Merchant Lead Conversion - HJ.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T12:41:29.047159Z",
     "start_time": "2019-04-24T12:41:21.409583Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "\n",
    "sns.set()\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T12:44:50.114465Z",
     "start_time": "2019-04-24T12:44:50.070583Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "leads = pd.read_csv('./brazilian-ecommerce/olist_marketing_qualified_leads_dataset.csv',\n",
    "                    parse_dates=['first_contact_date'])\n",
    "closed = pd.read_csv('./brazilian-ecommerce/olist_closed_deals_dataset.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA\n",
    "\n",
    "## First Contact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T12:44:51.100866Z",
     "start_time": "2019-04-24T12:44:51.078887Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test = leads.assign(dummy=1)\\\n",
    "            .pivot_table(columns='origin', index='first_contact_date', aggfunc='sum')\\\n",
    "            .fillna(0)\\\n",
    "            .resample('M')\\\n",
    "            .sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T12:44:51.507741Z",
     "start_time": "2019-04-24T12:44:51.504750Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test.columns = test.columns.get_level_values(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T12:44:52.374461Z",
     "start_time": "2019-04-24T12:44:52.081206Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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GC+JVCsud/Cchg1XJWYzqGMLAdgF4qGoDXqHpMkyTrZk2Vh3NorTFt6FUCCOT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\n2hnlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test.plot.area(figsize=(12,6));\n",
    "plt.ylabel('Number of leads')\n",
    "plt.xlabel('First contact date (month)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Landing Page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T12:44:54.070885Z",
     "start_time": "2019-04-24T12:44:53.914304Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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sczmPgmwux4Mbd3H68fNd5USSJEklYcos2Lytj30Do7acSJIkqWQM3wXrnthJ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\n9QB1dakpXzDdc6o9jgdN5Jh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      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cumulative_pct_landing = leads.landing_page_id.value_counts(normalize=True).cumsum() *100\n",
    "cumulative_pct_landing.reset_index(drop=True).plot.line(figsize=(12,6));\n",
    "plt.xlabel('Number of Landing Pages')\n",
    "plt.ylabel('% of total leads')\n",
    "plt.xlim((-5,500))\n",
    "plt.ylim((0,102));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## closed deals\n",
    "\n",
    "Success rate of origins = number of closed / number of leads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T12:44:55.351464Z",
     "start_time": "2019-04-24T12:44:55.177927Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wWkTcRDF06Q0VYnxuRKwA/p4/DX/6KPDmiLgB2HZE+weA68uYj8/Me0fZ72eA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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "origin_success_rate = closed.merge(leads, how='left', on='mql_id')\\\n",
    "                            .origin.value_counts() / leads.origin.value_counts()\n",
    "origin_success_rate.sort_values(ascending=True).plot.barh(figsize=(12,4), cmap='Accent')\n",
    "plt.xlabel('Close Rate');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the best landing pages by their success (close) rate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T12:44:56.957204Z",
     "start_time": "2019-04-24T12:44:56.943204Z"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "closed_landings = closed.merge(leads, how='left', on='mql_id')\\\n",
    "                        .landing_page_id.value_counts()\n",
    "lead_landings = leads.landing_page_id.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T12:44:57.656297Z",
     "start_time": "2019-04-24T12:44:57.643334Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_closed</th>\n",
       "      <th>rate_closed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30077c17f2ec5010a82e37ad8925b95f</th>\n",
       "      <td>10</td>\n",
       "      <td>0.208333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40dec9f3d5259a3d2dbcdab2114fae47</th>\n",
       "      <td>67</td>\n",
       "      <td>0.203030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22c29808c4f815213303f8933030604c</th>\n",
       "      <td>174</td>\n",
       "      <td>0.197055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b76ef37428e6799c421989521c0e5077</th>\n",
       "      <td>171</td>\n",
       "      <td>0.187500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7fa6214d82e911d070f51ef79381b956</th>\n",
       "      <td>11</td>\n",
       "      <td>0.161765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ce1a65abd0973638f1c887a6efcfa82d</th>\n",
       "      <td>59</td>\n",
       "      <td>0.149746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241f79c7a8fe0270f4fb79fcbbcd17ad</th>\n",
       "      <td>14</td>\n",
       "      <td>0.128440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a7982125ff7aa3b2054c6e44f9d28522</th>\n",
       "      <td>18</td>\n",
       "      <td>0.115385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88740e65d5d6b056e0cda098e1ea6313</th>\n",
       "      <td>31</td>\n",
       "      <td>0.069663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f017be4dbf86243af5c1ebed0cff36a2</th>\n",
       "      <td>21</td>\n",
       "      <td>0.067742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58326e62183c14b0c03085c33b9fdc44</th>\n",
       "      <td>27</td>\n",
       "      <td>0.054545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e492ee5eaf1697716985cc6f33f9cd9b</th>\n",
       "      <td>10</td>\n",
       "      <td>0.034364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  num_closed  rate_closed\n",
       "30077c17f2ec5010a82e37ad8925b95f          10     0.208333\n",
       "40dec9f3d5259a3d2dbcdab2114fae47          67     0.203030\n",
       "22c29808c4f815213303f8933030604c         174     0.197055\n",
       "b76ef37428e6799c421989521c0e5077         171     0.187500\n",
       "7fa6214d82e911d070f51ef79381b956          11     0.161765\n",
       "ce1a65abd0973638f1c887a6efcfa82d          59     0.149746\n",
       "241f79c7a8fe0270f4fb79fcbbcd17ad          14     0.128440\n",
