Final Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import missingno as msno\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler, SMOTE\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "import scikitplot as skplt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import statsmodels.formula.api as sm\n",
    "pd.options.display.float_format = '{:,.4f}'.format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import *\n",
    "from lightgbm import LGBMClassifier\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from datetime import datetime\n",
    "import math\n",
    "\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "import numpy as np\n",
    "from collections import Counter\n",
    "import pandas as pd \n",
    "import xgboost as xgb\n",
    "from lightgbm import LGBMClassifier\n",
    "from catboost import CatBoostClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions Declaration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def feature_importance_table(X, y, cutoff=2):\n",
    "    #CatBoost\n",
    "    print('running CatBoost')\n",
    "    cat_cls = CatBoostClassifier(iterations=100,learning_rate=0.05,verbose=False)\n",
    "    cat_cls.fit(X,y)\n",
    "    \n",
    "    \n",
    "    #random forest\n",
    "    print('running RF')\n",
    "    rf_cls = RandomForestClassifier()\n",
    "    rf_cls.fit(X, y)\n",
    "\n",
    "    # LIGHT\n",
    "    print('running LIGHTGBM')\n",
    "    light_cls = LGBMClassifier()\n",
    "    light_cls.fit(X, y)\n",
    "    \n",
    "    #create numpy for return data\n",
    "   \n",
    "    light = pd.Series(light_cls.feature_importances_)\n",
    "    cat = pd.Series(cat_cls.feature_importances_)\n",
    "    rf = pd.Series(rf_cls.feature_importances_)\n",
    "    \n",
    "      \n",
    "    coaf_df = pd.DataFrame(index=X.columns.tolist()) \n",
    "    \n",
    "    coaf_df['LIGHTGBM'] = light.values\n",
    "    coaf_df['CatBoost'] = cat.values\n",
    "    coaf_df['RND_FOREST'] = rf.values\n",
    "    \n",
    "    #store the data\n",
    "    return coaf_df, light_cls, cat_cls, rf_cls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "def col_Recommendation(df,return_cols = 0):\n",
    "    '''Functions gets a DataFrame and calssifies the columns into \n",
    "        Dummies Classification, Target Encoding and Further Engineering\n",
    "        if return_cols = 1, the function return list of columns for one-hot-encoding and target encoding\n",
    "        Otherwise, it returns blank lists'''\n",
    "\n",
    "    col_nunique_dictionary = dict(df.nunique()) # get unique_values from each \n",
    "    dummies = []\n",
    "    target_encoding = []\n",
    "    drop = {}\n",
    "    \n",
    "    for key,val in col_nunique_dictionary.items():\n",
    "        if df[key].dtype == 'O':\n",
    "            \n",
    "            if val < 10:\n",
    "                dummies.append(key)\n",
    "            elif 10<=val<2000:\n",
    "                target_encoding.append(key)\n",
    "\n",
    "            elif val >= 2000:\n",
    "                print(\"Please consider engineering column: {}\\n It Has {} unique values\".format(key,val))\n",
    "        \n",
    "        else:\n",
    "            if 2< val < 10:\n",
    "                dummies.append(key)\n",
    "            \n",
    "            elif 10 <= val < 2000:\n",
    "                target_encoding.append(key)\n",
    "            \n",
    "            elif val > 2000:\n",
    "                print(\"Please consider engineering column: {}\\n It Has {} unique values\".format(key,val))\n",
    "    \n",
    "    if drop != {}:\n",
    "        print(\"Please consider engineering columns: {}\\nThese columns have more than 200K unique values\".format(drop.keys()))\n",
    "    \n",
    "    if return_cols:\n",
    "        return dummies, target_encoding\n",
    "\n",
    "    else:\n",
    "        return [],[]\n",
    "      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def target_encoder(df,criterias,target='install'):\n",
    "    benchmark = len(df[df[target] ==1])/len(df)\n",
    "    for criteria in criterias:\n",
    "        classifier = pd.DataFrame(df[criteria][df[target] == 1]\\\n",
    "                        .value_counts(dropna=False)/df[criteria].value_counts(dropna=False))\\\n",
    "                        .fillna(0)\n",
    "        classifier /= benchmark\n",
    "        df = df.join(classifier,on=criteria,rsuffix='_mean').drop(criteria,axis=1)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create a dictionary of hour time frame in order to divide the day shifts \n",
    "def time_frame_dict(time_frame_hr):\n",
    "    hr_dict = {}\n",
    "    for i in range(0,24,time_frame_hr):\n",
    "        hr_dict[range(i,i+time_frame_hr)] = '{:2d} - {:2d}'.format(i,i+time_frame_hr)\n",
    "    \n",
    "    return hr_dict\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def hourgroup(a,dic):\n",
    "    \"\"\"divides the day to 3 shifts of 8 hours\"\"\"\n",
    "    \n",
    "    for key, value in dic.items():\n",
    "        if a in key:\n",
    "            \n",
    "            return value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def convert_to_local_timeframe(col,time_frame_hr = 1):\n",
    "    hr_dict = time_frame_dict(time_frame_hr)\n",
    "    \n",
    "    temp = pd.DataFrame()\n",
    "    temp['day'] = col.apply(lambda x: x.weekday())\n",
    "    temp['local_hour'] = col.apply(lambda x: x.hour)\n",
    "    \n",
    "    print(temp.local_hour.nunique())\n",
    "    temp['hour_range'] = temp.local_hour.apply(hourgroup, args = (hr_dict,))\n",
    "    \n",
    "    return temp.hour_range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def zscore_outliers(df,col, cutoff=3.0, return_thresholds=False):\n",
    "    \"\"\"\n",
    "    Return a boolean mask of outliers for a series\n",
    "    using standard deviation, works column-wise.\n",
    "    param nstd:\n",
    "        Set number of standard deviations from the mean\n",
    "        to consider an outlier\n",
    "    :type nstd: ``float``\n",
    "    param return_thresholds:\n",
    "        True returns the lower and upper bounds, good for plotting.\n",
    "        False returns the masked array \n",
    "    :type return_thresholds: ``bool``\n",
    "    \"\"\"\n",
    "\n",
    "    if df[col].mean():\n",
    "        dmean = df[col].mean()\n",
    "        dsd = df[col].std()\n",
    "        rng = dsd * cutoff\n",
    "        lower = dmean - rng\n",
    "        upper = dmean + rng\n",
    "        if return_thresholds:\n",
    "            return lower, upper\n",
    "        else:\n",
    "            return [True if z < lower or z > upper else False for z in df[col]]\n",
    "    \n",
    "    else:\n",
    "        pass\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def missing_table(df):\n",
    "    missing_table = pd.DataFrame(df.isna().sum()).rename({0:'No_of_Vals'},axis=1)\n",
    "    missing_table = missing_table[missing_table.iloc[:,0]>0]\n",
    "    missing_table['%_of_Total'] = 100*missing_table.No_of_Vals/len(df)\n",
    "    missing_table = missing_table.sort_values(by='%_of_Total',ascending= False).round(2)\n",
    "    \n",
    "    return missing_table "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def osv_normalizer(x):\n",
    "    \"\"\"Reformat Operation System Version across all ssps\"\"\"\n",
    "\n",
    "    try:\n",
    "        if np.isnan(x):\n",
    "            return x\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "    if len(x) > 12 or len(x) < 1:\n",
    "        return \"OTHER\"\n",
    "\n",
    "    elif 1< len(x) < 3:\n",
    "        return str(x)\n",
    "\n",
    "    norm = x\n",
    "    Flag = True\n",
    "    while Flag:\n",
    "        if (norm[-1] == '0') and (not norm[-2].isnumeric()):\n",
    "            norm = norm[:-2]\n",
    "        else:\n",
    "            Flag = False\n",
