs.io/en/latest/Quickstart.html\n",
    "\n",
    "\n",
    "NOTE: for simplicity we ignore for now all the `mandates` data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kaplan-Meier model\n",
    "\n",
    "This model requires two input features:\n",
    "* Duration `T`: the time to the churn event, i.e. how long the customers had been on your service\n",
    "* Event status `E`: indicates whether the event (churn) has occured to each customer or not, or whether it has been censored"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating Duration\n",
    "\n",
    "We want to make inferences about duration, and it is likely that not all churn events have occured yet. We therefore need to define the **criteria** for what we call a 'customer has churned'. This is the most critical input, for which I explored a few different options:\n",
    "\n",
    "* **Start date:** as the \"birth date\" I am using the `incorporation_date` found in the data, assuming none of the customers churned before 2014 as can be inferred as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 28)"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['total_payments'] == 0].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "* **End date:** as the \"death date\" I tried two different definitions - a simple one and another one to try to capture the difference in seasonality between customers: \n",
    "    \n",
    "    1. If payments stopped for at least 3 months after the last recorded payment, we consider this customer churned \n",
    "    ```python\n",
    "    def get_death_time_v1(x):\n",
    "    end_time = None\n",
    "    dead = False\n",
    "    for i, p in enumerate(x):\n",
    "        if not dead:\n",
    "            end_time = x.index[i]\n",
    "        # Require at least 3 months of no activity\n",
    "        if x[i] == 0 and x[i - 1] == 0 and x[i - 2] == 0:\n",
    "            dead = True\n",
    "            # print('dead:', x.index[i], p)\n",
    "        else:\n",
    "            dead = False\n",
    "    if end_time and dead:\n",
    "        return pd.to_datetime(end_time.split('_')[0])\n",
    "    return np.nan\n",
    "    ```\n",
    "    \n",
    "    2. We categorise customers by payment frequency - some have monthly payments (e.g. gym membership), some quarterly (e.g. utility bills). If a customer has got zero payments for at least 2 payments cycles we consider this customer as churned\n",
    "    \n",
    "    ```python\n",
    "def get_death_time_v2(x):\n",
    "    # In how many months do we have payment activity?\n",
    "    active_months = len(x[(x > 0)])\n",
    "\n",
    "    # remove the number of trailing '0' payment months\n",
    "    length = len(x)\n",
    "    add = True\n",
    "    counter = 0\n",
    "    for i in range(0, length):\n",
    "        i += 1\n",
    "        if x[-i] == 0:\n",
    "            if add:\n",
    "                counter += 1\n",
    "        else:\n",
    "            add = False\n",
    "    if counter > 0:\n",
    "        y = x[:-counter]\n",
    "    else:\n",
    "        y = x\n",
    "    removed_trailing_months = len(y)\n",
    "\n",
    "    # Calculate the average payment frequency observed, ignoring any recent '0' payment months\n",
    "    frequency = int(removed_trailing_months / active_months)\n",
    "\n",
    "    # As dead time threshold I set 2*frequency.\n",
    "    # So if a customer did not have any payments for twice his usual payment frequency\n",
    "    # I consider that customer churned.\n",
    "    dead_time = 2 * frequency\n",
    "\n",
    "    # Extract deadtime if it exists:\n",
    "    end_time = None\n",
    "    dead = False\n",
    "    z = x[int(removed_trailing_months):int(removed_trailing_months + dead_time)]\n",
    "\n",
    "    if len(z) > 0:\n",
    "        if len(z[z == 0.0]) == dead_time:\n",
    "            end_time = x.index[removed_trailing_months]\n",
    "            dead = True\n",
    "    if end_time and dead:\n",
    "        return pd.to_datetime(end_time.split('_')[0])\n",
    "    return np.nan\n",
    "    ```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at an example for a particular 'company_id=5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014-01-01_payments     0\n",
       "2014-02-01_payments     0\n",
       "2014-03-01_payments    39\n",
       "2014-04-01_payments     0\n",
       "2014-05-01_payments    54\n",
       "2014-06-01_payments     0\n",
       "2014-07-01_payments     0\n",
       "2014-08-01_payments    22\n",
       "2014-09-01_payments     0\n",
       "2014-10-01_payments     0\n",
       "2014-11-01_payments     0\n",
       "2014-12-01_payments     0\n",
       "Name: 4, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df.loc[4, cols_payments]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a156eb160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = x.plot(marker='o', figsize=(9, 4))\n",
    "_ = ax.set_xticklabels(xticklabels)\n",
    "_ = plt.title('Payment activity for company id=5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This company looks like it has churned but note the two definitions above give rise to different churned dates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In v1 this customer churned on 2014-11-01 00:00:00\n"
     ]
    }
   ],
   "source": [
    "print('In v1 this customer churned on {}'.format(get_death_time_v1(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In v2 this customer churned on 2014-09-01 00:00:00\n"
     ]
    }
   ],
   "source": [
