Relax_data_science_challenge.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading takehome_users file\n",
    "users = pd.read_csv('takehome_users.csv', encoding = 'ISO-8859-1', index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>creation_time</th>\n",
       "      <th>name</th>\n",
       "      <th>email</th>\n",
       "      <th>creation_source</th>\n",
       "      <th>last_session_creation_time</th>\n",
       "      <th>opted_in_to_mailing_list</th>\n",
       "      <th>enabled_for_marketing_drip</th>\n",
       "      <th>org_id</th>\n",
       "      <th>invited_by_user_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>object_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-04-22 03:53:30</td>\n",
       "      <td>Clausen August</td>\n",
       "      <td>AugustCClausen@yahoo.com</td>\n",
       "      <td>GUEST_INVITE</td>\n",
       "      <td>1.398139e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>10803.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-11-15 03:45:04</td>\n",
       "      <td>Poole Matthew</td>\n",
       "      <td>MatthewPoole@gustr.com</td>\n",
       "      <td>ORG_INVITE</td>\n",
       "      <td>1.396238e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>316.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-03-19 23:14:52</td>\n",
       "      <td>Bottrill Mitchell</td>\n",
       "      <td>MitchellBottrill@gustr.com</td>\n",
       "      <td>ORG_INVITE</td>\n",
       "      <td>1.363735e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>1525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-05-21 08:09:28</td>\n",
       "      <td>Clausen Nicklas</td>\n",
       "      <td>NicklasSClausen@yahoo.com</td>\n",
       "      <td>GUEST_INVITE</td>\n",
       "      <td>1.369210e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5151.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013-01-17 10:14:20</td>\n",
       "      <td>Raw Grace</td>\n",
       "      <td>GraceRaw@yahoo.com</td>\n",
       "      <td>GUEST_INVITE</td>\n",
       "      <td>1.358850e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>193</td>\n",
       "      <td>5240.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 creation_time               name                       email  \\\n",
       "object_id                                                                       \n",
       "1          2014-04-22 03:53:30     Clausen August    AugustCClausen@yahoo.com   \n",
       "2          2013-11-15 03:45:04      Poole Matthew      MatthewPoole@gustr.com   \n",
       "3          2013-03-19 23:14:52  Bottrill Mitchell  MitchellBottrill@gustr.com   \n",
       "4          2013-05-21 08:09:28    Clausen Nicklas   NicklasSClausen@yahoo.com   \n",
       "5          2013-01-17 10:14:20          Raw Grace          GraceRaw@yahoo.com   \n",
       "\n",
       "          creation_source  last_session_creation_time  \\\n",
       "object_id                                               \n",
       "1            GUEST_INVITE                1.398139e+09   \n",
       "2              ORG_INVITE                1.396238e+09   \n",
       "3              ORG_INVITE                1.363735e+09   \n",
       "4            GUEST_INVITE                1.369210e+09   \n",
       "5            GUEST_INVITE                1.358850e+09   \n",
       "\n",
       "           opted_in_to_mailing_list  enabled_for_marketing_drip  org_id  \\\n",
       "object_id                                                                 \n",
       "1                                 1                           0      11   \n",
       "2                                 0                           0       1   \n",
       "3                                 0                           0      94   \n",
       "4                                 0                           0       1   \n",
       "5                                 0                           0     193   \n",
       "\n",
       "           invited_by_user_id  \n",
       "object_id                      \n",
       "1                     10803.0  \n",
       "2                       316.0  \n",
       "3                      1525.0  \n",
       "4                      5151.0  \n",
       "5                      5240.0  "
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading takehome_user_engagement file\n",
    "engagement = pd.read_csv('takehome_user_engagement.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "engagement['time_stamp'] = pd.to_datetime(engagement.time_stamp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 207917 entries, 0 to 207916\n",
      "Data columns (total 3 columns):\n",
      "time_stamp    207917 non-null datetime64[ns]\n",
      "user_id       207917 non-null int64\n",
      "visited       207917 non-null int64\n",
      "dtypes: datetime64[ns](1), int64(2)\n",
      "memory usage: 4.8 MB\n"
     ]
    }
   ],
   "source": [
    "engagement.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>2014-04-22 03:53:30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-11-15 03:45:04</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-11-29 03:45:04</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-12-09 03:45:04</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-12-25 03:45:04</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "           time_stamp  user_id  visited\n",
       "0 2014-04-22 03:53:30        1        1\n",
       "1 2013-11-15 03:45:04        2        1\n",