    "    return str(norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.set_option('mode.chained_assignment', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Files Input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "os.chdir(r'C:\\Users\\Tal\\Desktop\\Final Project\\TABLES')\n",
    "df = pd.read_csv('installs_imprs_db.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rating = pd.read_csv('AppRating.csv').set_index('plBundle') # rating for each app in which the ad is shown\n",
    "category = pd.read_csv('AppCategory.csv').set_index('plBundle') # category for each app in which the ad is shown\n",
    "crid = pd.read_csv('CRID Data.csv').set_index('crid') # information about each creative (video showed) \n",
    "timezone = pd.read_csv('us_tz.csv') # conversion dictionary from through state time difference from utc time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "timezone = timezone[['abbr','utc_diff']]\n",
    "timezone['utc_diff'] = pd.to_timedelta(abs(timezone.utc_diff),unit='h')\n",
    "timezone = pd.DataFrame(timezone.set_index('abbr'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of master_df data before enrichments (544213, 31)\n"
     ]
    }
   ],
   "source": [
    "print('Size of master_df data before enrichments', df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's add all the enrichments to master df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>time</th>\n",
       "      <th>bidtime</th>\n",
       "      <th>bidid</th>\n",
       "      <th>crid</th>\n",
       "      <th>deviceid</th>\n",
       "      <th>devlang</th>\n",
       "      <th>sspid</th>\n",
       "      <th>bundle</th>\n",
       "      <th>contype</th>\n",
       "      <th>...</th>\n",
       "      <th>uascreenpixelswidth</th>\n",
       "      <th>uascreenpixelsheight</th>\n",
       "      <th>uascreeninchesdiagonal</th>\n",
       "      <th>ppi</th>\n",
       "      <th>ssps</th>\n",
       "      <th>apps</th>\n",
       "      <th>agebucket</th>\n",
       "      <th>gender</th>\n",
       "      <th>bundletotalfc</th>\n",
       "      <th>install</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>1548845128734</td>\n",
       "      <td>1548845084493</td>\n",
       "      <td>674a6c69-532e-45ac-8f07-81d60a9a06eb</td>\n",
       "      <td>1468</td>\n",
       "      <td>df23d29a-3750-4789-8b2c-4cca6a36772e</td>\n",
       "      <td>en</td>\n",
       "      <td>chartboost</td>\n",
       "      <td>com.mindstormstudios.coinparty.google</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>1,440.0000</td>\n",
       "      <td>3,120.0000</td>\n",
       "      <td>6.4000</td>\n",
       "      <td>536.9200</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>1548845135161</td>\n",
       "      <td>1548844905459</td>\n",
       "      <td>232a81e4-60cb-4e9b-a862-afa2a4a797a3</td>\n",
       "      <td>1169</td>\n",
       "      <td>0bd1dc48-3b67-43c5-bc1f-106172d74790</td>\n",
       "      <td>en</td>\n",
       "      <td>unity_us</td>\n",
       "      <td>com.vegasslotspocket.heartofdiamond</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>720.0000</td>\n",
       "      <td>1,280.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>293.7200</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>1548845213873</td>\n",
       "      <td>1548845209785</td>\n",
       "      <td>6d9feae9-1d70-4429-97ec-f6ae6aad14a1</td>\n",
       "      <td>1194</td>\n",
       "      <td>959e3d81-edde-4b01-a168-1b7a6f336eed</td>\n",
       "      <td>en</td>\n",
       "      <td>chartboost</td>\n",
       "      <td>com.zealgames.cashmania</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>720.0000</td>\n",
       "      <td>1,280.0000</td>\n",
       "      <td>5.5000</td>\n",
       "      <td>267.0200</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>12.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13</td>\n",
       "      <td>1548845247193</td>\n",
       "      <td>1548845211500</td>\n",
       "      <td>8f893eea-c785-48ef-98cb-67adf6c7395f</td>\n",
       "      <td>1193</td>\n",
       "      <td>9852f122-18cb-4f26-b445-fbdf961eeff2</td>\n",
       "      <td>en</td>\n",
       "      <td>adcolony</td>\n",
       "      <td>com.gsn.android.casino</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>11.0000</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>25 – 34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>1548845332743</td>\n",
       "      <td>1548845308448</td>\n",
       "      <td>b5f53834-9373-4a15-9cbd-893522584329</td>\n",
       "      <td>1469</td>\n",
       "      <td>e84ab04d-179c-4c5c-853a-b41a1d9f2dab</td>\n",
       "      <td>en</td>\n",
       "      <td>adcolony</td>\n",
       "      <td>com.fortmasongames.confetti</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>1,440.0000</td>\n",
       "      <td>2,560.0000</td>\n",
       "      <td>5.1000</td>\n",
       "      <td>575.9200</td>\n",
       "      <td>11.0000</td>\n",
       "      <td>53.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0           time        bidtime  \\\n",
       "0           4  1548845128734  1548845084493   \n",
       "1           6  1548845135161  1548844905459   \n",
       "2          11  1548845213873  1548845209785   \n",
       "3          13  1548845247193  1548845211500   \n",
       "4          21  1548845332743  1548845308448   \n",
       "\n",
       "                                  bidid  crid  \\\n",
       "0  674a6c69-532e-45ac-8f07-81d60a9a06eb  1468   \n",
       "1  232a81e4-60cb-4e9b-a862-afa2a4a797a3  1169   \n",
       "2  6d9feae9-1d70-4429-97ec-f6ae6aad14a1  1194   \n",
       "3  8f893eea-c785-48ef-98cb-67adf6c7395f  1193   \n",
       "4  b5f53834-9373-4a15-9cbd-893522584329  1469   \n",
       "\n",
       "                               deviceid devlang       sspid  \\\n",
       "0  df23d29a-3750-4789-8b2c-4cca6a36772e      en  chartboost   \n",
       "1  0bd1dc48-3b67-43c5-bc1f-106172d74790      en    unity_us   \n",
       "2  959e3d81-edde-4b01-a168-1b7a6f336eed      en  chartboost   \n",
       "3  9852f122-18cb-4f26-b445-fbdf961eeff2      en    adcolony   \n",
       "4  e84ab04d-179c-4c5c-853a-b41a1d9f2dab      en    adcolony   \n",
       "\n",
       "                                  bundle  contype  ... uascreenpixelswidth  \\\n",
       "0  com.mindstormstudios.coinparty.google   2.0000  ...          1,440.0000   \n",
       "1    com.vegasslotspocket.heartofdiamond   2.0000  ...            720.0000   \n",
       "2                com.zealgames.cashmania   2.0000  ...            720.0000   \n",
       "3                 com.gsn.android.casino   2.0000  ...                 nan   \n",
       "4            com.fortmasongames.confetti   2.0000  ...          1,440.0000   \n",
       "\n",
       "  uascreenpixelsheight uascreeninchesdiagonal      ppi    ssps    apps  \\\n",
       "0           3,120.0000                 6.4000 536.9200  4.0000  5.0000   \n",
       "1           1,280.0000                 5.0000 293.7200  6.0000  8.0000   \n",
       "2           1,280.0000                 5.5000 267.0200 10.0000 12.0000   \n",
       "3                  nan                    nan      nan 11.0000 25.0000   \n",
       "4           2,560.0000                 5.1000 575.9200 11.0000 53.0000   \n",
       "\n",
       "  agebucket  gender  bundletotalfc  install  \n",
       "0       NaN     NaN         2.0000   0.0000  \n",
       "1       NaN     NaN         3.0000   0.0000  \n",
       "2       NaN     NaN         3.0000   0.0000  \n",
       "3   25 – 34     NaN         1.0000   0.0000  \n",
       "4       NaN  Female         2.0000   0.0000  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.join(timezone,on='state')\n",
    "df = df.join(rating,on='bundle',how='left')\n",
    "df = df.join(category,on='bundle',how='left')\n",
    "df = df.join(crid,on='crid',how='left')\n",
    "df = df[~df.index.duplicated()]\n",
    "df = df.drop(['Unnamed: 0'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "544213\n"
     ]
    }
   ],
   "source": [