    "print('In v2 this customer churned on {}'.format(get_death_time_v2(x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this, we proceed to calculate the duration `T` (in months) and churn event `E` using `v1` above and the `lifelines.utils.datetimes_to_durations` helper:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from lifelines.utils import datetimes_to_durations\n",
    "\n",
    "# Calculate the death date over all rows using lambda function\n",
    "df['death_date'] = df[cols_payments].apply(lambda x: get_death_time_v1(x), axis=1)\n",
    "\n",
    "# Create duration and churn status\n",
    "start_times = df['incorporation_date']\n",
    "end_times = df['death_date']\n",
    "obs_time = datetime.datetime(2015, 1, 1)\n",
    "T, E = datetimes_to_durations(start_times, end_times, freq='M', fill_date=obs_time)\n",
    "df['T'] = T  # duration (in months)\n",
    "df['E'] = E  # churn status"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualisation of churn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/YNKkSezdu5f9+/fz0ksv0b59e7p06cKgQYOYNm0au3fvZteuXZjNZoYMGeKY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2d844978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = df[['company_id', 'E']].groupby('E').count()\n",
    "ax = temp.T.plot.bar(stacked=True, figsize=(7, 5))\n",
    "_ = ax.set_xticklabels([])\n",
    "plt.legend(['not-churned', 'churned'])\n",
    "\n",
    "heights = {'71': 261, '28': 641}  # fix as labels seems wrongly switched\n",
    "for p in ax.patches:\n",
    "    p_height = p.get_height()\n",
    "    pct = (p_height / df.shape[0]) * 100\n",
    "    #ax.annotate('{:.1f}%'.format(pct), (p.get_x(), p_height))\n",
    "    ax.annotate('{:.1f}%'.format(pct), (p.get_x(), heights[str(int(pct))]))\n",
    "    \n",
    "_ = plt.title('Churn rate out of the total customer base in 2014')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Lly76/PPPtXnzZiUkJOiRRx5Rq1atdOLECbm7u2v+/PlatGiRjh49qgULFujJ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\nXBti0wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2d9edb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = df[['company_id', 'E', 'vertical']].groupby(['E', 'vertical']).count()\n",
    "temp = temp.T\n",
    "\n",
    "ax = temp.plot.bar(stacked=True)\n",
    "_ = ax.set_xticklabels([])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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k5+crrbtLly5BW1tb6az8Zar2N+DFH63dunXDvHnzcOjQIZw8efI/L09ZvLxs//zzD3Jz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\nBUgAFkIIISxAArAQQghhAf8BEun6beHNJIoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2d6e3668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = df[['company_id', 'E', 'vertical']].groupby(['vertical', 'E']).count().reset_index()\n",
    "temp = temp.rename(columns={'company_id': 'Total Customers'})\n",
    "g = sns.factorplot(x=\"vertical\", y=\"Total Customers\", hue=\"E\", data=temp, size=6, kind=\"bar\")\n",
    "_ = plt.title('Churned vs not-churned customers broken by vertical')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above plots we see:\n",
    "\n",
    "* We have about 30% churn rate in the 2014 customer base (this indicates class imbalance wrt the total, as expected and discussed before)\n",
    "* In the stacked bar we see the largest base is from gym/fitness vertical, while accountancy and freelance developers are about half of the gym base, and both are relatively equal in terms shares\n",
    "* Despite gyms being the largest base, the biggest churners are accountancy and freelance developers\n",
    "* Accountancy and Freelance developers have quite a rather concerning churn rate as for both this is above 50%, and more accute in freelance developers. Gym/fitness has a more 'normal' churn rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train-test split\n",
    "\n",
    "<span style=\"color:red\">In real circumstance at this point one would proceed to split the data into train and test samples (or better: a train-validation-test split) to be able to assess model performance. However, given that here we are not using a Binary classifier and time constrains we will not do this here and just take the KM fit in the entire data. Below we outline some steps one would take to split into train and test: </span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(902, 31)"
      ]
     },
     "execution_count": 490,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "721"
      ]
     },
     "execution_count": 483,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Do an 80-20 split by taking the first 80% of the rows \n",
    "# (ideally better to randomly sample in case sorting applied in data)\n",
    "train_size = int(df.shape[0] * 0.8)\n",
    "train_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = df.iloc[:train_size, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now one would check the train dataset has good class and segmentation balance in the train sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       company_id\n",
       "E                \n",
       "False         510\n",
       "True          211"
      ]
     },
     "execution_count": 487,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['company_id', 'E']].groupby('E').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This train set has a similar class balance to the original dataset, so good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vertical</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>accountancy</th>\n",
       "      <td>219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freelance developer</th>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gym/fitness</th>\n",