       "a7982125ff7aa3b2054c6e44f9d28522          18     0.115385\n",
       "88740e65d5d6b056e0cda098e1ea6313          31     0.069663\n",
       "f017be4dbf86243af5c1ebed0cff36a2          21     0.067742\n",
       "58326e62183c14b0c03085c33b9fdc44          27     0.054545\n",
       "e492ee5eaf1697716985cc6f33f9cd9b          10     0.034364"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_landings_byrate = pd.DataFrame({'num_closed': closed_landings,\n",
    "                                    'rate_closed': closed_landings/lead_landings[closed_landings.index]})\n",
    "# choose minimum number of closed merchants at 10 arbitrarily\n",
    "top_landings_byrate.loc[top_landings_byrate.num_closed >= 10]\\\n",
    "                    .sort_values(by='rate_closed', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These could be landing pages to focus on increasing the lead volume.\n",
    "\n",
    "# Segmentation of leads\n",
    "\n",
    "**Objective:** Identify merchants most likely to close, based on lead attributes.\n",
    "\n",
    "**Method:** Use logistic regression to determine how each landing_page and origin affect closing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T12:45:00.311196Z",
     "start_time": "2019-04-24T12:45:00.288259Z"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# engineered features\n",
    "leads['month'] = leads.first_contact_date.dt.month\n",
    "leads['dayofweek'] = leads.first_contact_date.dt.dayofweek # Monday=0, Sunday=6\n",
    "\n",
    "# merge with closed dataset to get merchants who converted\n",
    "leads = leads.merge(closed[[\"seller_id\", \"mql_id\"]], how=\"left\", on=\"mql_id\")\n",
    "\n",
    "# engineer label and features\n",
    "top_landings_byvol = lead_landings.head(50).index # top 10% of landing pages\n",
    "leads['closed'] = np.where(leads.seller_id.isna(), 0, 1)\n",
    "leads = leads.drop(columns=['mql_id', 'first_contact_date', 'seller_id'])\n",
    "leads.landing_page_id = leads.landing_page_id.where(leads.landing_page_id.isin(top_landings_byvol), \n",
    "                                                    'others')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T13:11:37.690655Z",
     "start_time": "2019-04-24T13:11:37.670708Z"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "X = pd.get_dummies(leads.drop(columns=['closed']),\n",
    "                   columns=['landing_page_id', 'origin', 'dayofweek', 'month'],\n",
    "                   drop_first=True)\n",
    "y = leads['closed']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check for collinearity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T13:11:39.400083Z",
     "start_time": "2019-04-24T13:11:39.247494Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['landing_page_id_58326e62183c14b0c03085c33b9fdc44',\n",
       "       'landing_page_id_73f31a40697cc90a86c1273563ac230e',\n",
       "       'landing_page_id_88740e65d5d6b056e0cda098e1ea6313',\n",
       "       'landing_page_id_b76ef37428e6799c421989521c0e5077', 'origin_other',\n",
       "       'origin_social', 'origin_unknown'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns[(X.corr() > 0.3).sum() > 1] # correlated features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T13:11:40.034387Z",
     "start_time": "2019-04-24T13:11:40.028404Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = X.drop(columns=['landing_page_id_58326e62183c14b0c03085c33b9fdc44',\n",
    "                    'landing_page_id_73f31a40697cc90a86c1273563ac230e',\n",
    "                    'landing_page_id_88740e65d5d6b056e0cda098e1ea6313',\n",
    "                    'landing_page_id_b76ef37428e6799c421989521c0e5077'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T13:11:40.709583Z",
     "start_time": "2019-04-24T13:11:40.570959Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([], dtype='object')"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns[(X.corr() > 0.3).sum() > 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T13:11:43.722526Z",
     "start_time": "2019-04-24T13:11:43.715545Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    7158\n",
       "1     842\n",
       "Name: closed, dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts() # highly skewed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use upsampling strategy to correct for class imbalance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T13:11:45.500768Z",
     "start_time": "2019-04-24T13:11:45.495784Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_upsampled = int((y==0).sum()/2) # 3579"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T13:12:18.736888Z",
     "start_time": "2019-04-24T13:12:18.724921Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_ = X[y==1].sample(replace=True, n=n_upsampled, random_state=12345)\n",
    "y_ = pd.Series([1] * n_upsampled)\n",
    "\n",
    "X = X.append(X_, ignore_index=True)\n",
    "y = y.append(y_, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T13:12:24.016805Z",
     "start_time": "2019-04-24T13:12:23.904072Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = sm.Logit(y.T, sm.tools.add_constant(X, prepend=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T13:13:49.116197Z",
     "start_time": "2019-04-24T13:12:26.519081Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QC check did not pass for 3 out of 73 parameters\n",
      "Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers\n",
      "Could not trim params automatically due to failed QC check.  Trimming using trim_mode == 'size' will still work.\n",
      "QC check did not pass for 1 out of 73 parameters\n",
      "Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers\n",
      "Could not trim params automatically due to failed QC check.  Trimming using trim_mode == 'size' will still work.\n"
     ]
    }
   ],
   "source": [
    "# grid search for best fit model using aic\n",
    "best_alpha = 0\n",
    "best_aic = 1e9\n",
    "for a in np.logspace(0, 2, num=30):\n",
    "    results = model.fit_regularized(disp=False, method='l1', alpha=a)\n",
    "    if results.aic < best_aic:\n",
    "        best_alpha = a\n",
    "        best_aic = results.aic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T13:13:59.005757Z",
     "start_time": "2019-04-24T13:13:59.001761Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13417.062793761368, 1.0)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_aic, best_alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T13:14:20.447413Z",