       "2 2013-11-29 03:45:04        2        1\n",
       "3 2013-12-09 03:45:04        2        1\n",
       "4 2013-12-25 03:45:04        2        1"
      ]
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     "execution_count": 209,
     "metadata": {},
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   ],
   "source": [
    "engagement.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**building two new columns with 2 previous values of the login times**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding two new columns to the engagement dataframe, with two last previous login times for each user\n",
    "engagement['previous_1'] = engagement.groupby('user_id')['time_stamp'].shift(1)\n",
    "engagement['previous_2'] = engagement.groupby('user_id')['time_stamp'].shift(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
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       "      <th>previous_2</th>\n",
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       "      <th>0</th>\n",
       "      <td>2014-04-22 03:53:30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-11-15 03:45:04</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-11-29 03:45:04</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-11-15 03:45:04</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-12-09 03:45:04</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-11-29 03:45:04</td>\n",
       "      <td>2013-11-15 03:45:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-12-25 03:45:04</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-12-09 03:45:04</td>\n",
       "      <td>2013-11-29 03:45:04</td>\n",
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      ],
      "text/plain": [
       "           time_stamp  user_id  visited          previous_1  \\\n",
       "0 2014-04-22 03:53:30        1        1                 NaT   \n",
       "1 2013-11-15 03:45:04        2        1                 NaT   \n",
       "2 2013-11-29 03:45:04        2        1 2013-11-15 03:45:04   \n",
       "3 2013-12-09 03:45:04        2        1 2013-11-29 03:45:04   \n",
       "4 2013-12-25 03:45:04        2        1 2013-12-09 03:45:04   \n",
       "\n",
       "           previous_2  \n",
       "0                 NaT  \n",
       "1                 NaT  \n",
       "2                 NaT  \n",
       "3 2013-11-15 03:45:04  \n",
       "4 2013-11-29 03:45:04  "
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "engagement.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**calcuating the difference between two consecutive times and difference between 1st and third login for each observation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Subtracting the login time from two times before that and putting it into a new columns called delta\n",
    "engagement['delta1'] = engagement.time_stamp - engagement.previous_1\n",
    "engagement['delta2'] = engagement.previous_1 - engagement.previous_2\n",
    "engagement['delta3'] = engagement.time_stamp - engagement.previous_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>time_stamp</th>\n",
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       "      <th>visited</th>\n",
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       "      <th>0</th>\n",
       "      <td>2014-04-22 03:53:30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-11-15 03:45:04</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-11-29 03:45:04</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-11-15 03:45:04</td>\n",
       "      <td>NaT</td>\n",
       "      <td>14 days</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-12-09 03:45:04</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-11-29 03:45:04</td>\n",
       "      <td>2013-11-15 03:45:04</td>\n",
       "      <td>10 days</td>\n",
       "      <td>14 days</td>\n",
       "      <td>24 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-12-25 03:45:04</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-12-09 03:45:04</td>\n",
       "      <td>2013-11-29 03:45:04</td>\n",
       "      <td>16 days</td>\n",
       "      <td>10 days</td>\n",
       "      <td>26 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           time_stamp  user_id  visited          previous_1  \\\n",
       "0 2014-04-22 03:53:30        1        1                 NaT   \n",
       "1 2013-11-15 03:45:04        2        1                 NaT   \n",
       "2 2013-11-29 03:45:04        2        1 2013-11-15 03:45:04   \n",
       "3 2013-12-09 03:45:04        2        1 2013-11-29 03:45:04   \n",
       "4 2013-12-25 03:45:04        2        1 2013-12-09 03:45:04   \n",
       "\n",
       "           previous_2  delta1  delta2  delta3  \n",
       "0                 NaT     NaT     NaT     NaT  \n",
       "1                 NaT     NaT     NaT     NaT  \n",
       "2                 NaT 14 days     NaT     NaT  \n",
       "3 2013-11-15 03:45:04 10 days 14 days 24 days  \n",
       "4 2013-11-29 03:45:04 16 days 10 days 26 days  "
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "engagement.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**choosing the user_id based on the below conditions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "# user_id of the people who had logged into the product for 3 separate days in at least 1 seven-day period\n",
    "ids = engagement[(engagement.delta1.dt.days>=1) & (engagement.delta2.dt.days>=1) & (engagement.delta3.dt.days<=7)].user_id.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Storing the user_id of the people\n",