    "# drop the id of the bid since it is not our index and it doesn't contain\n",
    "# any additional information about installing rather than the id of the bid\n",
    "print(df.bidid.nunique())\n",
    "df = df.drop(['bidid','bidtime'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of master_df data after enrichments (544213, 34)\n"
     ]
    }
   ],
   "source": [
    "print('Size of master_df data after enrichments', df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>crid</th>\n",
       "      <th>deviceid</th>\n",
       "      <th>devlang</th>\n",
       "      <th>sspid</th>\n",
       "      <th>bundle</th>\n",
       "      <th>contype</th>\n",
       "      <th>make</th>\n",
       "      <th>model</th>\n",
       "      <th>osv</th>\n",
       "      <th>...</th>\n",
       "      <th>agebucket</th>\n",
       "      <th>gender</th>\n",
       "      <th>bundletotalfc</th>\n",
       "      <th>install</th>\n",
       "      <th>utc_diff</th>\n",
       "      <th>bundleAvgUserRating</th>\n",
       "      <th>bundleCategory</th>\n",
       "      <th>Video Name</th>\n",
       "      <th>is_landscape</th>\n",
       "      <th>rewarded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1548845128734</td>\n",
       "      <td>1468</td>\n",
       "      <td>df23d29a-3750-4789-8b2c-4cca6a36772e</td>\n",
       "      <td>en</td>\n",
       "      <td>chartboost</td>\n",
       "      <td>com.mindstormstudios.coinparty.google</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>LG</td>\n",
       "      <td>LM-V405</td>\n",
       "      <td>8.1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>06:00:00</td>\n",
       "      <td>4.4000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yahtzee</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1548845135161</td>\n",
       "      <td>1169</td>\n",
       "      <td>0bd1dc48-3b67-43c5-bc1f-106172d74790</td>\n",
       "      <td>en</td>\n",
       "      <td>unity_us</td>\n",
       "      <td>com.vegasslotspocket.heartofdiamond</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>SM-S327VL</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>06:00:00</td>\n",
       "      <td>4.7000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Big Money Reels</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1548845213873</td>\n",
       "      <td>1194</td>\n",
       "      <td>959e3d81-edde-4b01-a168-1b7a6f336eed</td>\n",
       "      <td>en</td>\n",
       "      <td>chartboost</td>\n",
       "      <td>com.zealgames.cashmania</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>SM-J737P</td>\n",
       "      <td>8.0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>06:00:00</td>\n",
       "      <td>4.7000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Big Money Reels</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1548845247193</td>\n",
       "      <td>1193</td>\n",
       "      <td>9852f122-18cb-4f26-b445-fbdf961eeff2</td>\n",
       "      <td>en</td>\n",
       "      <td>adcolony</td>\n",
       "      <td>com.gsn.android.casino</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25 – 34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>06:00:00</td>\n",
       "      <td>4.3000</td>\n",
       "      <td>games,games/casino</td>\n",
       "      <td>Big Money Reels</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1548845332743</td>\n",
       "      <td>1469</td>\n",
       "      <td>e84ab04d-179c-4c5c-853a-b41a1d9f2dab</td>\n",
       "      <td>en</td>\n",
       "      <td>adcolony</td>\n",
       "      <td>com.fortmasongames.confetti</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>SM-G930A</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>06:00:00</td>\n",
       "      <td>4.6000</td>\n",
       "      <td>games,games/casino</td>\n",
       "      <td>Yahtzee</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            time  crid                              deviceid devlang  \\\n",
       "0  1548845128734  1468  df23d29a-3750-4789-8b2c-4cca6a36772e      en   \n",
       "1  1548845135161  1169  0bd1dc48-3b67-43c5-bc1f-106172d74790      en   \n",
       "2  1548845213873  1194  959e3d81-edde-4b01-a168-1b7a6f336eed      en   \n",
       "3  1548845247193  1193  9852f122-18cb-4f26-b445-fbdf961eeff2      en   \n",
       "4  1548845332743  1469  e84ab04d-179c-4c5c-853a-b41a1d9f2dab      en   \n",
       "\n",
       "        sspid                                 bundle  contype     make  \\\n",
       "0  chartboost  com.mindstormstudios.coinparty.google   2.0000       LG   \n",
       "1    unity_us    com.vegasslotspocket.heartofdiamond   2.0000  Samsung   \n",
       "2  chartboost                com.zealgames.cashmania   2.0000  Samsung   \n",
       "3    adcolony                 com.gsn.android.casino   2.0000      NaN   \n",
       "4    adcolony            com.fortmasongames.confetti   2.0000  Samsung   \n",
       "\n",
       "       model    osv  ... agebucket  gender bundletotalfc install  utc_diff  \\\n",
       "0    LM-V405  8.1.0  ...       NaN     NaN        2.0000  0.0000  06:00:00   \n",
       "1  SM-S327VL    6.0  ...       NaN     NaN        3.0000  0.0000  06:00:00   \n",
       "2   SM-J737P  8.0.0  ...       NaN     NaN        3.0000  0.0000  06:00:00   \n",
       "3        NaN  8.1.0  ...   25 – 34     NaN        1.0000  0.0000  06:00:00   \n",
       "4   SM-G930A    8.0  ...       NaN  Female        2.0000  0.0000  06:00:00   \n",
       "\n",
       "   bundleAvgUserRating      bundleCategory       Video Name  is_landscape  \\\n",
       "0               4.4000                 NaN          Yahtzee             1   \n",
       "1               4.7000                 NaN  Big Money Reels             1   \n",
       "2               4.7000                 NaN  Big Money Reels             1   \n",
       "3               4.3000  games,games/casino  Big Money Reels             1   \n",
       "4               4.6000  games,games/casino          Yahtzee             1   \n",
       "\n",
       "   rewarded  \n",
       "0         1  \n",
       "1         0  \n",
       "2         1  \n",
       "3         1  \n",
       "4         1  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First let's convert from unix to full date-time with seconds, so we can actually read it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.time = pd.to_datetime(df.time/1000,unit='s')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "object exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're looking for a less than 0.6% crowd and their chance for installs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.005268157872009673"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.install.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "installs = df.install.value_counts()/len(df)\n",
    "labels = [str(int(key))+': % '+str(round(100*installs[key],2)) for key in dict(installs).keys()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "object             15\n",
       "float64            14\n",
       "int64               3\n",
       "datetime64[ns]      1\n",
       "timedelta64[ns]     1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Graph 1: Installs Vs No Installs')"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wObBTnqV9sYN0I63JFkxSKm+PClZa673AhUmpPDV2kG6kki2QpFR+D/5rsipYabUdgHPD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\nFRFpo/8HmDV0AIjPSJEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.pie(installs,labels=labels,radius=1.2,shadow=False)\n",
    "plt.title('Graph 1: Installs Vs No Installs',fontsize=15,color='b')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "unique_vals = df.nunique().rename('Unique_Values',axis=1,inplace=True)\n",
    "df_dtypes = df.dtypes.rename('Column Data Type',axis=1,inplace=True)\n",
    "df_dtype_summary = pd.DataFrame(unique_vals).join(df_dtypes).sort_values(by='Unique_Values',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.osv = df.osv.apply(osv_normalizer)\n",