     "start_time": "2019-04-24T13:14:14.991004Z"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QC check did not pass for 3 out of 73 parameters\n",
      "Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers\n",
      "Could not trim params automatically due to failed QC check.  Trimming using trim_mode == 'size' will still work.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>y</td>        <th>  No. Observations:  </th>  <td> 11579</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>               <td>Logit</td>      <th>  Df Residuals:      </th>  <td> 11506</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>    72</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 24 Apr 2019</td> <th>  Pseudo R-squ.:     </th>  <td>0.1382</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>13:14:20</td>     <th>  Log-Likelihood:    </th> <td> -6635.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -7699.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th>  <td> 0.000</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                          <td></td>                            <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                                            <td>   -0.1911</td> <td>    0.113</td> <td>   -1.688</td> <td> 0.091</td> <td>   -0.413</td> <td>    0.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_0218f6be0b76aca72ab4d00ee9e8cf10</th> <td>   -0.1255</td> <td>    0.253</td> <td>   -0.496</td> <td> 0.620</td> <td>   -0.622</td> <td>    0.370</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_0b37d43dbde6452cdd94c17bf61a88e4</th> <td>   -2.9866</td> <td>    1.024</td> <td>   -2.915</td> <td> 0.004</td> <td>   -4.994</td> <td>   -0.979</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_15cf89bab2c3163b4f9d8601d108f8b2</th> <td>   -1.0487</td> <td>    0.385</td> <td>   -2.722</td> <td> 0.006</td> <td>   -1.804</td> <td>   -0.293</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_1722481ac9e5371e5099dea226b5421d</th> <td>   -0.5077</td> <td>    0.316</td> <td>   -1.608</td> <td> 0.108</td> <td>   -1.127</td> <td>    0.111</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_1ceb590cd1e00c7ee95220971f82693d</th> <td>   -0.2547</td> <td>    0.213</td> <td>   -1.197</td> <td> 0.231</td> <td>   -0.672</td> <td>    0.162</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_21a104c3279681d9856b8a48723c4fee</th> <td>   -0.0834</td> <td>    0.288</td> <td>   -0.290</td> <td> 0.772</td> <td>   -0.647</td> <td>    0.480</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_22c29808c4f815213303f8933030604c</th> <td>    0.5551</td> <td>    0.080</td> <td>    6.918</td> <td> 0.000</td> <td>    0.398</td> <td>    0.712</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_241f79c7a8fe0270f4fb79fcbbcd17ad</th> <td>    0.0145</td> <td>    0.179</td> <td>    0.081</td> <td> 0.935</td> <td>   -0.335</td> <td>    0.364</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_27cd3540bace1f4a4b2bc6d319b0553c</th> <td>   -0.5971</td> <td>    0.358</td> <td>   -1.669</td> <td> 0.095</td> <td>   -1.298</td> <td>    0.104</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_30077c17f2ec5010a82e37ad8925b95f</th> <td>    0.6803</td> <td>    0.231</td> <td>    2.945</td> <td> 0.003</td> <td>    0.227</td> <td>    1.133</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_30534107aa0013db92899c96a1017d0b</th> <td>   -2.6596</td> <td>    1.017</td> <td>   -2.616</td> <td> 0.009</td> <td>   -4.653</td> <td>   -0.667</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_330b9d44078a1ae5a221d655dcb77afa</th> <td>   -1.3093</td> <td>    0.342</td> <td>   -3.832</td> <td> 0.000</td> <td>   -1.979</td> <td>   -0.640</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_35c9b150ab36fe584c1f24fd458c453a</th> <td>   -0.2189</td> <td>    0.233</td> <td>   -0.938</td> <td> 0.348</td> <td>   -0.676</td> <td>    0.238</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_36120633c8737035fbabf079082f4ec1</th> <td>   -1.0480</td> <td>    0.280</td> <td>   -3.744</td> <td> 0.000</td> <td>   -1.597</td> <td>   -0.499</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_3cd2a8301d5395f21cdd3094c9eaa2f4</th> <td>   -0.4668</td> <td>    0.556</td> <td>   -0.840</td> <td> 0.401</td> <td>   -1.556</td> <td>    0.622</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_40dec9f3d5259a3d2dbcdab2114fae47</th> <td>    0.3621</td> <td>    0.108</td> <td>    3.361</td> <td> 0.001</td> <td>    0.151</td> <td>    0.573</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_4aab772aeb9833c170d8ea5413e02eac</th> <td>   -0.0661</td> <td>    0.328</td> <td>   -0.202</td> <td> 0.840</td> <td>   -0.708</td> <td>    0.576</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_4e82dd1f6d00626bda0723eef0a269a6</th> <td>   -1.3629</td> <td>    0.499</td> <td>   -2.729</td> <td> 0.006</td> <td>   -2.342</td> <td>   -0.384</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_5dc848e7dd9418dffe183215b8ff0bc1</th> <td>   -0.4083</td> <td>    0.283</td> <td>   -1.445</td> <td> 0.148</td> <td>   -0.962</td> <td>    0.145</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_649b7b8b46b254497e92038d88f6ced8</th> <td>   -0.2016</td> <td>    0.266</td> <td>   -0.759</td> <td> 0.448</td> <td>   -0.723</td> <td>    0.319</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_65d9f9d71e562365e8b44037c2888d98</th> <td>   -0.9430</td> <td>    0.246</td> <td>   -3.834</td> <td> 0.000</td> <td>   -1.425</td> <td>   -0.461</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_73fec6021437e723ff5adae8dff89e23</th> <td>   -0.4544</td> <td>    0.443</td> <td>   -1.025</td> <td> 0.305</td> <td>   -1.323</td> <td>    0.415</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_7fa6214d82e911d070f51ef79381b956</th> <td>    0.3702</td> <td>    0.205</td> <td>    1.806</td> <td> 0.071</td> <td>   -0.032</td> <td>    0.772</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_87732658ba41d8775e8577df347a64db</th> <td> 2.689e-17</td> <td>    0.409</td> <td> 6.58e-17</td> <td> 1.000</td> <td>   -0.801</td> <td>    0.801</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_909e333f8f23574f93115bd3d402e202</th> <td>   -2.6687</td> <td>    1.031</td> <td>   -2.587</td> <td> 0.010</td> <td>   -4.690</td> <td>   -0.647</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_9fa37f5177115156b1814ffc237402a6</th> <td>   -0.7131</td> <td>    0.325</td> <td>   -2.193</td> <td> 0.028</td> <td>   -1.350</td> <td>   -0.076</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_a0fbc0263cb98bdb0d72b9f9f283a172</th> <td>    0.1555</td> <td>    0.279</td> <td>    0.558</td> <td> 0.577</td> <td>   -0.391</td> <td>    0.702</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_a56671a54260a44923d32c2f08fad39c</th> <td>   -1.6866</td> <td>    1.048</td> <td>   -1.609</td> <td> 0.108</td> <td>   -3.742</td> <td>    0.368</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_a7982125ff7aa3b2054c6e44f9d28522</th> <td>    0.0451</td> <td>    0.154</td> <td>    0.293</td> <td> 0.769</td> <td>   -0.256</td> <td>    0.347</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_b48ec5f3b04e9068441002a19df93c6c</th> <td>-2.557e-18</td> <td>    0.258</td> <td>-9.92e-18</td> <td> 1.000</td> <td>   -0.505</td> <td>    0.505</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_b6885f18d203a61176418c1fb3764815</th> <td>   -0.1850</td> <td>    0.223</td> <td>   -0.831</td> <td> 0.406</td> <td>   -0.621</td> <td>    0.251</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_bddebc4f4d44106c427bd00695840e64</th> <td>   -2.5791</td> <td>    1.027</td> <td>   -2.511</td> <td> 0.012</td> <td>   -4.592</td> <td>   -0.566</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_c555aa36478386fdf85a2d0ca5cd0ff8</th> <td>   -2.1673</td> <td>    1.023</td> <td>   -2.119</td> <td> 0.034</td> <td>   -4.172</td> <td>   -0.162</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_ce1a65abd0973638f1c887a6efcfa82d</th> <td>    0.0994</td> <td>    0.103</td> <td>    0.969</td> <td> 0.332</td> <td>   -0.102</td> <td>    0.301</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_d45d558f0daeecf3cccdffe3c59684aa</th> <td>   -2.6821</td> <td>    1.025</td> <td>   -2.617</td> <td> 0.009</td> <td>   -4.691</td> <td>   -0.673</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_d83b0d0e48c8447d1d5507a44027a955</th> <td>    0.0116</td> <td>    0.287</td> <td>    0.041</td> <td> 0.968</td> <td>   -0.550</td> <td>    0.573</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_da14b7e2eb0d1eecf65abbd05a31c87d</th> <td>   -0.5506</td> <td>    0.369</td> <td>   -1.490</td> <td> 0.136</td> <td>   -1.275</td> <td>    0.174</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_daa9619c6f8ed06d059265dca0f9257f</th> <td>   -2.6527</td> <td>    1.033</td> <td>   -2.567</td> <td> 0.010</td> <td>   -4.678</td> <td>   -0.627</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_e42a14209c69c3e9cc6b042620465f12</th> <td>   -0.6502</td> <td>    0.564</td> <td>   -1.153</td> <td> 0.249</td> <td>   -1.756</td> <td>    0.455</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_e492ee5eaf1697716985cc6f33f9cd9b</th> <td>   -1.1924</td> <td>    0.163</td> <td>   -7.331</td> <td> 0.000</td> <td>   -1.511</td> <td>   -0.874</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_ef0b482996664cc1107b9051efd3ca84</th> <td>   -1.7013</td> <td>    1.028</td> <td>   -1.655</td> <td> 0.098</td> <td>   -3.716</td> <td>    0.314</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_f017be4dbf86243af5c1ebed0cff36a2</th> <td>   -0.7371</td> <td>    0.137</td> <td>   -5.392</td> <td> 0.000</td> <td>   -1.005</td> <td>   -0.469</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_f80696b73d17fb34f5c4fde157f119d8</th> <td>   -3.6385</td> <td>    1.018</td> <td>   -3.576</td> <td> 0.000</td> <td>   -5.633</td> <td>   -1.644</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_f98c2bd96ac4a5d9476734e790c91384</th> <td>   -0.7290</td> <td>    0.338</td> <td>   -2.159</td> <td> 0.031</td> <td>   -1.391</td> <td>   -0.067</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_fbc24da54d531c6204ae2d17b1090bb1</th> <td>    0.5351</td> <td>    0.317</td> <td>    1.687</td> <td> 0.092</td> <td>   -0.087</td> <td>    1.157</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>landing_page_id_others</th>                           <td>   -0.4262</td> <td>    0.074</td> <td>   -5.740</td> <td> 0.000</td> <td>   -0.572</td> <td>   -0.281</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>origin_display</th>                                   <td>   -0.8590</td> <td>    0.246</td> <td>   -3.491</td> <td> 0.000</td> <td>   -1.341</td> <td>   -0.377</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>origin_email</th>                                     <td>   -0.9916</td> <td>    0.152</td> <td>   -6.527</td> <td> 0.000</td> <td>   -1.289</td> <td>   -0.694</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>origin_organic_search</th>                            <td>   -0.0991</td> <td>    0.085</td> <td>   -1.170</td> <td> 0.242</td> <td>   -0.265</td> <td>    0.067</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>origin_other</th>                                     <td>   -1.3303</td> <td>    0.239</td> <td>   -5.567</td> <td> 0.000</td> <td>   -1.799</td> <td>   -0.862</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>origin_other_publicities</th>                         <td>   -0.9082</td> <td>    0.311</td> <td>   -2.917</td> <td> 0.004</td> <td>   -1.519</td> <td>   -0.298</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>origin_paid_search</th>                               <td>    0.0360</td> <td>    0.094</td> <td>    0.383</td> <td> 0.702</td> <td>   -0.148</td> <td>    0.220</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>origin_referral</th>                                  <td>   -0.3128</td> <td>    0.138</td> <td>   -2.272</td> <td> 0.023</td> <td>   -0.583</td> <td>   -0.043</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>origin_social</th>                                    <td>   -0.8847</td> <td>    0.109</td> <td>   -8.080</td> <td> 0.000</td> <td>   -1.099</td> <td>   -0.670</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>origin_unknown</th>                                   <td>    0.3587</td> <td>    0.099</td> <td>    3.632</td> <td> 0.000</td> <td>    0.165</td> <td>    0.552</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dayofweek_1</th>                                      <td>    0.2608</td> <td>    0.070</td> <td>    3.729</td> <td> 0.000</td> <td>    0.124</td> <td>    0.398</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dayofweek_2</th>                                      <td>    0.0126</td> <td>    0.070</td> <td>    0.178</td> <td> 0.858</td> <td>   -0.125</td> <td>    0.151</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dayofweek_3</th>                                      <td>    0.2215</td> <td>    0.071</td> <td>    3.134</td> <td> 