    "ids = engagement[engagement.delta.dt.days <=7].user_id.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# building a dataframe from user_id's who were adopted users and giving them label value of 1\n",
    "adopted_df = pd.DataFrame(np.ones(len(ids)), index = ids, columns=['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reeidexing the above dataframe to include all the user_id's, the new user_id's will have NA as the label value\n",
    "adopted_df = adopted_df.reindex(users.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling NA values of the non-adopted users as 0\n",
    "adopted_df.fillna(0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "      <th>label</th>\n",
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       "    <tr>\n",
       "      <th>object_id</th>\n",
       "      <th></th>\n",
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       "      <td>0.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           label\n",
       "object_id       \n",
       "1            0.0\n",
       "2            1.0\n",
       "3            0.0\n",
       "4            0.0\n",
       "5            0.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adopted_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <td>2014-04-22 03:53:30</td>\n",
       "      <td>Clausen August</td>\n",
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       "      <td>11</td>\n",
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       "      <th>2</th>\n",
       "      <td>2013-11-15 03:45:04</td>\n",
       "      <td>Poole Matthew</td>\n",
       "      <td>MatthewPoole@gustr.com</td>\n",
       "      <td>ORG_INVITE</td>\n",
       "      <td>1.396238e+09</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>316.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-03-19 23:14:52</td>\n",
       "      <td>Bottrill Mitchell</td>\n",
       "      <td>MitchellBottrill@gustr.com</td>\n",
       "      <td>ORG_INVITE</td>\n",
       "      <td>1.363735e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
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       "      <td>2013-05-21 08:09:28</td>\n",
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       "      <td>0</td>\n",
       "      <td>193</td>\n",
       "      <td>5240.0</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "                 creation_time               name                       email  \\\n",
       "object_id                                                                       \n",
       "1          2014-04-22 03:53:30     Clausen August    AugustCClausen@yahoo.com   \n",
       "2          2013-11-15 03:45:04      Poole Matthew      MatthewPoole@gustr.com   \n",
       "3          2013-03-19 23:14:52  Bottrill Mitchell  MitchellBottrill@gustr.com   \n",
       "4          2013-05-21 08:09:28    Clausen Nicklas   NicklasSClausen@yahoo.com   \n",
       "5          2013-01-17 10:14:20          Raw Grace          GraceRaw@yahoo.com   \n",
       "\n",
       "          creation_source  last_session_creation_time  \\\n",
       "object_id                                               \n",
       "1            GUEST_INVITE                1.398139e+09   \n",
       "2              ORG_INVITE                1.396238e+09   \n",
       "3              ORG_INVITE                1.363735e+09   \n",
       "4            GUEST_INVITE                1.369210e+09   \n",
       "5            GUEST_INVITE                1.358850e+09   \n",
       "\n",
       "           opted_in_to_mailing_list  enabled_for_marketing_drip  org_id  \\\n",
       "object_id                                                                 \n",
       "1                                 1                           0      11   \n",
       "2                                 0                           0       1   \n",
       "3                                 0                           0      94   \n",
       "4                                 0                           0       1   \n",
       "5                                 0                           0     193   \n",
       "\n",
       "           invited_by_user_id  \n",
       "object_id                      \n",
       "1                     10803.0  \n",
       "2                       316.0  \n",
       "3                      1525.0  \n",
       "4                      5151.0  \n",
       "5                      5240.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**adding the label column to user dataframe, with adopted users having 1 and non-adopted users having 0 as label value**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merging the labels with the user dataframe\n",
    "final_df = pd.merge(adopted_df, users, left_index = True, right_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
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       "      <td>316.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>Bottrill Mitchell</td>\n",
       "      <td>MitchellBottrill@gustr.com</td>\n",
       "      <td>ORG_INVITE</td>\n",
       "      <td>1.363735e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-05-21 08:09:28</td>\n",
       "      <td>Clausen Nicklas</td>\n",
       "      <td>NicklasSClausen@yahoo.com</td>\n",
       "      <td>GUEST_INVITE</td>\n",
       "      <td>1.369210e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5151.0</td>\n",