    "# normalizes the operation system Android version strings that might come in different writing versions\n",
    "#for example: 8.1.0 is actually 8.1 , and 9.0 is actually 9, 8.1.1 stays 8.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.crid = df.crid.astype('O') #Creative ID should be considered a number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Graph 2: Population Distribution (%) as func of no. of unique ssps visited by user, \\n')"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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484BUkGA71r0N5qBXlDhrvke11iu11rswLSnn0VonW/vjWMzJ6TarLqWVBNRTSjkeTEv6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\nEZFyUgMlIiIiUk7/HxjKPyqt6FvQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "zeros = pd.DataFrame(100*df.ssps[df.install == 0].value_counts()/len(df[df.install == 0])).sort_index()\n",
    "ones = 100*df.ssps[df.install == 1].value_counts()/len(df[df.install == 1])\n",
    "\n",
    "zeros.join(ones,rsuffix='ones').plot(figsize=(9,6))\n",
    "plt.legend(['Non Installers','Installers'])\n",
    "plt.xlim(1,19)\n",
    "plt.xticks(range(1,19))\n",
    "plt.title('Graph 2: Population Distribution (%) as func of no. of unique ssps visited by user, \\n',fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Graph 3: Log of No. Of Apps Per User , installers vs. non installers\\n')"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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twI6T27LsZyDa29sZjnNrtGj28oN10OzlB+tgpJV/KO9I0cO8Tc0nKaUlQ0hb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\nDDwkSVI2/x/2hPWuSeoYrQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,6))\n",
    "for key in installs.keys():\n",
    "    df.apps[df.install == key].hist()\n",
    "    \n",
    "plt.legend(['Non Installers','Installers'])\n",
    "plt.yscale('log')\n",
    "plt.title('Graph 3: Log of No. Of Apps Per User , installers vs. non installers\\n',fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Graph 4: Population Distribution (%) as func of no. of Pixels Per Inch (PPI) each by user, \\n')"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RTa37QbXvq/T9tRrrPpxXy2851nXVus/W6nxCVfBWr60z0UxCsJll8MUghM/6Q0LzjFJl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\nRK5CYII=\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "zeros = pd.DataFrame(100*df.ppi[df.install == 0].value_counts()/len(df[df.install == 0])).sort_index()\n",
    "ones = 100*df.ppi[df.install == 1].value_counts()/len(df[df.install == 1])\n",
    "\n",
    "zeros.join(ones,rsuffix='ones').plot(figsize=(9,6))\n",
    "plt.legend(['Non Installers','Installers'])\n",
    "plt.title('Graph 4: Population Distribution (%) as func of no. of Pixels Per Inch (PPI) each by user, \\n',fontsize=14)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Graph 5: Population Distribution (%) as a func of Device Language \\n')"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hSZKqMHRIkqQqDB2SJKkKQ4ckSarC0CFJkqowdEiSpCoMHZIkqQpDhyRJqsLQ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\nFAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "zeros = pd.DataFrame(100*df.devlang[df.install == 0].value_counts()/len(df[df.install == 0]))\n",
    "ones = 100*df.devlang[df.install == 1].value_counts()/len(df[df.install == 1])\n",
    "\n",
    "zeros.join(ones,rsuffix='ones').plot.bar(figsize=(9,6))\n",
    "plt.legend(['Non Installers','Installers'])\n",
    "plt.xlim(-1,10)\n",
    "plt.title('Graph 5: Population Distribution (%) as a func of Device Language \\n',fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please consider engineering column: time\n",
      " It Has 544142 unique values\n",
      "Please consider engineering column: deviceid\n",
      " It Has 227745 unique values\n"
     ]
    }
   ],
   "source": [
    "dummies, target = col_Recommendation(df)      \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature 'time' Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['local_time'] = df.time-df.utc_diff # convert from utc time and time differenct to local time\n",
    "df.drop('utc_diff',axis=1,inplace=True) ## difference in hours from utc is irrelevant now"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "since we don't have information about more than 1 month, January,\n",
    "variables such as day of month cannot be put against next month , \n",
    "So it's not a feature which is reasonable to use.\n",
    "Hence we're going to engineer 'day of the week' and 'hour time frame' variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24\n"
     ]
    }
   ],
   "source": [
    "df['local_time_bricks'] = convert_to_local_timeframe(df.local_time) # This Feature divides the day to 1hr bricks\n",
    "df['weekday'] = df.local_time.apply(lambda x: x.isoweekday()) #day of the week, where Monday is 1 and Sunday is 7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature 'deviceid' Engineering\n",
    "\n",
    "#### We're aggregating data about device ids, according to the K-th time they were shown an ad during January,\n",
    "#### And In case we see there's a point in engineering more features - we will"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['ad_no'] = df.sort_values(['deviceid','time']).groupby(['deviceid']).cumcount()+1 \n",
    "# add a value of the k-th time the user was shown an ad at the time of January"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Graph 6: Population Distribution (%) as a func of No. Of Times ad shown per user, \\n')"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Gjx7NVVddxVlnncWll14KwMqVK7npppsAmDBhAtdffz1jxoxh5cqVgzAXNlyG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\nGKYkSZJqMExJkiTVYJiSJEmq4f8DGldgNJ7XCPcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "zeros = pd.DataFrame(100*df.ad_no[df.install == 0].value_counts()/len(df[df.install == 0]))\n",
    "ones = 100*df.ad_no[df.install == 1].value_counts()/len(df[df.install == 1])\n",
    "\n",
    "zeros.join(ones,rsuffix='ones').plot.bar(figsize=(9,6))\n",
    "plt.legend(['Non Installers','Installers'])\n",
    "plt.xlim(-1,10)\n",
    "plt.title('Graph 6: Population Distribution (%) as a func of No. Of Times ad shown per user, \\n',fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Columns - Report\n",
    "Dropping above 10K unique values in the prediction after engineering "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please consider engineering column: time\n",
      " It Has 544142 unique values\n",
      "Please consider engineering column: deviceid\n",
      " It Has 227745 unique values\n",
      "Please consider engineering column: local_time\n",
      " It Has 508337 unique values\n",
      "Columns For Dummy Encoding:\n",
      "['sspid', 'contype', 'netspeed', 'uadevicetype', 'agebucket', 'gender', 'bundletotalfc', 'Video Name', 'weekday']\n",
      " \n",
      "Columns For Target Encoding:\n",
      "['crid', 'devlang', 'bundle', 'make', 'model', 'osv', 'isp', 'state', 'uabatterycapacity', 'uadeviceram', 'uareleaseyear', 'uamaxinternalstorage', 'uascreenpixelswidth', 'uascreenpixelsheight', 'uascreeninchesdiagonal', 'ppi', 'ssps', 'apps', 'bundleAvgUserRating', 'bundleCategory', 'local_time_bricks', 'ad_no'] \n"
     ]
    }
   ],
   "source": [
    "dummies_columns, target_columns = col_Recommendation(df,return_cols=1)\n",
    "print(\"Columns For Dummy Encoding:\\n{}\\n \\nColumns For Target Encoding:\\n{} \".format(dummies_columns,target_columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.drop(['time','deviceid','local_time'],axis=1) # after engineering data from the uppermentioned columns we can from the values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Encoding and handling missing in categories\n",
    "\n",
    "## The vast majority of the data is either categorical or ordinal (no. of times a user watched an ad is a good example - first time , second, etc..)\n",
    "\n",
    "## Only float - continouos variable is ppi which is pixels per inch for  the users' screen, hence again a variable of the device which we can see in Graph 4, has pattern what so over \n",