0.002</td> <td>    0.083</td> <td>    0.360</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dayofweek_4</th>                                      <td>    0.3456</td> <td>    0.073</td> <td>    4.763</td> <td> 0.000</td> <td>    0.203</td> <td>    0.488</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dayofweek_5</th>                                      <td>    0.0616</td> <td>    0.097</td> <td>    0.633</td> <td> 0.527</td> <td>   -0.129</td> <td>    0.253</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dayofweek_6</th>                                      <td>    0.0832</td> <td>    0.099</td> <td>    0.843</td> <td> 0.399</td> <td>   -0.110</td> <td>    0.277</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_2</th>                                          <td>    0.0885</td> <td>    0.074</td> <td>    1.203</td> <td> 0.229</td> <td>   -0.056</td> <td>    0.233</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_3</th>                                          <td>    0.1406</td> <td>    0.071</td> <td>    1.974</td> <td> 0.048</td> <td>    0.001</td> <td>    0.280</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_4</th>                                          <td>    0.1262</td> <td>    0.070</td> <td>    1.800</td> <td> 0.072</td> <td>   -0.011</td> <td>    0.264</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_5</th>                                          <td>   -0.2165</td> <td>    0.073</td> <td>   -2.973</td> <td> 0.003</td> <td>   -0.359</td> <td>   -0.074</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_6</th>                                          <td>   -0.3372</td> <td>    1.197</td> <td>   -0.282</td> <td> 0.778</td> <td>   -2.684</td> <td>    2.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_7</th>                                          <td>   -2.6005</td> <td>    0.318</td> <td>   -8.179</td> <td> 0.000</td> <td>   -3.224</td> <td>   -1.977</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_8</th>                                          <td>   -1.7792</td> <td>    0.186</td> <td>   -9.583</td> <td> 0.000</td> <td>   -2.143</td> <td>   -1.415</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_9</th>                                          <td>   -1.6236</td> <td>    0.186</td> <td>   -8.730</td> <td> 0.000</td> <td>   -1.988</td> <td>   -1.259</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_10</th>                                         <td>   -1.2526</td> <td>    0.142</td> <td>   -8.816</td> <td> 0.000</td> <td>   -1.531</td> <td>   -0.974</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_11</th>                                         <td>   -1.1211</td> <td>    0.129</td> <td>   -8.661</td> <td> 0.000</td> <td>   -1.375</td> <td>   -0.867</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_12</th>                                         <td>   -0.9804</td> <td>    0.167</td> <td>   -5.885</td> <td> 0.000</td> <td>   -1.307</td> <td>   -0.654</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                11579\n",
       "Model:                          Logit   Df Residuals:                    11506\n",
       "Method:                           MLE   Df Model:                           72\n",
       "Date:                Wed, 24 Apr 2019   Pseudo R-squ.:                  0.1382\n",
       "Time:                        13:14:20   Log-Likelihood:                -6635.5\n",
       "converged:                       True   LL-Null:                       -7699.4\n",
       "                                        LLR p-value:                     0.000\n",
       "====================================================================================================================\n",
       "                                                       coef    std err          z      P>|z|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------------------------------------\n",
       "const                                               -0.1911      0.113     -1.688      0.091      -0.413       0.031\n",
       "landing_page_id_0218f6be0b76aca72ab4d00ee9e8cf10    -0.1255      0.253     -0.496      0.620      -0.622       0.370\n",
       "landing_page_id_0b37d43dbde6452cdd94c17bf61a88e4    -2.9866      1.024     -2.915      0.004      -4.994      -0.979\n",
       "landing_page_id_15cf89bab2c3163b4f9d8601d108f8b2    -1.0487      0.385     -2.722      0.006      -1.804      -0.293\n",
       "landing_page_id_1722481ac9e5371e5099dea226b5421d    -0.5077      0.316     -1.608      0.108      -1.127       0.111\n",
       "landing_page_id_1ceb590cd1e00c7ee95220971f82693d    -0.2547      0.213     -1.197      0.231      -0.672       0.162\n",
       "landing_page_id_21a104c3279681d9856b8a48723c4fee    -0.0834      0.288     -0.290      0.772      -0.647       0.480\n",
       "landing_page_id_22c29808c4f815213303f8933030604c     0.5551      0.080      6.918      0.000       0.398       0.712\n",
       "landing_page_id_241f79c7a8fe0270f4fb79fcbbcd17ad     0.0145      0.179      0.081      0.935      -0.335       0.364\n",
       "landing_page_id_27cd3540bace1f4a4b2bc6d319b0553c    -0.5971      0.358     -1.669      0.095      -1.298       0.104\n",
       "landing_page_id_30077c17f2ec5010a82e37ad8925b95f     0.6803      0.231      2.945      0.003       0.227       1.133\n",
       "landing_page_id_30534107aa0013db92899c96a1017d0b    -2.6596      1.017     -2.616      0.009      -4.653      -0.667\n",
       "landing_page_id_330b9d44078a1ae5a221d655dcb77afa    -1.3093      0.342     -3.832      0.000      -1.979      -0.640\n",
       "landing_page_id_35c9b150ab36fe584c1f24fd458c453a    -0.2189      0.233     -0.938      0.348      -0.676       0.238\n",
       "landing_page_id_36120633c8737035fbabf079082f4ec1    -1.0480      0.280     -3.744      0.000      -1.597      -0.499\n",
       "landing_page_id_3cd2a8301d5395f21cdd3094c9eaa2f4    -0.4668      0.556     -0.840      0.401      -1.556       0.622\n",
       "landing_page_id_40dec9f3d5259a3d2dbcdab2114fae47     0.3621      0.108      3.361      0.001       0.151       0.573\n",
       "landing_page_id_4aab772aeb9833c170d8ea5413e02eac    -0.0661      0.328     -0.202      0.840      -0.708       0.576\n",
       "landing_page_id_4e82dd1f6d00626bda0723eef0a269a6    -1.3629      0.499     -2.729      0.006      -2.342      -0.384\n",
       "landing_page_id_5dc848e7dd9418dffe183215b8ff0bc1    -0.4083      0.283     -1.445      0.148      -0.962       0.145\n",