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       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-01-17 10:14:20</td>\n",
       "      <td>Raw Grace</td>\n",
       "      <td>GraceRaw@yahoo.com</td>\n",
       "      <td>GUEST_INVITE</td>\n",
       "      <td>1.358850e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>193</td>\n",
       "      <td>5240.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           label        creation_time               name  \\\n",
       "object_id                                                  \n",
       "1            0.0  2014-04-22 03:53:30     Clausen August   \n",
       "2            1.0  2013-11-15 03:45:04      Poole Matthew   \n",
       "3            0.0  2013-03-19 23:14:52  Bottrill Mitchell   \n",
       "4            0.0  2013-05-21 08:09:28    Clausen Nicklas   \n",
       "5            0.0  2013-01-17 10:14:20          Raw Grace   \n",
       "\n",
       "                                email creation_source  \\\n",
       "object_id                                               \n",
       "1            AugustCClausen@yahoo.com    GUEST_INVITE   \n",
       "2              MatthewPoole@gustr.com      ORG_INVITE   \n",
       "3          MitchellBottrill@gustr.com      ORG_INVITE   \n",
       "4           NicklasSClausen@yahoo.com    GUEST_INVITE   \n",
       "5                  GraceRaw@yahoo.com    GUEST_INVITE   \n",
       "\n",
       "           last_session_creation_time  opted_in_to_mailing_list  \\\n",
       "object_id                                                         \n",
       "1                        1.398139e+09                         1   \n",
       "2                        1.396238e+09                         0   \n",
       "3                        1.363735e+09                         0   \n",
       "4                        1.369210e+09                         0   \n",
       "5                        1.358850e+09                         0   \n",
       "\n",
       "           enabled_for_marketing_drip  org_id  invited_by_user_id  \n",
       "object_id                                                          \n",
       "1                                   0      11             10803.0  \n",
       "2                                   0       1               316.0  \n",
       "3                                   0      94              1525.0  \n",
       "4                                   0       1              5151.0  \n",
       "5                                   0     193              5240.0  "
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "#final_df.last_session_creation_time.fillna(final_df.last_session_creation_time.mean(), inplace = True)\n",
    "final_df.dropna(subset = ['last_session_creation_time'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding new columns of month, day and year from creation_time column\n",
    "final_df['creation_time'] = pd.to_datetime(final_df.creation_time, infer_datetime_format = True)\n",
    "final_df['month'] = final_df.creation_time.dt.month\n",
    "final_df['day'] = final_df.creation_time.dt.day\n",
    "final_df['year'] = final_df.creation_time.dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping 4 columns, name and email columns have no information\n",
    "# invited_by_user_id column has around 50% NA values and creation_time column is replaced by year, month and day columns\n",
    "final_df.drop(['name', 'email', 'creation_time', 'invited_by_user_id'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating dummy variables from columns below\n",
    "final_df_dummy = pd.get_dummies(final_df, columns = ['creation_source', 'year', 'month', 'day'], drop_first = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.0</td>\n",
       "      <td>1.369210e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.358850e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>193</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           label  last_session_creation_time  opted_in_to_mailing_list  \\\n",
       "object_id                                                                \n",
       "1            0.0                1.398139e+09                         1   \n",
       "2            1.0                1.396238e+09                         0   \n",
       "3            0.0                1.363735e+09                         0   \n",
       "4            0.0                1.369210e+09                         0   \n",
       "5            0.0                1.358850e+09                         0   \n",
       "\n",
       "           enabled_for_marketing_drip  org_id  creation_source_ORG_INVITE  \\\n",
       "object_id                                                                   \n",
       "1                                   0      11                           0   \n",
       "2                                   0       1                           1   \n",
       "3                                   0      94                           1   \n",
       "4                                   0       1                           0   \n",
       "5                                   0     193                           0   \n",
       "\n",
       "           creation_source_PERSONAL_PROJECTS  creation_source_SIGNUP  \\\n",
       "object_id                                                              \n",
       "1                                          0                       0   \n",
       "2                                          0                       0   \n",