    "\n",
    "## Hence, we assume all of our data is categorcal / Ordinal and should be treated with feature_na in case of nan value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# ua features - refers to User Agent, and this is a string that is being parsed that describes the data aboutThe device information, such as ram memory, storage,width and height of the screen, battery capacity etc.\n",
      "# agebucket, gender features -  are details which the user chooses to reveal while using an app, and we can see that       most users choose not to disclose their gender/age while using commercialized apps\n",
      "# bundlecategory - is an enrichement of data, that uses the bundle, to disclose all the categories in which it \n"
     ]
    },
    {
     "data": {
      "image/png": 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rAx+UDMY+wN7AbmRDzesBkf0PApin6dnjVZmdyGbMPwRekvRxRMxABhfOlLRN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W+NyX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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "missingdata_df = df.columns[df.isnull().any()].tolist()\n",
    "msno.matrix(df[missingdata_df])\n",
    "print('# ua features - refers to User Agent, and this is a string that is being parsed that describes the data about\\\n",
    "The device information, such as ram memory, storage,width and height of the screen, battery capacity etc.')\n",
    "\n",
    "print('# agebucket, gender features -  are details which the user chooses to reveal while using an app, and we can see that\\\n",
    "       most users choose not to disclose their gender/age while using commercialized apps')\n",
    "\n",
    "print('# bundlecategory - is an enrichement of data, that uses the bundle, to disclose all the categories in which it ')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Outliers Handling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Col_Name</th>\n",
       "      <th>OutlierCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>uabatterycapacity</td>\n",
       "      <td>18474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>uadeviceram</td>\n",
       "      <td>789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>uareleaseyear</td>\n",
       "      <td>478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>uamaxinternalstorage</td>\n",
       "      <td>17965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>uascreenpixelswidth</td>\n",
       "      <td>1046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>uascreenpixelsheight</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>uascreeninchesdiagonal</td>\n",
       "      <td>4255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ppi</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ssps</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>apps</td>\n",
       "      <td>10539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>bundleAvgUserRating</td>\n",
       "      <td>3557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ad_no</td>\n",
       "      <td>8537</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Col_Name OutlierCount\n",
       "0        uabatterycapacity        18474\n",
       "1              uadeviceram          789\n",
       "2            uareleaseyear          478\n",
       "3     uamaxinternalstorage        17965\n",
       "4      uascreenpixelswidth         1046\n",
       "5     uascreenpixelsheight           33\n",
       "6   uascreeninchesdiagonal         4255\n",
       "7                      ppi           33\n",
       "8                     ssps           17\n",
       "9                     apps        10539\n",
       "10     bundleAvgUserRating         3557\n",
       "11                   ad_no         8537"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outlier_report = pd.DataFrame(columns=['Col_Name','OutlierCount'])\n",
    "\n",
    "for col in df.columns:\n",
    "    if (df[col].nunique() > 2) & (df[col].dtype != 'O'):\n",
    "        zscore_mask =  zscore_outliers(df,col, cutoff=3.0, return_thresholds=False)\n",
    "        outlier_len = len(df[col][zscore_mask])\n",
    "        if (outlier_len == 0):\n",
    "            continue   \n",
    "        outlier_report = outlier_report.append({'Col_Name':col, 'OutlierCount' : outlier_len} , ignore_index=True)\n",
    "        \n",
    "outlier_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "outliers_df = df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Device Release Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "478"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(outliers_df[outliers_df.uareleaseyear< 2014])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27e01bd5320>"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FXAFsq6rTgW3NNsBjwKXA1R37kRaUqaOK6balUdZbWFTVnqq6p3n/JLALGAPWAluaw7YA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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=df.uareleaseyear)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before removing release year outlier:  544213\n",
      "after removing release year outlier:  543735\n"
     ]
    }
   ],
   "source": [
    "#removing all records with years below 2014\n",
    "print(\"before removing release year outlier: \",len(outliers_df))\n",
    "outliers_df = outliers_df[~(outliers_df.uareleaseyear < 2014)]\n",
    "print(\"after removing release year outlier: \",len(outliers_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27e0d5494e0>"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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52l5V/7inuiRJI6TrE9y/B1wG/ASD22AvB/b3VZQkabR0DYulzXMNr1bVRuBa4If6K0uS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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=outliers_df.uareleaseyear)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27e0d594d68>"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LzL+MiB8Bj0bE/cD3aV3JRFn+z4gYoTVDuLXG2iRJ06gtFDJzL607n8/sf4nW+YUz+08A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\n//bDeDZ8M0nIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=outliers_df.apps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10539"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.apps>90].apps.value_counts().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before removing apps outlier:  543735\n",
      "after removing apps outlier:  533199\n"
     ]
    }
   ],
   "source": [
    "#removing all records with years below 2014\n",
    "print(\"before removing apps outlier: \",len(outliers_df))\n",
    "outliers_df = outliers_df[~(outliers_df.apps > 90)]\n",
    "print(\"after removing apps outlier: \",len(outliers_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27e03515e48>"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAADrCAYAAABuBv24AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4ZekYv4Pk8VtBON81+YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=outliers_df.apps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Device Battery capacity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### check device types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27e0d5b8e10>"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6RMyJiJvGe9udFhEzyr6tiIhfRsQvGsZ3aTL/lIh4YJh1fSki3rCN7b0rIp42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\nHEmSVDsmOJIkqXb+PzOme4iCePhTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,6))\n",
    "sns.boxplot(y=outliers_df.uabatterycapacity,x=outliers_df.uadevicetype)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SmartPhone     398105\n",
       "NaN             67754\n",
       "Tablet          66441\n",
       "Desktop           532\n",
       "Mobile            299\n",
       "SmallScreen        44\n",
       "Console            13\n",
       "MediaHub           11\n",
       "Name: uadevicetype, dtype: int64"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outliers_df.uadevicetype.value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# We see that Tablets are have better battery capacity, so this is not an outlier.\n",