       "      <td>286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     company_id\n",
       "vertical                       \n",
       "accountancy                 219\n",
       "freelance developer         216\n",
       "gym/fitness                 286"
      ]
     },
     "execution_count": 488,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['company_id', 'vertical']].groupby('vertical').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also has good balance across the vertical segmentation, so good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>company_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vertical</th>\n",
       "      <th>E</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">accountancy</th>\n",
       "      <th>False</th>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">freelance developer</th>\n",
       "      <th>False</th>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">gym/fitness</th>\n",
       "      <th>False</th>\n",
       "      <td>247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           company_id\n",
       "vertical            E                \n",
       "accountancy         False         139\n",
       "                    True           80\n",
       "freelance developer False         124\n",
       "                    True           92\n",
       "gym/fitness         False         247\n",
       "                    True           39"
      ]
     },
     "execution_count": 489,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['company_id', 'vertical', 'E']].groupby(['vertical', 'E']).count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The churn rates are also similar to the full dataset, so in theory we could use this"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now back to the KM model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using still the `v1` churn definition, we get the following KM Survival Function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1a2a522c18>"
      ]
     },
     "execution_count": 471,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FCxbXNmnSxPyYYpmZmbpy5Yp+/ZWdGQAAAFC12V2Ql5WVZXMxnY+Pj3Jzcy3a\nWrZsqe7du+ull17SokWLFBAQoPnz50uS8vLs180mJycb7Xe1dOrHa0rz85b023hk5hQq/u1vLa5p\n16yuenSuZ9GWkVWggMJUOYunj3tVxbi7HmPuHoy7ezDurseYu4ejx91uOPb19S0RgiUpNzdXfn5+\nJdoXLFig6dOn67bbblPdunU1btw4hYWFKSAgwG5nIiIiDHa7esrwOqegQF9JUvdfTpXY5/hqRo7O\nXyrU/SGhFu0XLl5Thpe3+bYjd69ITk72+HGvihh312PM3YNxdw/G3fUYc/eo6LiXFajthuPmzZsr\nMzNTGRkZ5oCbkZGhrKwsBQeXDGitW7fW+vXrdenSJfn4+KhevXq65ZZb1KZNm3J33JMNu7V9idPx\nStv7uHhv5GKcogcAAOAcdsNxWFiY/P39lZycrH79+kmSdu3aJX9/f4WFhVlcazKZ9Mgjj+jpp59W\nt27dJEkHDx7UtWvXLBbp1VTWB4Pk5hWUCL5GsBcyAACAcxgqqxg7dqxiY2OVmJgok8mkuLg4jRs3\nTnXr1lV6erry8vIUFBSkWrVqqV69ekpMTFRsbKwyMjI0Y8YMPfjgg6pfv74r3k+VZh1ebR0KcjUj\np8QMctcOjS1mmW0FamaTAQAAKs/QCXnTp09Xdna2Jk2aJG9vb0VFRWnatGmSpPj4eO3atUvbtm2T\nJM2dO1fz5s3TvffeK39/f40dO1ZPPfWU896BB+naobHNOuTvTv5aogQDAAAAjmcoHPv4+Cg2Nlax\nsbEl7ktISLC4HRQUpNdee80xvathylOHDAAAAMczdHw0AAAAUBMYmjlG1cciPQAAgMojHHsIFukB\nAABUHuG4GrDewcJ69woAAAA4BuG4irPewaI8u1dYl1pQZgEAAFA2wrEbWR8KIpU8GMR6B4vy7F7B\nyXoAAADlQzh2I1uzuLYOBrFm5KAQAAAAlB/huJqpzEEhtna0+PmnLEVEOLybAAAA1RLhuJqpzEEh\ntna0OJVb6JB+AQAAeAIOAQEAAACKMHNcxRhZpAcAAADnIBxXMSzSAwAAcB/KKjxA1w6NVT+grkVb\n8SI9AAAAGMfMsQeozCI9AAAA/BfhuIbLKzCVKNvgJD0AAFBTEY49mHUdsq0a5EYBtRUU6GvRxkl6\nAACgpqLmuBoo3sGi+OvCxWt2H2Ndh0wNMgAAgH3MHFcD1iUORnavsK5DLk8NsvVJepRZAACAmoJw\nXIPY2u6BOKETAAAgAElEQVStVSMvhYZYXme9pzJlFgAAoKagrKKGKG27tx//k+OmHgEAAFQ9zBxX\nQxU5Ra+07d7y8vKc0kcAAIDqiHBcDVX0FL2Ksq5BlqhDBgAAnolwDLtszUhThwwAADwR4RgVwmwy\nAADwRIRjVAizyQAAwBMRjj1ERRbpSVJmTmGJ7d1snaRnBLPJAACguiMce4iKLNLr2qGx9h69YNFW\nfJJeRcIxs8kAAKC6IxzXYMNuba8OTXIVGhJqbivPSXpGcNoeAACoTgjHKMH6JL2KlllInLYHAACq\nF07I82DFdcjFXxcuXrP7GOuT9IrLLAAAAGoCZo49mHX5gpGDQqxP0nN0mQUAAEBVRjiGS7GjBQAA\nqMoIxzWIre3eLmXk232cdQ2yVPE6ZHa0AAAAVRnhuAaxNTt76uTJMh/TtUPjEjXHldnuzRZmkwEA\nQFVBOEaZrGuQJcfXITObDAAAqgrCMSrEkaUWAAAAVQXhGOXmjlILyiwAAIArEI5RbqWVWjhz4R5l\nFgAAwBUIx3AIW7PJl9NztOPATxbtFQ3LLNoDAACuQDiu4Xx9vErMyubmFdhcJFcWW7PJn/+/UxbB\nuDKlF7b6c+HiNQIzAABwKMJxDde5pZ8iwltbtBk5Sc8IZ5+2xy4XAADA0QyF4/z8fCUkJGjz5s0q\nKCjQyJEjNWPGDPn4+JS4Njc3V4sXL9bmzZuVnZ2tyMhIxcTEqEWLFg7vPGCNhXwAAKAyvIxctGjR\nIu3YsUMrVqzQsmXLtG3bNi1evNjmtStWrNDWrVu1ePFivf/++8rKytKTTz7p0E4DpWkeVE9Bgb7m\nr2vZee7uEgAAqEbszhzn5ORo3bp1WrhwocLDwyVJMTExevbZZzVlyhT5+vpaXL9t2zaNHTtWPXr0\nkCQ99dRTuu+++3Tp0iU1atTICW8BjmZ9zHRFapBL4+r9kW0t5LOFGWYAACAZCMcpKSnKzMxUz549\nzW2RkZHKzMxUSkqKOTAXa9CggbZu3aoxY8aoQYMG2rRpk9q2basGDRo4vvdwCuuQ6KgaZFfsj2zN\naKi3tbjPCEI1AACexW44Tk1Nlb+/vwIDA81tAQEB8vPz04ULF0pcHx0draefflp9+/aVt7e36tev\nr/fee09eXoYqOODBXHEUdUVVdGacBYAAAHgWu+E4KyvL5sI7Hx8f5ebmlmg/e/asGjdurDlz5qhh\nw4ZasmSJpk6dqg0bNsjPz6/M10pOTi5H1+Eo9sb91I/XlObn7ZTXzsvLU2ZOoeLf/taivV2zuurR\n2TGlHM50KSNfp06erNBjv/vx35V+fV8fL3VuWfb3Ff6LnzHuwbi7B+Pueoy5ezh63O2GY19fX5sh\nODc3t0TYzcjIUHR0tJYuXaq+fftKkpYsWaL+/fvr888/15gxY8p8rYiIiPL0HQ6QnJxsd9xN/qkl\nFrY5qg65+y+nbJZanL9UqPtDQiv9/FXV0WNHFeqA93cxPbvEVnywzchnHY7HuLsH4+56jLl7VHTc\nywrUdsNx8+bNlZmZqYyMDAUEBEj6LQRnZWUpONiy1vLkyZPKzMxUWFiYuS0gIEDt2rXT2bNny91x\nVA22amqdtReyZPsoamcu2qvOjC44NIL6aQAADITjsLAw+fv7Kzk5Wf369ZMk7dq1S/7+/hYhWJKa\nNWsmSTpy5Ih55jgnJ0dnz57Vdddd5+i+w0NZL9xz9qK96sxRu4hInDgIAIBksKxi7Nixio2NVWJi\nokwmk+Li4jRu3DjVrVtX6enpysvLU1BQkJo3b65BgwYpPj5eL774oho2bKhly5apXr16uvPOO13x\nfuAi1tu9SY4rtXD2yXqwzegR3dYI0AAAT2LohLzp06crOztbkyZNkre3t6KiojRt2jRJUnx8vHbt\n2qVt27ZJkl5++WUtXrxYzzzzjLKyshQREaF3333X7mI8VC/OLLWwxdX7I+M3Rn7Zqeg2eEYRvgEA\nrmQoHPv4+Cg2NlaxsbEl7ktISLC4HRAQoDlz5mjOnDmO6SGqDWfNJtvaH/lyeo52HPipRDuB2fUc\nWdphi3X4JiwDAJzJUDgGjHDWbLKtRXuf/z/bu1xQm+x5rMM3tdEAAGciHMOpnHUUNbtc1FxGa6MJ\nzACAiiAcw6mcdRS1LdblF5Re1BxGAvPPP2WJLUgBAPYQjuExrGeTbZVeEJhrDuvAfCzLcXtCG8HM\nNQBUT4RjeCyjtcqlBWYjCNXVR6OA2goK9HXZ61kvTgUAVA+EY9QoRgOzESwARFmMnl7IDDMAVC2E\nY7iUMw8PqShbgdkIWwsAjcrLy1OdXZU/3ISZ66rL6Ge6ovtEE6oBwDkIx3ApVx8e4ky29l92JWau\nPUNFfzGkbAMAnINwDFRQRWecJenosaMKDQmt1OtXZubaFmahqxejZRtGMAsNAP9FOIbbVcVSi+rA\nkTPXzEJXP478/mAWGgD+i3AMt/OkUgtXqszMtbXKzEIz4wwA8CSEY1RJzjpZD7ZVdBa6MtvgGUX4\ndr6KlmhQjgHAExGOUSW58mQ9VHwWuqLb4BlFuYdrsCgQAP6LcAygwhxZ2mGLrXIPZpKrDiMzzqd+\nvKYMr7KvYQYaQFVCOEa1wKK9msm63KM8ZRyEaOcz8v2X5udt92RC9noGUJUQjlEt2PoHcE9KKoHZ\nw1nPTBst47AVols18lJoiFO6iUqirANAVUI4RrXFLhc1j9EyDusQfTUjR3l5Xs7sGgDAQxCO4VHY\n5QJSyRD98to9uuLAA1OMoKwDAKonwjE8CrtcwJauHRpr79ELLns9dtlwDaNb0FGbDKA8CMfwaCzk\ng/TbTHKHJrmVPrLbqPIcqsIMc8UZ/T62teCPwAygNIRjeDQW8sEdjB6qUplDVAjVxtn63mYxH4DS\nEI5R4xgNzEYQqmFLRRcOGkXZRuVZl2QwkwygGOEYkO3AbERFQ3VGVoFDZq4I59VbRQ9RMVq2wexy\n6ay/byq617JRhG+g+iAcA5VQ0X/sAgpTFRHeutKvz4LDmslI2QYHppSPs3/JpIwDqD4IxwBQzRiZ\ncTZaskGJhmsY3VnDGjPOgOsRjoFqzNZuHBVFiYZnMVqy4cq9n2uyin5vGSn3IEADjkU4BqoxR/6D\nyC4eNZfRbeesUY7hfEa+/2wF6FM/XlOGFwsOgYogHAOQVPFdPAjQ1ZvRbeesUY5Rddj6/kvz81ZQ\noK/5NjXPgHGEYwClMjLTVNEdO4wifDuXs3fMyMvLU51d7KrhbpwmCBhHOAZQKc7+h5QdOaqmis44\nW+MgFNeozGmC1gjQ8HSEYwBVmvWiQ2aSqwajM85Hjx0t89huDkKpWox8b1GiAU9HOAZQpVnPULFw\n0LNUpqwD7lHRbekqiplquBrhGEC1YnThIIEZcA5Xf19V9PRCQjUqinAMoNozEpgvZeS7sktwAaNb\n0FGbXL1VNIxT/oGKIhwD8EjWgfnncz+69B9LZq6dy+iCwNIW/BGYAZSGcAygRujc0k8R4a1d9nrs\nsuFcRmuVbS34YzEfgLIQjgHACYwe7c0Ms3PZCtEs5gNQFsIxADiB0YVAFT1EhVBdOdb1ypRZAChG\nOAYAN6roanrKNirOul65MgeRlOc1Cd9A9UA4BoBqyEjZBrPLtlmXWlT0IBKjOAUQqF4IxwBQDRmZ\ncWZ22ZiKHkRiFKcAuoetw0rY+xhGGArH+fn5SkhI0ObNm1VQUKCRI0dqxowZ8vHxsbhu586dmjBh\ngs3nSExM1KhRoyrfYwCAISwKrBoqcwqgkb2cmV22zdZnmr2PYYShcLxo0SLt2LFDK1asUF5enqKj\no1WnTh3NnDnT4rrw8HB9/fXXFm1LlizRt99+q8GDBzuu1wAAu4zOkDHDXDUZ2cu5tJKNvLw81dnF\ngkNrRo6+ZnYZdsNxTk6O1q1bp4ULFyo8PFySFBMTo2effVZTpkyRr6+v+VofHx81bdrUfPv777/X\nRx99pHfffVcBAQFO6D4AAJ7JyIyzkZINyjP+y8hfSJhdht1wnJKSoszMTPXs2dPcFhkZqczMTKWk\npJgDsy0LFizQ0KFD1aNHD8f0FgDgcEbLL6xRjuF+pQXoo8eOKjQkVJLx8gyJGWZAMhCOU1NT5e/v\nr8DAQHNbQECA/Pz8dOHChVIft3//fu3cuVNJSUmO6SkAwCnYTs6zGT1qmxlm4Dd2w3FWVlaJhXfS\nbyUUubm5pT7u/fff1+23366OHTsa7kxycrLha+E4jLt7MO6ux5g71s8/ZelUbqHd65IPHC7z/rwC\nkxoFsHmSox09dlSS1KGJ1KFJoJ2rpQ+/ydOVjBzFv/1tmde1a1ZXPTp77l8MLmXk69TJkxZtvj5e\n6tzSz+5j+RnjHo4ed7s/jXx9fW2G4NzcXPn52f6g5ObmauvWrYqPjy9XZyIiIsp1PSovOTmZcXcD\nxt31GHPHMzKcRsZ9T0qqrmXnlfv1Keso3e/LKozq/ov9+uXL6Tk6dCZL5y/Z/6XIUapCqcfF9GxF\nhLcu8xp+xrhHRce9rEBtNxw3b95cmZmZysjIMC+qy8jIUFZWloKDbf8pbs+ePSooKFC/fv3K3VkA\nQM1CWUfV4KgFgI5EqQfcwW44DgsLk7+/v5KTk81hd9euXfL391dYWJjNx+zbt0/XX3+96tXjN3oA\nADyFsw9MsVaexYTWqsKMM6onQ2UVY8eOVWxsrBITE2UymRQXF6dx48apbt26Sk9PV15enoKCgsyP\nOXr0qEJCQpzacQAA4NmMLia05ugZZ07bq1kMrYCYPn26srOzNWnSJHl7eysqKkrTpk2TJMXHx2vX\nrl3atm2b+fpff/1Vbdu2dU6PAQAQJwDWBJU5XdCROG2vZjEUjn18fBQbG6vY2NgS9yUkJJRoe++9\n9yrfMwAAysAJgACcgb1zAAAezdYMM7PJqCzrUotTP15ThhelF56AcAwA8Gi2wgmzyags61+u0vy8\nFRToa9FG6UX15OXuDgAAAABVBTPHAIAax7rUgjILAMUIxwCAGse61GJPSqpT/wRO+K6Z2AKueiIc\nAwBqPGeHFWqcXc/W4SGuPhiELeCqJ8IxAABOZnRPZmvMOFeMrcNDOIoaRhGOAQBwsorOTBsp9yBA\nl2Tr8BBHHwwCz0U4BgCgijISqinZMM5WqYU1V5deoOohHAMAUI3ZKtnIyCpgNw4rtkotrLmi9MJ6\nkR4L9KoewjEAANWYrWAVUJiqiPDW5tvMLtsutbDmitIL619SWKBX9RCOAQDwcEYXBDLDDBCOAQDw\neEb/bM8Ms+vZ2gvZFsovXIdwDAAAJBmbYfb02WVX749sdCwpv3AdwjEAAJDE7hjsjwyJcAwAAMqh\nogeaVJQrZ6qr8v7I7HLhOoRjAABgmKsDmSfPVJcHu1y4DuEYAABUWRy9DVcjHAMAgCqrojPVjpxx\ndvUiPSNs7XJBqYVjeLm7AwAAAFVV1w6NVT+grkVb8SI9d2oeVE9Bgb4WX9ey89zaJ0/BzDEAAEAp\nqvIiPTgH4RgAAKCcrEst8vLyVGeXe0svjB4oUlE1pWyDcAwAAFAOtvZDtuaO/ZGdvQCxpuyQQTgG\nAAAoB1ulFkePHVVoSKj5NqUX1RcL8gAAAIAihGMAAACgCGUVAAAATlAV90eGfcwcAwAAOFhV3R8Z\n9jFzDAAAPI6tY6ddeaQ0+yNXX4RjAADgcWztx+vMPYBrAiP7KHvCXsiEYwAAUCPYmk225srZ5erG\nyLh4wl7IhGMAAFAjGJnRZHYZhGMAAAAXsd7Bgt0rqh7CMQAAgAtYHzvtjiOmnc0T6pIJxwAAAC5g\nvYOFJ+5e4Ql1yYRjAAAAN7F1UIgtlF+4DoeAAAAAFCne0eL3XxcuXnPKa9k6KMQWDg9xLWaOAQAA\nirhyf2RbB4XY4onlF1UZM8cAAABAEWaOAQAAqjhP2gLOyI4Wkvt2tSAcAwAAVGGetgWc0RMI3bWr\nBeEYAACgCqsJW8BVJYbCcX5+vhISErR582YVFBRo5MiRmjFjhnx8fGxev3r1ar3zzju6fPmyIiIi\nNG/ePLVq1cqhHQcAAHCF4h0sfi83r8DwDKgz2NoCrjqXWlQlhhbkLVq0SDt27NCKFSu0bNkybdu2\nTYsXL7Z57bp16/Taa68pOjpaH3zwgQoKCjRlyhSHdhoAAMBVenQJVr/w1hZfPnW83dYfW1vAsd2b\n49idOc7JydG6deu0cOFChYeHS5JiYmL07LPPasqUKfL19TVfazKZtGLFCk2ZMkVDhw6VJM2dO1cP\nPvigUlNTFRxcdY8KBAAAqA5sbQFHqYXj2A3HKSkpyszMVM+ePc1tkZGRyszMVEpKijkwS9KJEyeU\nmpqqYcOGmdvatm2r7du3O7bXAAAAbmRdapGRVVDlSi9QMXbDcWpqqvz9/RUYGGhuCwgIkJ+fny5c\nuGBx7enTp+Xj46OzZ89q8uTJOn/+vMLDwxUTE8OsMQAA8BjWW4wFFKYqIry1RZuzDg8pjdGjqCuq\nptQ02w3HWVlZNhfe+fj4KDc316Lt2rVrMplMiomJ0YwZM9S4cWMtXrxYjzzyiD7++GPVrl32yyUn\nJ5ez+3AExt09GHfXY8zdg3F3D8bd9azH/NSP15Tm55ra5FaNvJSX56W8vDynPH9mTqH2Hr2gDk1y\n7V/sIBlZBQooTLV7naM/63bDsa+vb4kQLEm5ubny8/OzaKtTp47y8vI0ffp0DRw4UNJvi/n69Omj\nXbt26dZbby3ztSIiIsrTdzhAcnIy4+4GjLvrMebuwbi7B+PuerbGPMPrnIICfUt5hGOFhjj3+Ytn\npENDQp37Qr9zMT27xGy8tYp+1ssK1HZ3q2jevLkyMzOVkZFhbsvIyFBWVlaJUolmzZpJkkJC/vt/\nKCgoSI0aNdL58+fL3XEAAADAleyG47CwMPn7+1sk7F27dsnf319hYWEW13bp0kW+vr76/vvvzW2/\n/vqrLl26pDZt2jiw2wAAAIDjGSqrGDt2rGJjY5WYmCiTyaS4uDiNGzdOdevWVXp6uvLy8hQUFKR6\n9epp3LhxSkhIUIMGDdSsWTO99NJL6tSpk8VuFwAAAEBZcvMK7C5qPPXjNWV4lX/hY0AZ9xk6IW/6\n9OnKzs7WpEmT5O3traioKE2bNk2SFB8fr127dmnbtm3ma318fDRjxgxdu3ZNvXv31qpVq+Tt7b7N\nsgEAAFytKp6sV50YGac0P+8K1XXnXin9PkPh2MfHR7GxsYqNjS1xX0JCgsVtb29vTZs2zRyeAQAA\naiLr7d4k12/vhvIzdHw0AAAAUBMQjgEAAIAihGMAAACgiKGaYwAAANRsRo6n9oQjppk5BgAAQJm6\ndmis+gF1y7zmakaOvjv5q4t65DzMHAMAALiI9fZu1WVrt2G3trc7I2xvVrm6IBwDAAC4iPX2bmzt\nVvVQVgEAAAAUYeYYAADATWydomdLdSm/8ASEYwAAADexdYqeLdWl/MITdrSgrAIAAACV5ik7WjBz\nDAAAgErzlB0tmDkGAAAAijBzDAAAUMVV1/2RqyPCMQAAQBXH/siuQ1kFAAAAUIRwDAAAABShrAIA\nAKCasXV4SHWpQzayF7Lkvv2QCccAAADVjK3DQ6pDHXLXDo0N7XNcvB8y4RgAAAAey8heyJJ790Om\n5hgAAAAoQjgGAAAAilBWAQAA4AFsLdJzpOqy4K+yCMcAAAAewNYiPUeqDgv+HIGyCgAAAKAI4RgA\nAAAoQjgGAAAAihCOAQAAgCKEYwAAAKAI4RgAAAAoQjgGAAAAihCOAQAAgCKEYwAAAKAI4RgAAAAo\nwvHRAAAAqHKuZuTo5bV7yrwmLy9PdXaVfY0tU6Oalnof4RgAAAB21fOto4vp2WVek5tXoOZB9Sr9\nWl07NNZ3J3+t9PNUBOEYAAAAdvXoEmz3mq/2nXPIaw27tb2G3dre7nVHjx1VaEhouZ8/98rpUu+j\n5hgAAAAoQjgGAAAAihCOAQAAgCKEYwAAAKCIoQV5+fn5SkhI0ObNm1VQUKCRI0dqxowZ8vHxKXFt\namqqbr/99hLt3377rYKCgirfYwAAAMBJDIXjRYsWaceOHVqxYoXy8vIUHR2tOnXqaObMmSWu/eGH\nH1S/fn199tlnFu2NGjVyTI8BAAAAJ7EbjnNycrRu3TotXLhQ4eHhkqSYmBg9++yzmjJlinx9fS2u\nP378uDp06KCmTUvfXBkAAACex5V7ITuL3XCckpKizMxM9ezZ09wWGRmpzMxMpaSkmANzsRMnTqhD\nhw6O7ykAAACqNFfuhewsdhfkpaamyt/fX4GBgea2gIAA+fn56cKFCyWuP378uFJTUzV27Fj17dtX\nkyZN0unTpW+0DAAAAFQVdmeOs7KybC688/HxUW5ubon2kydPqn379po5c6Zq166tN954Q+PHj1dS\nUpLq169f5mslJyeXo+twFMbdPRh312PM3YNxdw/G3fUYc2NO/XhNaX7eDnu+o8eOlvsx7YN9S73P\nbjj29fW1GYJzc3Pl5+dXov2rr76St7e3OVC/+uqr6t+/v7Zs2aKxY8eW+VoRERH2ugMHS05OZtzd\ngHF3PcbcPRh392DcXY8xNy7D65yCAksPp+XhluOjmzdvrszMTGVkZJjbMjIylJWVpeDgknUlfn5+\nFjPNvr6+at26tc0SDAAAAKAqsRuOw8LC5O/vb/Gngl27dsnf319hYWEW16ampioiIkJ79uwxt2Vk\nZOjHH39Ux44dHdhtAAAAwPHshmNfX1+NHTtWsbGx2rNnj3bv3q24uDiNGzdOdevWVXp6ui5evChJ\nCg4O1vXXX6+4uDgdOHBAR44c0bPPPqsmTZrojjvucPqbAQAAACrD0CEg06dPV3Z2tiZNmiRvb29F\nRUVp2rRpkqT4+Hjt2rVL27ZtkyS99tprSkxM1KRJk5SVlaVbbrlFb731lurUqeO8dwEAAAA4gKFw\n7OPjo9jYWMXGxpa4LyEhweJ2UFCQEhMTHdM7AAAAwIUMhWMAAADAEYycoie57yQ9wjEAAABcxsgp\nepL7TtKzuyAPAAAAqCkIxwAAAEARwjEAAABQhHAMAAAAFCEcAwAAAEUIxwAAAEARwjEAAABQhHAM\nAAAAFCEcAwAAAEUIxwAAAEARwjEAAABQhHAMAAAAFCEcAwAAAEUIxwAAAEARwjEAAABQhHAMAAAA\nFCEcAwAAAEUIxwAAAEARwjEAAABQhHAMAAAAFCEcAwAAAEVqu7sDAAAAgLV6vnV0MT27zGsysgrs\nXmNLQBn3EY4BAABQ5fToEmz3moDCVEWEty73cycnp5Z6H2UVAAAAQBHCMQAAAFCEcAwAAAAUIRwD\nAAAARQjHAAAAQBHCMQAAAFCEcAwAAAAUIRwDAAAARQjHAAAAQBHCMQAAAFCEcAwAAAAUIRwDAAAA\nRQjHAAAAQJFaJpPJ5O5OSFJycrK7uwAAAIAaIiIiwmZ7lQnHAAAAgLtRVgEAAAAUIRwDAAAARQjH\nAAAAQBHCMQAAAFCEcAwAAAAUcXs4zs/PV1xcnHr37q2ePXsqLi5Oubm57u6WR7lw4YImT56sXr16\nqU+fPpo9e7auXr0qScrIyNBzzz2nHj16qE+fPnr99dfFBiaOt2jRIg0cONB8m8+98xQUFGjRokXq\n06ePevTooWnTpuny5cuSGHdnysjI0OzZs9W7d2/17t1b0dHRunLliiTG3dFMJpMeeeQRrV271qJ9\nxYoV6tu3r8LDwzV9+nTzz/liH3zwgQYOHKibb75Zjz32mC5cuODKbld7tsb9ypUriomJUd++fdWr\nVy9NnjxZqamp5vv57FdeaZ/3Yn/7298UGhpaot3e94O9F3WrxMRE05AhQ0x79+417dy50zRgwABT\nQkKCu7vlMfLz802jR482Pfzww6YjR46YDhw4YBo9erRp0qRJJpPJZJo8ebLpD3/4g+nw4cOmL7/8\n0tSzZ0/TmjVr3Nxrz/Ldd9+Zrr/+etOAAQPMbXzunWfBggWmPn36mL755hvToUOHTHfffbdp8uTJ\nJpOJcXem5557zjRmzBjTd999Z/ruu+9Mo0aNMj311FMmk4lxd6SCggLTvHnzTCEhIRY/q9euXWu6\n5ZZbTP/+979NBw8eNI0aNcr09NNPm+/fvn27qVu3bqakpCRTSkqKaeLEiaYxY8aYCgsL3fE2qp3S\nxv3xxx83jRkzxnTgwAHTkSNHTBMnTjSNHj3alJ+fbzKZ+OxXVmnjXuynn34yde/e3RQSEmLRbu/7\nwR63huPs7GxTt27dTF9++aW57csvvzR169bNlJWV5caeeY6DBw+aQkJCTP/5z3/MbXv27DGFhISY\nzp07ZwoNDTUdOXLEfN+7775rGjRokDu66pFycnJMI0aMMN1///3mcMzn3nnS09NNN954o2nr1q3m\ntm+//dY0ZMgQU3p6OuPuRBEREaZPP/3UfPuTTz4xdevWjc+7A505c8Z03333mQYMGGDq0aOHRVgY\nNGiQ6d133zXfPnLkiCkkJMR0/vx5k8lkMk2YMMH00ksvme+/ePGi6YYbbjDt3r3bdW+gmipt3H/9\n9VdTSEiIaf/+/eZrL1y4YAoJCTGlpKTw2a+ksj7vxSZOnGi6//77S4Rje98P9ri1rCIlJUWZmZnq\n2bOnuS0yMlKZmZlKSUlxY888R+vWrfXmm2+qadOm5rZatWpJkvbv36/AwECLP0f07NlTZ8+e1X/+\n8x+X99UTLVu2TNddd53uvPNOcxufe+fZs2ePvLy81K9fP3Nb7969tWXLFp04cYJxd6IGDRpo8+bN\nSk9PV0ZGhpKSktS1a1c+7w60f/9+hYSEaOPGjQoMDDS3p6Wl6ezZsxZjHBoaqgYNGmjv3r0qLCzU\ngQMH1KNHD/P9jRo1UufOnTmd1oDSxt3X11crV65Uly5dzG3F/75mZ2fz2a+k0sa92AcffKDLly/r\noYcesmi39/1gRO1K9bySUlNT5e/vb/GmAwIC5OfnRy2UgzRq1Ei33367Rdvq1avVtm1bpaamKjg4\n2Fr3SfcAAAixSURBVOK+Zs2aSfqtTrn4v1Exhw4d0oYNG/TJJ59oy5Yt5nY+985z+vRptWjRQtu3\nb9frr7+uixcv6rbbbtOsWbMYdyebN2+eZsyYoZ49e6pWrVpq1aqV1q1bp7179zLuDhIVFaWoqKgS\n7cU1rtY/s5s2baoLFy7oypUrysrKKvHzvvh+lK20cff397f4RVyS3n77bQUEBCg0NFT//ve/+exX\nQmnjLkk///yzFi5cqHfeeUc//fSTxX32vh+McOvMcVZWlnx8fEq0+/j4ULDuJCtXrtQ///lPzZ49\n2+b4F99m/CsnNzdX0dHRmjFjhsWsvcTn3pmuXbum//znP1q2bJmio6O1cOFCHT58WM888wzj7mSn\nT59Wx44dtXr1aq1evVr169fXc889x7i7QHZ2tiTZ/Hmem5tr9344RlJSklavXq1nn31Wfn5+fPad\nKCYmRuPHj7e5EM8Rn3e3zhz7+vra7Ghubq78/Pzc0CPPtmzZMi1ZskR//vOf1b9/f/3www8lxr/4\nNuNfOcuXL1dwcLBGjx5d4j4+985Tu3ZtXbt2TQkJCQoLC5MkxcfHa8yYMerduzfj7iSnT5/Wiy++\nqM8//1zt27eXJC1dulSDBg3Svffey7g7Wd26dSWVnNQoHmN796PyNm7cqJiYGD344IMaN26cJH7W\nO8sHH3ygtLQ0PfbYYzbvd8Tn3a3huHnz5srMzFRGRoYCAgIk/bYdkK0//6By4uPjtWbNGr3wwgvm\nb9zmzZsrLS3N4rriWmNKKirnk08+UVpamsLDwyVJeXl5ys/PV3h4uN58800+905S/Lnt1KmTua1j\nx46SfvvByLg7x6FDh+Tj42MOxpLUqlUrNWrUSD/++CPj7mTNmzeX9FutZVBQkLk9LS1NwcHBatSo\nkfz8/Er8vE9LS7OoQ0bFrF69Wi+99JIeffRRPfvss+Z2Mo5zfPLJJzp58qQiIyMl/bZ9pySFh4dr\n3rx5uuWWWySV/v1ghFvLKsLCwuTv72+xIGDXrl3y9/c3z/qg8l577TWtXbtWCQkJ5mAs/fZBunz5\nsk6cOGFu2717t9q0aVOiFADls2bNGm3evFmbNm3Spk2b9Nhjj6lZs2batGmTbrzxRj73TtK9e3dJ\n0uHDh81tx48flyQNGTKEcXeSZs2aKScnR6dOnTK3/fLLL7p8+bK6d+/OuDtZ06ZN1aZNG+3Zs8fc\ndvToUV29elXdunVTrVq1dNNNN1ncf+nSJR0/ftz8PYOK+fDDD/XSSy9p6tSpFsFYIuM4yyuvvKKk\npCTzv68xMTGSpE2bNmngwIF2vx+McHtZxdixYxUbG6vExESZTCbFxcVp3Lhx5mlxVM7hw4f1xhtv\naOLEierTp4/FzEGzZs00ePBgzZgxQ7GxsUpLS9PSpUs1depUN/bYM7Rq1cridqNGjVS7dm21bdtW\nkvjcO8l1112noUOHavbs2XrxxRdVp04dPf/88+rXr586duzIuDtJt27ddMMNN2j27Nn685//LC8v\nLyUkJOjGG29Ujx49GHcXeOCBB7RkyRK1atVKTZo00Zw5c3TnnXeqZcuWkqQJEybomWeeUUhIiLp0\n6aKXX35ZXbp0Yea4EtLS0hQXF6cRI0bonnvusfj3tUGDBmQcJ7Ge/T158qQkmf99lex/P9jj1nAs\nSdOnT1d2drYmTZokb29vRUVFadq0ae7ulsfYsmWLCgsLtWrVKq1atcrivk8//VTz58/XCy+8oPHj\nxysgIEAPPfSQxewynIPPvfMkJCQoMTFRjz32mAoKCjRo0CDNmTNHEuPuLLVr19bKlSuVkJCgRx99\nVCaTyXwap5eXF+PuAhMmTNClS5cUHR2t/Px89e/fX3PnzjXfXzwRsmjRIqWnp6tXr156/fXX3ddh\nD7B9+3ZlZWVp8+bN2rx5s8V9b7zxhgYMGMBn303sfT/YU8tk4qxgAAAAQHJzzTEAAABQlfz/9u6e\npZEojOL4EaIoNsKASloNvn2AWFmJYGkKESFoY5qkEoQEgy+gjfgFRCzS6SCYVBailaARQUFwFLFI\nEzCgMIbYJJPZZlh2WWVXdLO6/n/VMDBn5jbD4fLApRwDAAAAHsoxAAAA4KEcAwAAAB7KMQAAAOCh\nHAMAAAAeyjEA1FixWFQ0GtXd3Z2mpqbenJfNZhUOhyVJs7Ozuri4eHMmAHxV//wQEAD4amzblmVZ\namtr0/r6+rtmLy8vv2seAHw1lGMAqLGlpSUVCgVFo1FZlqWDgwPF43E1NTXp8vJSj4+Pmp6eViaT\n0dXVlQYHBxWPx+U4jlZWVnRyciLHcRQKhTQ5OflTdjgcViwWkyStra2psbFRt7e36urq0urqqhoa\nGpROp5VKpVStVtXX16f5+XmOswUAD2MVAFBjyWRSra2tSiQSP90vFAra2tpSJBJRIpHQ4uKi0um0\nTNNUsViUaZqSpJ2dHW1vb2t/f1+np6cvvufs7Exzc3Pa3d1VPp/X4eGhbm5uZJqmNjc3lclkZBiG\nNjY2/up6AeAzYecYAD6IgYEBSZLf71cgEJBhGJKklpYW2bato6MjWZal4+NjSdLT05Our6/V2dn5\nbF4gEFB7e7skqaOjQ7ZtK5/PK5fLaXR0VJJULpfV29v7t5cGAJ8G5RgAPoj6+vrv1z7fr79nx3E0\nMzOjoaEhSdLDw4Oam5t1fn7+bN6PoxJ1dXVyXVeO42h4eFjJZFKSVCqV5DjOey4DAD41xioAoMZ8\nPp8qlcqrn+vv75dpmiqXyyqVShofH3+xGL8kGAxqb29P9/f3cl1XCwsLSqVSr/4WAPhfsXMMADVm\nGIb8fv8vM8e/MzY2plwup5GREVUqFYVCIQWDQWWz2T/O6O7uViwW08TEhKrVqnp6ehSJRF67BAD4\nb9W5ruv+648AAAAAPgLGKgAAAAAP5RgAAADwUI4BAAAAD+UYAAAA8FCOAQAAAA/lGAAAAPBQjgEA\nAAAP5RgAAADwfANvDiaBV+nwEAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2a58e358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lifelines import KaplanMeierFitter\n",