       "3                                          0                       0   \n",
       "4                                          0                       0   \n",
       "5                                          0                       0   \n",
       "\n",
       "           creation_source_SIGNUP_GOOGLE_AUTH  year_2013   ...    day_22  \\\n",
       "object_id                                                  ...             \n",
       "1                                           0          0   ...         1   \n",
       "2                                           0          1   ...         0   \n",
       "3                                           0          1   ...         0   \n",
       "4                                           0          1   ...         0   \n",
       "5                                           0          1   ...         0   \n",
       "\n",
       "           day_23  day_24  day_25  day_26  day_27  day_28  day_29  day_30  \\\n",
       "object_id                                                                   \n",
       "1               0       0       0       0       0       0       0       0   \n",
       "2               0       0       0       0       0       0       0       0   \n",
       "3               0       0       0       0       0       0       0       0   \n",
       "4               0       0       0       0       0       0       0       0   \n",
       "5               0       0       0       0       0       0       0       0   \n",
       "\n",
       "           day_31  \n",
       "object_id          \n",
       "1               0  \n",
       "2               0  \n",
       "3               0  \n",
       "4               0  \n",
       "5               0  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df_dummy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import precision_recall_curve, average_precision_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# random forest classifier\n",
    "rf = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = final_df_dummy.label\n",
    "X = final_df_dummy.drop('label', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting the data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# performing cross validation using the random forest classifier\n",
    "cross_val = cross_val_score(rf, X_train, y_train, cv = 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**accuracy values, calculated using cross validation on the training data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.94475921, 0.94334278, 0.94617564, 0.94617564, 0.9490085 ,\n",
       "       0.93767705, 0.95184136, 0.93626062, 0.94184397, 0.94893617])"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**accuracy on test data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# accuracy value performed on test data\n",
    "rf.score(X_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**f1 score on test data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8494983277591973"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# f1_score value on test data\n",
    "f1_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**classification report**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      " non-adopted       0.95      0.99      0.97      1440\n",
      "     adopted       0.93      0.78      0.85       325\n",
      "\n",
      "    accuracy                           0.95      1765\n",
      "   macro avg       0.94      0.88      0.91      1765\n",
      "weighted avg       0.95      0.95      0.95      1765\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred, target_names = ['non-adopted', 'adopted']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculating the values to calcuate precision_recall curve\n",
    "precision, recall, thresholds = precision_recall_curve(y_test, rf.predict_proba(X_test)[:, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "middle_point = np.argmin(np.abs(thresholds - .5))\n",
    "plt.plot(precision[middle_point], recall[middle_point], 'ro', fillstyle = 'none', markersize = 10, mew = 2,\n",
    "         label = 'Default Prediction Threshold')\n",
    "plt.plot(precision, recall, label = \"Precision Recall Curve\")\n",
    "plt.xlabel('Precision')\n",
    "plt.ylabel('Recall')\n",
    "plt.title(\"Precision Recall Curve for Adopted Users Class\")\n",
    "plt.legend()\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**area under the precision_recall curve, as a measure of how good the model performes from 0 (bad) to 1 (good)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Precision Score of the model: 0.93\n"
     ]
    }
   ],
   "source": [
    "av_pr = average_precision_score(y_test, rf.predict_proba(X_test)[:, 1])\n",
    "print(\"Average Precision Score of the model: {:.2f}\".format(av_pr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**most important features of the model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importances = list(zip(X.columns, rf.feature_importances_))\n",
    "features_sorted = sorted(feature_importances, key = lambda x: x[1], reverse = True)\n",
    "features, scores = zip(*features_sorted)\n",
    "features_df = pd.DataFrame(np.array(scores), index = features, columns = [\"value\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20c58dd46d8>"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features_df[:10].plot(kind = 'barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}