    "# However Since the Campaign is targetted at mobiles and Tablets, all the rest of the devicetypes are clearly outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before removing devices outlier:  533199\n",
      "after removing devices outlier:  532300\n"
     ]
    }
   ],
   "source": [
    "#removing all records with devicetypes which shouldn't be there\n",
    "print(\"before removing devices outlier: \",len(outliers_df))\n",
    "outliers_df = outliers_df[(outliers_df.uadevicetype.isin(['Tablet','SmartPhone',np.nan]))]\n",
    "print(\"after removing devices outlier: \",len(outliers_df))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27e03706978>"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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t3gb8NC3/CLhR0t7AW4Dr0nEhGybdrF+cRMwynezYMi+NCvtD4L0RsVLSGcDE\nsjo9jRkk4FsR8YMetl0PnEo2e961u9i3p1Fn742I4/uIH7KWx83Ac8B1pWTUgyD7rE91H4XZrL98\nOcss8wRwoLJ5vXfnxcEr9wEeT5eVyged/B/g9LRcXr4Y+Fj6pY+kJkkHpm3Xpn1OJUso3d0KfFJS\nfdp3f+BPwFhlUyAj6WW7GnU2Ih4DHgO+Rpb8Skalc0Ia2TYingEeknRaOq4kHdnL38esR04iZkBE\nvEA2E97twH/y4mjGX09lS9hxhONZwDmS7iAbmrx0nFvJJkL6XZrX5XrSf/QRcW9aXhsRO12SIhu1\n+BGykZJXAh9KI8meClyUyu4iuwwFWaK4LHWsl+ZsuQZ4NCLuKzvus8DhkpYD70yfE7Lkd1Y67r3A\n1Er+VmblPIqv2Qgi6XvAnRFxRVnZpojYu8CwbARzEjEbIVJL41nghIjYWlbuJGJV4yRiZma5uU/E\nzMxycxIxM7PcnETMzCw3JxEzM8vNScTMzHL7/+qqg+4segieAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=outliers_df.uabatterycapacity,x=outliers_df.uadevicetype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bat_filt = outliers_df.uadevicetype == 'SmartPhone'\n",
    "mem_filt = ~outliers_df.uabatterycapacity.between(1600,4800)\n",
    "null_fil = ~outliers_df.uabatterycapacity.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before removing battery outlier:  532300\n",
      "after removing battery outlier:  527701\n"
     ]
    }
   ],
   "source": [
    "print(\"before removing battery outlier: \",len(outliers_df))\n",
    "outliers_df = outliers_df[~(mem_filt & null_fil & bat_filt)] ## removing all not null, smartphones with out of bounds batteries\n",
    "print(\"after removing battery outlier: \",len(outliers_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27e03717a20>"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yM7O60dXcWef0ViBmtu0WrR7I1XOG1jqMmnthTXaRZc8dN9U4ktpbtHogo6t4/LyjswYD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\nMCcRMzMrzEnEzMwKcxIxM7PC/j+3a+Hl47+pPAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=outliers_df.uabatterycapacity,x=outliers_df.uadevicetype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before removing battery outlier:  527701\n",
      "after removing battery outlier:  527701\n"
     ]
    }
   ],
   "source": [
    "print(\"before removing battery outlier: \",len(outliers_df))\n",
    "outliers_df.loc[bat_filt] = outliers_df.loc[bat_filt & ~outliers_df.uabatterycapacity.between(1600,4800)] \n",
    "print(\"after removing battery outlier: \",len(outliers_df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ad number "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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zc3NNfDpRe3u7JGnixIlJczQ2NvIHHPv0zPp7MPsvFuWfoXfeeSd1BACJUf5VVCuz3MWL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\nx4IVxFYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=outliers_df.ad_no,x=outliers_df.install)\n",
    "print(max(outliers_df.ad_no[outliers_df.install == 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before removing ads shown more than 10 times outlier:  527701\n",
      "after removing ads shown more than 10 times outlier:  141762\n"
     ]
    }
   ],
   "source": [
    "print(\"before removing ads shown more than 10 times outlier: \",len(outliers_df))\n",
    "outliers_df = outliers_df.loc[outliers_df.ad_no <= 10] \n",
    "print(\"after removing ads shown more than 10 times outlier: \",len(outliers_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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/OMH9823fWr9/k+3u1qdERravt/2y7Sf2c79t/339tfnftk9pdcZOR/l3KNuzJf2DpI9K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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=outliers_df.ad_no,x=outliers_df.install)\n",
    "print(max(outliers_df.ad_no[outliers_df.install == 1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SSPS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27e03806780>"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SmgwISVKTASFJajIgJElNBoQkqWkxj8UkaZZvPLWE929yfKxHd838XXvq8c92XEn3vvHU\nEs4a4v4NCOkw4HhQz/lerwfAsS/1/8lZDPfYMCCkw4BjhT3HscJGx2sQkqQmA0KS1GRASJKaDAhJ\nUpMBIUlqMiAkSU0GhCSpyYCQJDUZEJKkJgNCktRkQEiSmgwISVLTyAMiyRlJ/jLJ15Lcl+SKRpuV\nSR5Psrn/+e1R1ylJ466L0Vz3Av+qqjYlORG4O8kdVXX/nHafr6o3dFCfJIkOehBV9UhVbepPPwl8\nDVg+6jokSQfX6TWIJCuAnwa+3Fj96iT3JPl0klcdZB9rkkwlmZqenh5SpZI0fjoLiCQvBG4Cfr2q\nnpizehPw0qo6G1gH/Pmg/VTV+qqarKrJZcuWDa9gSRoznQREkqOZCYePVdUn566vqieq6qn+9Ebg\n6CSnjLhMSRprXdzFFOB64GtV9YcD2vxYvx1JzmOmzm+PrkpJUhd3MZ0P/BPg3iSb+8veC5wJUFXX\nAW8FfjXJXmA3cGlVVQe1StLYGnlAVNUXgPyANtcA14ymIklSi09SS5KaDAhJUpMBIUlqMiAkSU0G\nhCSpyYCQJDUZEJKkJgNCktRkQEiSmgwISVKTASFJasqRNAbe5ORkTU1NdV3G87Ju3Tp6vV6nNez7\n/YmJiU7r2FfD2rVruy5DLI5jExbP8XmkHJtJ7q6qyda6LkZz1SJ33HHHdV2CNJDH5+jYg5CkMXaw\nHoTXICRJTQaEJKnJgJAkNRkQkqQmA0KS1GRASJKaDAhJUpMBIUlqOqIelEsyDTzcdR1HiFOAb3Vd\nhDSAx+eh89KqWtZacUQFhA6dJFODnq6UuubxORqeYpIkNRkQkqQmA0KDrO+6AOkgPD5HwGsQkqQm\nexCSpCYDQpLUZECMuSSrknw9SS/Jexrrj03yZ/31X06yYvRVahwl2ZBkZ5ItA9YnyQf7x+ZXk5w7\n6hqPdAbEGEuyBPgQcBHwSuCyJK+c0+wdwF9X1QTwR8B/GG2VGmM3AKsOsv4i4Kz+Zw1w7QhqGisG\nxHg7D+hV1UNV9T3g48Alc9pcAny0P/0J4HVJMsIaNaaq6k7gsYM0uQT4k5rxJeCkJKeNprrxYECM\nt+XAN2fNb+sva7apqr3A48CPjKQ66eDmc/zqeTAgxlurJzD3vuf5tJG64LE5ZAbEeNsGnDFr/nRg\nx6A2SY4C/hYH7/ZLozKf41fPgwEx3u4CzkrysiTHAJcCt8xpcwtweX/6rcD/KJ+u1OJwC/CL/buZ\nfhZ4vKoe6bqoI8lRXReg7lTV3iTvAm4HlgAbquq+JL8LTFXVLcD1wH9J0mOm53BpdxVrnCS5EVgJ\nnJJkG/A+4GiAqroO2AisBnrALuDt3VR65HKoDUlSk6eYJElNBoQkqcmAkCQ1GRCSpCYDQpLUZEBI\nP4Qk/2uB272pMRBiq93vJPmN/vQNSd66kN+TDgUDQvohVNXPLXDTNzEzYq502DAgpB9Ckqf63yuT\nfC7JJ5I8kORj+0a5TfKBJPf331Hwn5L8HPBG4PeTbE7yE0n+eZK7ktyT5KYkx3f53yW1+CS1tHA/\nDbyKmfF//idwfpL7gTcDr6iqSnJSVX0nyS3AX1TVJwCSfKeqPtyfvoqZ926s6+S/QhrAHoS0cF+p\nqm1V9SywGVgBPAE8DXwkyVuYGQKi5SeTfD7JvcAvMBM00qJiQEgLt2fW9DPAUf13ZpwH3MTMdYfb\nBmx7A/Cuqvop4N8BS4dYp7QgnmKSDqEkLwSOr6qNSb7EzEByAE8CJ85qeiLwSJKjmelBbB9tpdIP\nZkBIh9aJwKeSLGXmhTbv7i//OPDhJL/GzLDpvwV8GXgYuJf9w0NaFBzNVZLU5DUISVKTASFJajIg\nJElNBoQkqcmAkCQ1GRCSpCYDQpLU9P8B9f0w10+z4Q8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=outliers_df.ssps,x=outliers_df.install)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bundle Rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27e03840588>"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AGGrx+k9J+ilwC8mcQqcA3RHRGRGvtiJAy0e1Wh1Q7urqKigSMyvaUC2C+cAW4A7gS+lC\nMl4h7ABw4403DijfcMMNxQRiZoUbKhEcCXyKZNGYmqT7gMmSMg87tdFpx44dTctmVh5NE0FE7ImI\n70TEXJKpqL8NPApskvSVVgRo+ZgyZUrTspmVR9YpJoiIXRGxLCIuIUkK/5BfWJa3+kdDN910UzGB\nmFnhso4aOkLSPZK+k340k/14K9lGn46OjgHl0047raBIzKxoWVsE95K0AN6Rlv8V+FgeAVlreNSQ\nmfXJmggOj4ivA3sB0iUr9zT/io1mHjVkZn2yJoJfSppOOnRU0hnA1tyistx51JCZ9cn6nP/jJG8U\nHyfph8AM4NLcorLcTZkyZcAvf48aMiuvrHMNPQH8J5LlKv8bcFJEPJVnYJYvjxoysz5NWwSSLh7k\n0L+XRER8I4eYrAU6OjqYPHkyO3fuZPLkyR41ZFZiQz0auqDJsQAGTQTpUpYPAxPT6yyLiBvq6kwE\nlgKnAT3A+yNiw9Bh20iIiAH/mlk5NU0EEXHFWzj3bmB2ROyQNAH4gaTvRMRj/epcCbwaERVJlwGf\nJlkBzXJWrVbZtWsXALt27aKrq8utArOSGurR0MebHY+IW5scC6CvN3JCutX/6XkRcGO6vwz4vCSF\n/0TNXaPhoytWrCgmGDMr1FCdxVPTrQP470Bbul0FnDjUySWNk7QWeBlYHRGP11VpA16Efe8mbAWm\nNzjPfElVSdXu7u6hLmsZePiomfUZatK5myLiJuBw4NSIuCYiriF5pn/0UCdPJ617d1r3dEnvrKui\nRl9rcJ67IqIjIjpmzJgx1GUtA086Z2Z9sr5Qdgzwer/y60B71otExC+ANcCcukMbSeYtIp3a+u3A\nK1nPa8Pn4aNm1idrIrgP+JGkGyXdADxOMtpnUJJmSDo03Z8MnAM8W1dtOTAv3b8U+K77B1qjb/go\n4OGjZiWX6c3iiPiUpFXA76QfXRERTw7xtaOATknjSBLO1yNihaTFQDUilgP3APdJqpG0BC4b1k9h\nwzJt2jR27tzJtGnTig7FzAq0P1NJrwU2931H0jER8cJgldM3j09p8Pn1/fZ3AX+wHzHYCKnVamzZ\nsgWALVu2UKvVqFQqBUdlZkXIuh7BApK1i1cDK4CV6b82Rt18881Ny2ZWHllbBAuB30wXr7cDwIYN\nG5qWzaw8snYWv4innT6gtLe3Ny2bWXlkTQTPAWskXSvp431bnoFZvhYtWtS0bGblkTURvEDSP/A2\n3njbeGpeQVn+KpXKvlZAe3u7O4rNSizr8FG/bXQAWrRoEQsXLnRrwKzkMiUCSd+j8dQPs0c8ImuZ\nSqXCypUriw7DzAqWddTQJ/rtTwIuAXpHPhwzM2u1rI+Guuo++qGk7+cQj5mZtVjWR0OH9SseRDIt\n9ZG5RGRmZi2V9dFQF2/0EfQCG0hWFzMzszEuayI4EfgzkknnAngEqOYVlJmZtU7WRNAJbANuS8t/\nRDI1tSeMMzMb47Imgt+MiJP7lb8n6cd5BGRmZq2V9c3iJyWd0VeQ9B7gh/mEZGZmrdS0RSBpHUmf\nwARgrqQX0vKxwE/yD8/MzPI21KOh97UkCjMzK0zTRBARz7cqEDMzK0bWPgIzMztAORGYmZWcE4GZ\nWck5EZiZlZwTgZlZyTkRmJmVXG6JQNJMSd+TtF7SM5IWNqgzS9JWSWvT7fq84jEzs8ayzjU0HL3A\nNRHxhKSpQJek1RFR/0byIxHhF9fMzAqSW4sgIjZHxBPp/nZgPdCW1/XMzGx4WtJHIKkdOAV4vMHh\n35b0Y0nfkXTSIN+fL6kqqdrd3Z1jpGZm5ZN7IpA0BXgA+FhEbKs7/ARwbDrF9e3AtxqdIyLuioiO\niOiYMWNGvgGbmZVMrolA0gSSJPDliPhG/fGI2BYRO9L9B4EJkg7PMyYzMxsoz1FDAu4B1kfErYPU\nOTKth6TT03h68orJBurp6eHqq6+mp8f/5WZllmeL4Czgg8DsfsNDz5d0laSr0jqXAk+nq53dBlwW\nEZFjTNZPZ2cn69atY+nSpUWHYmYFym34aET8ANAQdT4PfD6vGGxwPT09rFq1iohg1apVzJ07l+nT\npxcdlpkVwG8Wl1RnZyd79+4FYM+ePW4VmJWYE0FJPfTQQ/T29gLQ29vL6tWrC47IzIriRFBS55xz\nDuPHJ08Gx48fz7nnnltwRGZWFCeCkpo3bx7pgC0kMXfu3IIjMrOiOBGU1PTp05k4cSIAEydOdEex\nWYk5EZRUrVZjx44dAOzYsYNarVZwRGZWFCeCkrr55publs2sPJwISmrDhg1Ny2ZWHk4EJdXe3t60\nbGbl4URQUosWLWpaNrPycCIoqUqlsq8V0N7eTqVSKTYgMyuME0GJLVq0iEMOOcStAbOSy3PNYhvl\nKpUKK1euLDoMMyuYWwRmZiXnRGBmVnJOBGZmJedEYGZWck4EZmYl50RgZlZyTgRmZiXnRGBmVnJO\nBGZmJedEYGZWck4EJTZr1qx9m9lo0tPTw9VXX01PT0/RoZRCbolA0kxJ35O0XtIzkhY2qCNJt0mq\nSXpK0ql5xWNmY0dnZyfr1q1j6dKlRYdSCnm2CHqBayLiPwBnAB+RdGJdnfOA49NtPnBHjvFYP/Wt\nALcKbLTo6elh1apVRASrVq1yq6AFcksEEbE5Ip5I97cD64G2umoXAUsj8RhwqKSj8orJzEa/zs5O\n9u7dC8CePXvcKmiBlvQRSGoHTgEerzvUBrzYr7yRNycLJM2XVJVU7e7uzitMMxsFHnroIXp7ewHo\n7e1l9erVBUd04Ms9EUiaAjwAfCwittUfbvCVeNMHEXdFREdEdMyYMSOPMM1slDjnnHMYPz5ZKmX8\n+PGce+65BUd04Ms1EUiaQJIEvhwR32hQZSMws1/5aOClPGMys9Ft3rx5HHRQ8qtp3LhxzJ07t+CI\nDnx5jhoScA+wPiJuHaTacmBuOnroDGBrRGzOKyZ7w5o1a5qWzYoyffp05syZgyTmzJnD9OnTiw7p\ngJfnUpVnAR8E1klam352HXAMQETcCTwInA/UgNeAK3KMx8zGiHnz5rFhwwa3BlpEEW96JD+qdXR0\nRLVaLToMM7MxRVJXRHQ0OuY3i83MSs6JwMys5JwIzMxKzonAzKzkxlxnsaRu4Pmi4ziAHA78vOgg\nzBrwvTmyjo2Ihm/kjrlEYCNLUnWwkQRmRfK92Tp+NGRmVnJOBGZmJedEYHcVHYDZIHxvtoj7CMzM\nSs4tAjOzknMiMDMrOSeCkpA0R9K/SKpJ+osGxydKuj89/ni6qpxZriR9UdLLkp4e5Lgk3Zbel09J\nOrXVMZaBE0EJSBoH/C1wHnAi8EeSTqyrdiXwakRUgL8BPt3aKK2k7gXmNDl+HnB8us0H7mhBTKXj\nRFAOpwO1iHguIl4HvgZcVFfnIqAz3V8G/Od0cSGz3ETEw8ArTapcBCyNxGPAoZKOak105eFEUA5t\nwIv9yhvTzxrWiYheYCvgpaGsaFnuXXuLnAjKodFf9vXjhrPUMWs135ct4ERQDhuBmf3KRwMvDVZH\n0njg7TRvspu1QpZ7194iJ4Jy+GfgeEm/IeltwGXA8ro6y4F56f6lwHfDbxta8ZYDc9PRQ2cAWyNi\nc9FBHWjyXLzeRomI6JX0UeAfgHHAFyPiGUmLgWpELAfuAe6TVCNpCVxWXMRWFpK+CswCDpe0EbgB\nmAAQEXcCDwLnAzXgNeCKYiI9sHmKCTOzkvOjITOzknMiMDMrOScCM7OScyIwMys5JwIzs5JzIjCr\nI+nRYX7v9xtM5teo3o2SPpHu3yvp0uFcz2ykOBGY1YmIM4f51d8nmd3VbExxIjCrI2lH+u8sSWsk\nLZP0rKQv983IKumvJP0knSP//0g6E7gQ+IyktZKOk/RhSf8s6ceSHpB0cJE/l9lg/GaxWXOnACeR\nzG/zQ+AsST8B/itwQkSEpEMj4heSlgMrImIZgKRfRMQX0v2bSdZ8uL2Qn8KsCbcIzJr7UURsjIi9\nwFqgHdgG7ALulnQxydQHjbxT0iOS1gF/QpJQzEYdJwKz5nb3298DjE/XazgdeICkX2DVIN+9F/ho\nRPwWcBMwKcc4zYbNj4bM9pOkKcDBEfGgpMdIJkQD2A5M7Vd1KrBZ0gSSFsGm1kZqlo0Tgdn+mwp8\nW9IkkoVT/kf6+deAL0i6mmQq7/8FPA48D6xjYJIwGzU8+6iZWcm5j8DMrOScCMzMSs6JwMys5JwI\nzMxKzonAzKzknAjMzErOicDMrOT+P4DcIHfAAymmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=outliers_df.bundleAvgUserRating,x=outliers_df.install)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before removing ads shown in 0-3.5 rated apps:  141762\n",
      "after removing ads shown in 0-3.5 rated apps:  141477\n"
     ]
    }
   ],
   "source": [
    "rating = ~outliers_df.bundleAvgUserRating.between(0,3.5)\n",
    "\n",
    "print(\"before removing ads shown in 0-3.5 rated apps: \",len(outliers_df))\n",
    "outliers_df = outliers_df.loc[rating] \n",
    "print(\"after removing ads shown in 0-3.5 rated apps: \",len(outliers_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27e0ad29588>"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/pCuAyZLyvuKIiO3ESG8o20wxpPNfWjOJ5wM7A2skfcf2B2uIMSIiKlT6l73t5ylm/14j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\niSAiouH+C5itAo1gtMCuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=outliers_df.bundleAvgUserRating,x=outliers_df.install)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RAM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27e0c100e48>"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AL5XttiLFOoq3P8c4YBzAoEGDdiVNMzPrQN4U5eupMDV5SUTsn3cCSX2AH5E9p/F62dtZ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\n5WJhZma5XCzMzCyXi4WZmeX6/3xsh4it5FS5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=outliers_df.uadeviceram,x=outliers_df.uadevicetype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tab_filt = outliers_df.uadevicetype == 'Tablet'\n",
    "ram = outliers_df.uadeviceram < 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before removing ads shown in low ram memory tablets::  141477\n",
      "after removing ads shown in low ram memory tablets:  141448\n"
     ]
    }
   ],
   "source": [
    "print(\"before removing ads shown in low ram memory tablets:: \",len(outliers_df))\n",
    "outliers_df = outliers_df[~(tab_filt & ram )]\n",
    "print(\"after removing ads shown in low ram memory tablets: \",len(outliers_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27e0c166898>"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OuoyuhqknsI3K+Pi4T8STRqi3I4Wq+r/APVs1Hw+sbvOrgROG2s+rgauAhUkO7as2bWvB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\nkjqGgiSp8/8Bup2bwKq2lUAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=outliers_df.ppi,x=outliers_df.uadevicetype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], dtype=object)"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outliers_df.model[outliers_df.ppi>700].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#These are Sony Xperia Models, there's nothing to imply on user behavior here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Encoding Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Since most of the data doesn't contain Continuous values, and the one which does  - PPI, There's no continouos data that we can impute in, and all is categorical data - we can apply  \"Feature_NA\" on our categorical Data when Encoding Data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# applying target encoding for columns recommended for mean ecoding\n",
    "outliers_df = target_encoder(outliers_df,target_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# applying one-hot_encoding for dummy columns\n",
    "outliers_df = pd.get_dummies(outliers_df,columns=dummies_columns,dummy_na=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(141448, 78)"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outliers_df.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "outliers_df.crid_mean = outliers_df.crid_mean.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running CatBoost\n",
      "running RF\n",
      "running LIGHTGBM\n"
     ]
    }
   ],
   "source": [
    "X = outliers_df.drop('install',axis=1)\n",
    "y = outliers_df.install\n",
    "\n",
    "coaf_df, light_cls, cat_cls, rf_cls = feature_importance_table(X, y, 2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "coaf_df.sort_values(by=['LIGHTGBM','CatBoost','RND_FOREST'],ascending=False)\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "coaf_df[coaf_df.columns] = scaler.fit_transform(coaf_df[coaf_df.columns])\n",
    "coaf_df['Avg_Score'] = coaf_df.mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_list = coaf_df.sort_values(by='Avg_Score',ascending=False).head(25).index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27e0cff1ac8>"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PXOI677rrLv3nP/+RJC1YsECPPvqo1ltvvaXars6iaQQAAEAE3d3l6ahRo7T55ptr0qRJ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\nIYQQ+tL/AGrvKrIzARmXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coaf_df.ix[feature_list].iloc[:,:3].plot.bar(figsize=(12,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X[feature_list], y,stratify=y,test_size=0.20)\n",