       "landing_page_id_649b7b8b46b254497e92038d88f6ced8    -0.2016      0.266     -0.759      0.448      -0.723       0.319\n",
       "landing_page_id_65d9f9d71e562365e8b44037c2888d98    -0.9430      0.246     -3.834      0.000      -1.425      -0.461\n",
       "landing_page_id_73fec6021437e723ff5adae8dff89e23    -0.4544      0.443     -1.025      0.305      -1.323       0.415\n",
       "landing_page_id_7fa6214d82e911d070f51ef79381b956     0.3702      0.205      1.806      0.071      -0.032       0.772\n",
       "landing_page_id_87732658ba41d8775e8577df347a64db  2.689e-17      0.409   6.58e-17      1.000      -0.801       0.801\n",
       "landing_page_id_909e333f8f23574f93115bd3d402e202    -2.6687      1.031     -2.587      0.010      -4.690      -0.647\n",
       "landing_page_id_9fa37f5177115156b1814ffc237402a6    -0.7131      0.325     -2.193      0.028      -1.350      -0.076\n",
       "landing_page_id_a0fbc0263cb98bdb0d72b9f9f283a172     0.1555      0.279      0.558      0.577      -0.391       0.702\n",
       "landing_page_id_a56671a54260a44923d32c2f08fad39c    -1.6866      1.048     -1.609      0.108      -3.742       0.368\n",
       "landing_page_id_a7982125ff7aa3b2054c6e44f9d28522     0.0451      0.154      0.293      0.769      -0.256       0.347\n",
       "landing_page_id_b48ec5f3b04e9068441002a19df93c6c -2.557e-18      0.258  -9.92e-18      1.000      -0.505       0.505\n",
       "landing_page_id_b6885f18d203a61176418c1fb3764815    -0.1850      0.223     -0.831      0.406      -0.621       0.251\n",
       "landing_page_id_bddebc4f4d44106c427bd00695840e64    -2.5791      1.027     -2.511      0.012      -4.592      -0.566\n",
       "landing_page_id_c555aa36478386fdf85a2d0ca5cd0ff8    -2.1673      1.023     -2.119      0.034      -4.172      -0.162\n",
       "landing_page_id_ce1a65abd0973638f1c887a6efcfa82d     0.0994      0.103      0.969      0.332      -0.102       0.301\n",
       "landing_page_id_d45d558f0daeecf3cccdffe3c59684aa    -2.6821      1.025     -2.617      0.009      -4.691      -0.673\n",
       "landing_page_id_d83b0d0e48c8447d1d5507a44027a955     0.0116      0.287      0.041      0.968      -0.550       0.573\n",
       "landing_page_id_da14b7e2eb0d1eecf65abbd05a31c87d    -0.5506      0.369     -1.490      0.136      -1.275       0.174\n",
       "landing_page_id_daa9619c6f8ed06d059265dca0f9257f    -2.6527      1.033     -2.567      0.010      -4.678      -0.627\n",
       "landing_page_id_e42a14209c69c3e9cc6b042620465f12    -0.6502      0.564     -1.153      0.249      -1.756       0.455\n",
       "landing_page_id_e492ee5eaf1697716985cc6f33f9cd9b    -1.1924      0.163     -7.331      0.000      -1.511      -0.874\n",
       "landing_page_id_ef0b482996664cc1107b9051efd3ca84    -1.7013      1.028     -1.655      0.098      -3.716       0.314\n",
       "landing_page_id_f017be4dbf86243af5c1ebed0cff36a2    -0.7371      0.137     -5.392      0.000      -1.005      -0.469\n",
       "landing_page_id_f80696b73d17fb34f5c4fde157f119d8    -3.6385      1.018     -3.576      0.000      -5.633      -1.644\n",
       "landing_page_id_f98c2bd96ac4a5d9476734e790c91384    -0.7290      0.338     -2.159      0.031      -1.391      -0.067\n",
       "landing_page_id_fbc24da54d531c6204ae2d17b1090bb1     0.5351      0.317      1.687      0.092      -0.087       1.157\n",
       "landing_page_id_others                              -0.4262      0.074     -5.740      0.000      -0.572      -0.281\n",
       "origin_display                                      -0.8590      0.246     -3.491      0.000      -1.341      -0.377\n",
       "origin_email                                        -0.9916      0.152     -6.527      0.000      -1.289      -0.694\n",
       "origin_organic_search                               -0.0991      0.085     -1.170      0.242      -0.265       0.067\n",
       "origin_other                                        -1.3303      0.239     -5.567      0.000      -1.799      -0.862\n",
       "origin_other_publicities                            -0.9082      0.311     -2.917      0.004      -1.519      -0.298\n",
       "origin_paid_search                                   0.0360      0.094      0.383      0.702      -0.148       0.220\n",
       "origin_referral                                     -0.3128      0.138     -2.272      0.023      -0.583      -0.043\n",
       "origin_social                                       -0.8847      0.109     -8.080      0.000      -1.099      -0.670\n",
       "origin_unknown                                       0.3587      0.099      3.632      0.000       0.165       0.552\n",
       "dayofweek_1                                          0.2608      0.070      3.729      0.000       0.124       0.398\n",
       "dayofweek_2                                          0.0126      0.070      0.178      0.858      -0.125       0.151\n",
       "dayofweek_3                                          0.2215      0.071      3.134      0.002       0.083       0.360\n",
       "dayofweek_4                                          0.3456      0.073      4.763      0.000       0.203       0.488\n",
       "dayofweek_5                                          0.0616      0.097      0.633      0.527      -0.129       0.253\n",
       "dayofweek_6                                          0.0832      0.099      0.843      0.399      -0.110       0.277\n",
       "month_2                                              0.0885      0.074      1.203      0.229      -0.056       0.233\n",
       "month_3                                              0.1406      0.071      1.974      0.048       0.001       0.280\n",
       "month_4                                              0.1262      0.070      1.800      0.072      -0.011       0.264\n",
       "month_5                                             -0.2165      0.073     -2.973      0.003      -0.359      -0.074\n",
       "month_6                                             -0.3372      1.197     -0.282      0.778      -2.684       2.009\n",
       "month_7                                             -2.6005      0.318     -8.179      0.000      -3.224      -1.977\n",
       "month_8                                             -1.7792      0.186     -9.583      0.000      -2.143      -1.415\n",
       "month_9                                             -1.6236      0.186     -8.730      0.000      -1.988      -1.259\n",
       "month_10                                            -1.2526      0.142     -8.816      0.000      -1.531      -0.974\n",