    "kmf = KaplanMeierFitter()\n",
    "kmf.fit(T, event_observed=E)  # or, more succiently, kmf.fit(T, E)\n",
    "kmf.plot()\n",
    "plt.title('KM Survival Function using v1 churn def')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graph tell us the probability of a customer still being a customer (y-axis) after n months (x-axis). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And this is how `v1` compares to `v2` churn definition:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1a162e3080>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a16371b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_two_churn_defs(df)\n",
    "plt.title('KM Survival Function using v1 and v2 churn definitions')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As these two curves are not very different given their uncertainty bands, we will limit the results to just v1 definition which is easier to understand and simpler in terms of coding."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KM curves by vertical\n",
    "\n",
    "Can create different KM curves for each subset of subscribers you want to look at.  The statistical significance of the differences can be tested in many ways, including the Log-Rank test, which we’ll apply below. The Log-Rank test simply evaluates whether the underlying population survival curves for the two sampled groups are likely to be the same. The p-value is essentially the probability that the curves are the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 598,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gLy/P54XHIos1sKUW7hSTJiLb7TdVpO7YjjXD6UEdKaROaGR5CZF/SEmMsPSC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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a29fc8f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fit a separate KM model to each vertical\n",
    "vertical_types = np.unique(df['vertical'])\n",
    "\n",
    "# Store results in this dict\n",
    "kmf_dict = {}\n",
    "mask_dict = {}\n",
    "\n",
    "for i, _type in enumerate(vertical_types):\n",
    "    mask = (df['vertical'] == _type)\n",
    "    kmf.fit(T[mask], E[mask], label=_type)\n",
    "    # if model not yet in dict, add it\n",
    "    if _type not in kmf_dict:\n",
    "        kmf_dict[_type] = kmf\n",
    "    if _type not in mask_dict:\n",
    "        mask_dict[_type] = mask        \n",
    "    if i == 0:\n",
    "        ax = kmf.plot()\n",
    "    else:\n",
    "        kmf.plot(ax=ax)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Both accountancy and freelance developer seem to have very similar churn profiles\n",
    "* Most interesting is the separation of verticals between gym/fitness and the rest (accountancy + freelance developer)\n",
    "* This tells that gym membership direct debit payments are very regular with significant higher chance of retention\n",
    "* In the 3 cases we see large uncertainty bands surrounding the profile, given the small dataset and large censorship"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Statistical significance of the different curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 599,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['accountancy', 'freelance developer', 'gym/fitness'])"
      ]
     },
     "execution_count": 599,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask_dict.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 606,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#########   Results for accountancy   #########\n",
      "\n",
      "t_0=-1, alpha=0.99, null_distribution=chi squared, df=1\n",
      "\n",
      "test_statistic      p     \n",
      "       13.3654 0.0003  ***\n",
      "---\n",
      "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 \n",
      "None\n",
      "#########   Results for freelance developer   #########\n",
      "\n",
      "t_0=-1, alpha=0.99, null_distribution=chi squared, df=1\n",
      "\n",
      "test_statistic      p     \n",
      "       35.2654 0.0000  ***\n",
      "---\n",
      "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 \n",
      "None\n",
      "#########   Results for gym/fitness   #########\n",
      "\n",
      "t_0=-1, alpha=0.99, null_distribution=chi squared, df=1\n",
      "\n",
      "test_statistic      p     \n",
      "       75.0904 0.0000  ***\n",
      "---\n",
      "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 \n",
      "None\n"
     ]
    }
   ],
   "source": [
    "from lifelines.statistics import logrank_test\n",
    "\n",
    "for _type in mask_dict:\n",
    "    mask = mask_dict[_type]\n",
    "    results = logrank_test(T[mask], T[~mask], E[mask], E[~mask], alpha=.99)\n",
    "    print('#########   Results for {}   #########'.format(_type))\n",