    "X_train, X_dev, y_train, y_dev = train_test_split(X_train, y_train,stratify=y_train,test_size=0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "ros_under = RandomUnderSampler()\n",
    "ros_over = RandomOverSampler()\n",
    "ros_smoth = SMOTE()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ros_under.fit(X_train, y_train)\n",
    "X_resampled_rus, y_resampled_rus = ros_under.fit_sample(X_train, y_train)\n",
    "X_resampled_ros, y_resampled_ros = ros_over.fit_sample(X_train, y_train)\n",
    "X_resampled_smo, y_resampled_smo = ros_smoth.fit_sample(X_train, y_train)\n",
    "#adding the feature list\n",
    "X_resampled_rus = pd.DataFrame(X_resampled_rus,columns=feature_list)\n",
    "X_resampled_ros = pd.DataFrame(X_resampled_ros,columns=feature_list)\n",
    "X_resampled_smo = pd.DataFrame(X_resampled_smo,columns=feature_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1020\n",
      "180032\n",
      "180032\n"
     ]
    }
   ],
   "source": [
    "print(len(X_resampled_rus))\n",
    "print(len(X_resampled_ros))\n",
    "print(len(X_resampled_smo))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cat Boost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Sampled = [\"NoneSampled\",\"UnderSampled\",\"OverSampled\",\"Smoth\"]\n",
    "Models = [\"CATBoost\",\"Light\",\"RF\"]\n",
    "\n",
    "all_models = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tqdm "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████| 4/4 [00:44<00:00, 11.82s/it]\n",
      "100%|████████████████████████████████████████████| 4/4 [00:10<00:00,  2.86s/it]\n",
      "100%|████████████████████████████████████████████| 4/4 [00:12<00:00,  3.18s/it]\n"
     ]
    }
   ],
   "source": [
    "classifiers = [CatBoostClassifier(verbose=False,iterations=100,learning_rate=0.05), LGBMClassifier(), RandomForestClassifier()]\n",
    "Datas = [(X_train,y_train),(X_resampled_rus,y_resampled_rus),(X_resampled_ros,y_resampled_ros),(X_resampled_smo,y_resampled_smo)]\n",
    "\n",
    "for i in range(3):\n",
    "    all_models[Models[i]] = {}\n",
    "    for j in tqdm.tqdm(range(4)):\n",
    "        all_models[Models[i]][Sampled[j]] = {} \n",
    "        all_models[Models[i]][Sampled[j]][\"Classifier\"] = classifiers[i]\n",
    "        all_models[Models[i]][Sampled[j]][\"Model\"] = all_models[Models[i]][Sampled[j]][\"Classifier\"]\\\n",
    "            .fit(X = Datas[j][0], y = Datas[j][1])\n",
    "        all_models[Models[i]][Sampled[j]][\"Prediction\"] = all_models[Models[i]][Sampled[j]][\"Model\"].predict(X_dev)\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Winning Model and Sampling Method with best auc:\n",
      "CATBoost  UnderSampled   0.7371\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "Model_Comparison = pd.DataFrame(columns = Sampled,index=Models)\n",
    "\n",
    "for model in Models: \n",
    "    for sample in Sampled:\n",
    "        fpr, tpr, thrasholds = roc_curve(y_dev, all_models[model][sample][\"Prediction\"], pos_label=1)\n",
    "        roc_auc = auc(fpr, tpr)\n",
    "        Model_Comparison.loc[model,sample] = roc_auc\n",
    "        \n",
    "        \n",
    "print(\"Winning Model and Sampling Method with best auc:\\n{}\".format(Model_Comparison.stack().sort_values(ascending=False).head(1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "folds = 20\n",
    "param_comb = 20\n",
    "skf = StratifiedKFold(n_splits=folds, shuffle = True, random_state = 1001)\n",
    "\n",
    "cat = CatBoostClassifier()\n",
    "params = {\n",
    "                  'depth':[3,1,2,6,4,5,7],\n",
    "                  'iterations':[128,256,512,64],\n",
    "                  'learning_rate':[0.03,0.01,0.1,0.2,0.3], \n",
    "                  'l2_leaf_reg':[3,1,5,10,30],\n",
    "                  'border_count':[32,5,10,20,64,128],\n",
    "                  \n",
    "                  \n",
    "\n",
    "         }\n",
    "\n",
    "random_search = RandomizedSearchCV(cat, param_distributions=params, n_iter=param_comb, n_jobs=-1, cv=skf.split(X_resampled_rus,y_resampled_rus),scoring='accuracy', verbose=3, random_state=1001 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 20 folds for each of 20 candidates, totalling 400 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  24 tasks      | elapsed:  2.7min\n",
      "[Parallel(n_jobs=-1)]: Done 120 tasks      | elapsed:  5.4min\n",
      "[Parallel(n_jobs=-1)]: Done 280 tasks      | elapsed: 16.5min\n",
      "[Parallel(n_jobs=-1)]: Done 400 out of 400 | elapsed: 22.9min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "505:\tlearn: 0.4212550\ttotal: 9.65s\tremaining: 114ms\n",
      "506:\tlearn: 0.4211783\ttotal: 9.67s\tremaining: 95.4ms\n",
      "507:\tlearn: 0.4210982\ttotal: 9.69s\tremaining: 76.3ms\n",
      "508:\tlearn: 0.4210841\ttotal: 9.71s\tremaining: 57.2ms\n",
      "509:\tlearn: 0.4210725\ttotal: 9.73s\tremaining: 38.1ms\n",
      "510:\tlearn: 0.4210623\ttotal: 9.74s\tremaining: 19.1ms\n",
      "511:\tlearn: 0.4210378\ttotal: 9.76s\tremaining: 0us\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=<generator object _BaseKFold.split at 0x0000027E0FA5CA98>,\n",
       "                   error_score='raise-deprecating',\n",
       "                   estimator=<catboost.core.CatBoostClassifier object at 0x0000027E15DFA978>,\n",
       "                   iid='warn', n_iter=20, n_jobs=-1,\n",
       "                   param_distributions={'border_count': [32, 5, 10, 20, 64,\n",
       "                                                         128],\n",
       "                                        'depth': [3, 1, 2, 6, 4, 5, 7],\n",
       "                                        'iterations': [128, 256, 512, 64],\n",
       "                                        'l2_leaf_reg': [3, 1, 5, 10, 30],\n",
       "                                        'learning_rate': [0.03, 0.01, 0.1, 0.2,\n",
       "                                                          0.3]},\n",
       "                   pre_dispatch='2*n_jobs', random_state=1001, refit=True,\n",
       "                   return_train_score=False, scoring='accuracy', verbose=3)"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.fit(X_resampled_rus,y_resampled_rus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'border_count': 64,\n",
       " 'depth': 1,\n",
       " 'iterations': 512,\n",
       " 'l2_leaf_reg': 3,\n",
       " 'learning_rate': 0.2}"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat = CatBoostClassifier(**random_search.best_params_,verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model = cat.fit(X=X_resampled_rus, y=y_resampled_rus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7350710255187489\n"
     ]
    }
   ],
   "source": [
    "final_pred = final_model.predict(X_test).astype(pd.Series)#.rename(\"pred_install\",axis=1)\n",
    "fpr, tpr, thrasholds = roc_curve(y_test, final_pred, pos_label=1)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "print(roc_auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>col_0</th>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>install</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0000</th>\n",
       "      <td>19241</td>\n",
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       "      <th>1.0000</th>\n",
       "      <td>34</td>\n",
       "      <td>125</td>\n",
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      ],
      "text/plain": [
       "col_0    0.0000  1.0000\n",
       "install                \n",
       "0.0000    19241    8890\n",
       "1.0000       34     125"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(y_test,final_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'False Positive Rate')"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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OEJHZwA7gVlVdU7hfwRXklltsNcsXXvA5E0mhRw94913raBo61Bc2dzEXTa2n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\neaJwzjmXr/8HCRdPjE58tmQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, 'b', label = 'AUC = %0.3f' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}