       "month_11                                            -1.1211      0.129     -8.661      0.000      -1.375      -0.867\n",
       "month_12                                            -0.9804      0.167     -5.885      0.000      -1.307      -0.654\n",
       "====================================================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = model.fit_regularized(disp=False, method='l1', alpha=round(best_alpha, 1))\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No feature selection done. Refit model with significant features only:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T13:19:43.980223Z",
     "start_time": "2019-04-24T13:19:42.853236Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hong Jie\\Anaconda3\\envs\\python35\\lib\\site-packages\\statsmodels\\base\\model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "# recursively trim model for insignificant features\n",
    "while (results.pvalues > 0.05).sum() > 0:\n",
    "    X = X.loc[:, (results.pvalues < 0.06)] # some buffer\n",
    "    model_trim = sm.Logit(y, sm.add_constant(X, prepend=True))\n",
    "    results = model_trim.fit(disp=0, maxiter=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T13:31:00.847348Z",
     "start_time": "2019-04-24T13:31:00.825407Z"
    },
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model AIC = 13583.33\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   No. Observations:                11579\n",
      "Model:                          Logit   Df Residuals:                    11548\n",
      "Method:                           MLE   Df Model:                           30\n",
      "Date:                Wed, 24 Apr 2019   Pseudo R-squ.:                  0.1219\n",
      "Time:                        13:31:00   Log-Likelihood:                -6760.7\n",
      "converged:                       True   LL-Null:                       -7699.4\n",
      "                                        LLR p-value:                     0.000\n",
      "====================================================================================================================\n",
      "                                                       coef    std err          z      P>|z|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------------------------------------\n",
      "const                                               -0.1899      0.046     -4.131      0.000      -0.280      -0.100\n",
      "landing_page_id_15cf89bab2c3163b4f9d8601d108f8b2    -1.1568      0.399     -2.899      0.004      -1.939      -0.375\n",
      "landing_page_id_22c29808c4f815213303f8933030604c     0.6184      0.063      9.830      0.000       0.495       0.742\n",
      "landing_page_id_30077c17f2ec5010a82e37ad8925b95f     0.7784      0.227      3.427      0.001       0.333       1.224\n",
      "landing_page_id_330b9d44078a1ae5a221d655dcb77afa    -1.2878      0.351     -3.671      0.000      -1.975      -0.600\n",
      "landing_page_id_36120633c8737035fbabf079082f4ec1    -1.0273      0.281     -3.654      0.000      -1.578      -0.476\n",
      "landing_page_id_40dec9f3d5259a3d2dbcdab2114fae47     0.4827      0.091      5.312      0.000       0.305       0.661\n",
      "landing_page_id_4e82dd1f6d00626bda0723eef0a269a6    -1.5705      0.544     -2.889      0.004      -2.636      -0.505\n",
      "landing_page_id_65d9f9d71e562365e8b44037c2888d98    -0.8717      0.242     -3.605      0.000      -1.346      -0.398\n",
      "landing_page_id_9fa37f5177115156b1814ffc237402a6    -0.6608      0.323     -2.043      0.041      -1.295      -0.027\n",
      "landing_page_id_e492ee5eaf1697716985cc6f33f9cd9b    -1.1335      0.157     -7.207      0.000      -1.442      -0.825\n",
      "landing_page_id_f017be4dbf86243af5c1ebed0cff36a2    -0.7012      0.128     -5.472      0.000      -0.952      -0.450\n",
      "landing_page_id_f98c2bd96ac4a5d9476734e790c91384    -0.7664      0.345     -2.219      0.026      -1.443      -0.089\n",
      "landing_page_id_others                              -0.3129      0.060     -5.248      0.000      -0.430      -0.196\n",
      "origin_display                                      -0.8575      0.233     -3.673      0.000      -1.315      -0.400\n",
      "origin_email                                        -1.2287      0.131     -9.412      0.000      -1.485      -0.973\n",
      "origin_other                                        -1.3857      0.228     -6.080      0.000      -1.832      -0.939\n",
      "origin_other_publicities                            -1.0290      0.302     -3.402      0.001      -1.622      -0.436\n",
      "origin_referral                                     -0.3085      0.117     -2.630      0.009      -0.538      -0.079\n",
      "origin_social                                       -0.8258      0.069    -11.967      0.000      -0.961      -0.691\n",
      "origin_unknown                                       0.4161      0.058      7.115      0.000       0.301       0.531\n",
      "dayofweek_1                                          0.2438      0.057      4.266      0.000       0.132       0.356\n",
      "dayofweek_3                                          0.2176      0.058      3.745      0.000       0.104       0.331\n",
      "dayofweek_4                                          0.3386      0.060      5.615      0.000       0.220       0.457\n",
      "month_5                                             -0.3222      0.056     -5.765      0.000      -0.432      -0.213\n",
      "month_7                                             -2.8794      0.327     -8.817      0.000      -3.520      -2.239\n",
      "month_8                                             -2.0209      0.171    -11.819      0.000      -2.356      -1.686\n",
      "month_9                                             -1.7969      0.175    -10.251      0.000      -2.140      -1.453\n",
      "month_10                                            -1.4018      0.132    -10.597      0.000      -1.661      -1.143\n",
      "month_11                                            -1.2521      0.120    -10.441      0.000      -1.487      -1.017\n",
      "month_12                                            -1.0737      0.160     -6.695      0.000      -1.388      -0.759\n",
      "====================================================================================================================\n"
     ]
    }
   ],
   "source": [