    "    print(results.print_summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "They all seem to be significant..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prioritising at-risk customers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we have 3 models for each of the verticals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accountancy': <lifelines.KaplanMeierFitter: fitted with 366 observations, 320 censored>,\n",
       " 'freelance developer': <lifelines.KaplanMeierFitter: fitted with 366 observations, 320 censored>,\n",
       " 'gym/fitness': <lifelines.KaplanMeierFitter: fitted with 366 observations, 320 censored>}"
      ]
     },
     "execution_count": 509,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmf_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's estimate the churn probabilities for each of the verticals then:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 561,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimating accountancy probabilities...\n",
      "Estimating freelance developer probabilities...\n",
      "Estimating gym/fitness probabilities...\n"
     ]
    }
   ],
   "source": [
    "# Estimate the churn probabilities for each of the verticals\n",
    "df['churn_prob'] = np.nan\n",
    "for i, _type in enumerate(vertical_types):\n",
    "    print('Estimating {} probabilities...'.format(_type))\n",
    "    mask = (df['vertical'] == _type)\n",
    "    # Use lambda function to loop over all durations and apply the correct KM curve\n",
    "    # Note churn prob is 1 - survival prob.\n",
    "    df.loc[mask, 'churn_prob'] = df.loc[mask, 'T'].apply(lambda x: 1 - kmf_dict[_type].predict(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assuming the business model in GC is that customers with the highest number of payments throughout the year, are the most valuable, we can sort by this metric first and then by churn risk from highest to lowest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 596,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company_id</th>\n",
       "      <th>vertical</th>\n",
       "      <th>total_payments</th>\n",
       "      <th>churn_prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>456</td>\n",
       "      <td>gym/fitness</td>\n",
       "      <td>14380.0</td>\n",
       "      <td>0.115359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>831</th>\n",
       "      <td>928</td>\n",
       "      <td>gym/fitness</td>\n",
       "      <td>9363.0</td>\n",
       "      <td>0.115359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>795</th>\n",
       "      <td>885</td>\n",
       "      <td>gym/fitness</td>\n",
       "      <td>9040.0</td>\n",
       "      <td>0.099438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>50</td>\n",
       "      <td>gym/fitness</td>\n",
       "      <td>7069.0</td>\n",
       "      <td>0.221457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>837</th>\n",
       "      <td>935</td>\n",
       "      <td>gym/fitness</td>\n",
       "      <td>3675.0</td>\n",
       "      <td>0.099438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>286</td>\n",
       "      <td>freelance developer</td>\n",
       "      <td>14157.0</td>\n",
       "      <td>0.115359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>358</td>\n",
       "      <td>freelance developer</td>\n",
       "      <td>3356.0</td>\n",
       "      <td>0.102984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>603</td>\n",
       "      <td>freelance developer</td>\n",
       "      <td>2492.0</td>\n",
       "      <td>0.038251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>857</td>\n",
       "      <td>freelance developer</td>\n",
       "      <td>2071.0</td>\n",
       "      <td>0.146201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>159</td>\n",
       "      <td>freelance developer</td>\n",
       "      <td>1978.0</td>\n",
       "      <td>0.102984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>677</td>\n",
       "      <td>accountancy</td>\n",
       "      <td>4847.0</td>\n",
       "      <td>0.110957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>111</td>\n",
       "      <td>accountancy</td>\n",
       "      <td>4105.0</td>\n",
       "      <td>0.121678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>675</td>\n",
       "      <td>accountancy</td>\n",
       "      <td>2810.0</td>\n",
       "      <td>0.160672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>177</td>\n",
       "      <td>accountancy</td>\n",
       "      <td>2549.0</td>\n",
       "      <td>0.016393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>65</td>\n",
       "      <td>accountancy</td>\n",
       "      <td>2507.0</td>\n",
       "      <td>0.121678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     company_id             vertical  total_payments  churn_prob\n",
       "415         456          gym/fitness         14380.0    0.115359\n",
       "831         928          gym/fitness          9363.0    0.115359\n",
       "795         885          gym/fitness          9040.0    0.099438\n",
       "47           50          gym/fitness          7069.0    0.221457\n",
       "837         935          gym/fitness          3675.0    0.099438\n",
       "261         286  freelance developer         14157.0    0.115359\n",
       "324         358  freelance developer          3356.0    0.102984\n",
       "544         603  freelance developer          2492.0    0.038251\n",
       "770         857  freelance developer          2071.0    0.146201\n",
       "150         159  freelance developer          1978.0    0.102984\n",
       "610         677          accountancy          4847.0    0.110957\n",
       "105         111          accountancy          4105.0    0.121678\n",
       "608         675          accountancy          2810.0    0.160672\n",
       "167         177          accountancy          2549.0    0.016393\n",
       "61           65          accountancy          2507.0    0.121678"
      ]
     },
     "execution_count": 596,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sorting by value (assuming this is proportional to total payments)\n",
    "temp = df.sort_values(['vertical', 'total_payments', 'churn_prob'], ascending=[False, False, False])\n",
    "temp = temp[['company_id', 'vertical', 'total_payments', 'churn_prob']]\n",
    "temp.groupby('vertical').head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sorting by highest churn risk gives a different result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 597,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>823</th>\n",
       "      <td>920</td>\n",
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       "      <th>93</th>\n",
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       "      <th>680</th>\n",
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       "      <td>621.0</td>\n",
       "      <td>0.221457</td>\n",
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       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>343</td>\n",
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       "      <td>607.0</td>\n",
       "      <td>0.221457</td>\n",
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       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>311</td>\n",
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       "      <td>169.0</td>\n",
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       "      <th>26</th>\n",
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       "      <td>57.0</td>\n",
       "      <td>0.221457</td>\n",
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       "      <th>281</th>\n",
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       "      <td>189.0</td>\n",
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       "      <td>0.221457</td>\n",
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      ],
      "text/plain": [
       "     company_id             vertical  total_payments  churn_prob\n",
       "47           50          gym/fitness          7069.0    0.221457\n",
       "823         920          gym/fitness          2901.0    0.221457\n",
       "93           97          gym/fitness           616.0    0.221457\n",
       "283         310          gym/fitness           517.0    0.221457\n",
       "860         959          gym/fitness           287.0    0.221457\n",
       "14           16  freelance developer           867.0    0.221457\n",
       "680         752  freelance developer           621.0    0.221457\n",
       "312         343  freelance developer           607.0    0.221457\n",
       "284         311  freelance developer           169.0    0.221457\n",
       "26           28  freelance developer            57.0    0.221457\n",
       "281         307          accountancy           887.0    0.221457\n",
       "717         790          accountancy           189.0    0.221457\n",
       "51           54          accountancy           151.0    0.221457\n",
       "318         351          accountancy           135.0    0.221457\n",
       "73           77          accountancy           112.0    0.221457"
      ]
     },
     "execution_count": 597,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sorting by highest churn risk\n",
    "temp = df.sort_values(['vertical', 'churn_prob', 'total_payments'], ascending=[False, False, False])\n",
    "temp = temp[['company_id', 'vertical', 'total_payments', 'churn_prob']]\n",
    "temp.groupby('vertical').head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Either of these two lists could be used to take actions against churn!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ideas for Future work\n",
    "\n",
    "Due to the short time spent working on this project, the below are left as avenues for future work:\n",
    "\n",
    "1) Remove **outliers** in the payments and mandates data where applicable and see the effect from this in the current model\n",
    "\n",
    "2) Extracting **seasonality** using fft (Fast Fourier Transforms) or `statsmodels` `seasonal_decompose`\n",
    "\n",
    "\n",
    "3) Understand how **mandates** could be incorporated into the model (so far only used payments)\n",
    "\n",
    "\n",
    "4) KM curves for different duration bins\n",
    "\n",
    "\n",
    "5) Implement a **Survival Regression model** like the Cox and Aalen model from `lifelines` (see http://lifelines.readthedocs.io/en/latest/Survival%20Regression.html) \n",
    "\n",
    "\n",
    "6) Do more **feature engineering** with this data. This would be particularly important for (5). For example using Exponential Moving Averages (EMAs) to give more weights to recent data; and using lagged values from the time series\n",
    "\n",
    "\n",
    "7) Compare Survival Analysis results with **supervised binary classifier** models\n",
    "\n",
    "\n",
    "8) Also could try an unsupervised model, like **k-means clustering** to find n=2 clusters, where one cluster would be churned and the other non-churned. This would require a lot of (6)\n",
    "\n",
    "\n",
    "9) Do a proper train-test split of the data and/or **cross-validation** - this would be particularly importan for (5), (7) and (8)\n"
   ]
  }
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