    "print(\"model AIC = %.2f\" % results.aic)\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Error Analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T13:35:17.321529Z",
     "start_time": "2019-04-24T13:35:17.310525Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n",
      "=================\n",
      "Obs |   Predicted\n",
      "        0       1\n",
      "0  5552.0  1606.0\n",
      "1  2085.0  2336.0\n"
     ]
    }
   ],
   "source": [
    "# confusion matrix\n",
    "print(\"Confusion Matrix\")\n",
    "print(\"=================\")\n",
    "print(\"Obs |   Predicted\")\n",
    "print(pd.DataFrame(results.pred_table(threshold=0.5)).to_string())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T13:30:23.092313Z",
     "start_time": "2019-04-24T13:30:23.084334Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6812332671215131"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(np.diag(results.pred_table())) / X.shape[0] # accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T13:36:08.142600Z",
     "start_time": "2019-04-24T13:36:07.910222Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(results.resid_response, bins=30);\n",
    "plt.xlabel('Residuals')\n",
    "plt.ylabel('Count');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## identify landing pages with better than origin-average rates of conversion\n",
    "\n",
    "i.e. Which landing pages work better with which channel origins?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T13:48:30.162292Z",
     "start_time": "2019-04-24T13:48:30.141311Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lead_table = leads.pivot_table(index='landing_page_id',\n",
    "                               columns='origin',\n",
    "                               values='closed',\n",
    "                               aggfunc='mean').fillna(0)\\\n",
    "                  .filter(items=top_landings_byvol, axis='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T13:48:31.200480Z",
     "start_time": "2019-04-24T13:48:31.194496Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "direct_traffic       0.112224\n",
       "display              0.050847\n",
       "email                0.030426\n",
       "organic_search       0.118031\n",
       "other                0.026667\n",
       "other_publicities    0.046154\n",
       "paid_search          0.122951\n",
       "referral             0.084507\n",
       "social               0.055556\n",
       "unknown              0.162875\n",
       "Name: origin, dtype: float64"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "origin_success_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T13:48:41.021215Z",
     "start_time": "2019-04-24T13:48:40.517562Z"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uqoAxkQcOsCO+gBTcyHsTGBSOAPHnvXj0FjNbF8/R/xh4AJ8RKC1ifBk37u4A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vBTh6ghLGhm9+DO21O4Y/ytpHvi+hl4xBU8CtsuvrfBo96l9DYR1S2mdweqzOxxoBVwpqSbgbui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VJ+reFnhN0ne4bry/pBHApnG9Nb5zzs7xuzOuU38GWNvMOkV5xwFnTE//SXpV0mKFAcFzuGb7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\nERERA+7/AbjNeSjVkwszAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "origin_leaders = (lead_table.apply(lambda x: x > origin_success_rate[x.name])\n",
    "                            .sum(axis=1) > 0)\n",
    "\n",
    "plt.figure(figsize=(10,9))\n",
    "sns.heatmap(lead_table[origin_leaders], cmap='viridis');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conversion rate if we only used the origin leaders\n",
    "\n",
    "Conversion rate threshold set at 0.2 arbitrarily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T14:27:54.084539Z",
     "start_time": "2019-04-24T14:27:54.075543Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "target_combos = lead_table[origin_leaders].unstack()\\\n",
    "                                          .where(lambda x: x >= 0.2)\\\n",
    "                                          .dropna().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T14:27:54.100467Z",
     "start_time": "2019-04-24T14:27:54.086496Z"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin</th>\n",
       "      <th>landing_page_id</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>direct_traffic</td>\n",
       "      <td>ce1a65abd0973638f1c887a6efcfa82d</td>\n",
       "      <td>0.239130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>direct_traffic</td>\n",
       "      <td>40dec9f3d5259a3d2dbcdab2114fae47</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>direct_traffic</td>\n",
       "      <td>241f79c7a8fe0270f4fb79fcbbcd17ad</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>direct_traffic</td>\n",
       "      <td>330b9d44078a1ae5a221d655dcb77afa</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>direct_traffic</td>\n",
       "      <td>30077c17f2ec5010a82e37ad8925b95f</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           origin                   landing_page_id         0\n",
       "0  direct_traffic  ce1a65abd0973638f1c887a6efcfa82d  0.239130\n",
       "1  direct_traffic  40dec9f3d5259a3d2dbcdab2114fae47  0.200000\n",
       "2  direct_traffic  241f79c7a8fe0270f4fb79fcbbcd17ad  0.333333\n",
       "3  direct_traffic  330b9d44078a1ae5a221d655dcb77afa  0.333333\n",
       "4  direct_traffic  30077c17f2ec5010a82e37ad8925b95f  0.250000"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_combos.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T14:34:00.942066Z",
     "start_time": "2019-04-24T14:34:00.926110Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "new conversion rate:  0.22123893805309736\n",
      "number of leads:  1808\n"
     ]
    }
   ],
   "source": [
    "# get all rows that match both origin and landing page pairs\n",
    "target_combo_results = leads.merge(target_combos.drop(columns=[0]).drop_duplicates())\n",
    "# conversion rate\n",
    "print(\"new conversion rate: \", target_combo_results.closed.sum() / target_combo_results.shape[0])\n",
    "print(\"number of leads: \", target_combo_results.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-24T14:34:01.452707Z",
     "start_time": "2019-04-24T14:34:01.446717Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "new conversion rate:  0.10525\n",
      "number of leads:  8000\n"
     ]
    }
   ],
   "source": [
    "# original conversion rate\n",
    "print(\"new conversion rate: \", leads.closed.sum() / leads.shape[0])\n",
    "print(\"number of leads: \", leads.shape[0])"
   ]
  }
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