Categorical Featuring.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "firstDay = pd.Timestamp(2019, 4, 18)\n",
    "lastDay = pd.Timestamp(2019, 4, 27)\n",
    "\n",
    "location = 'data/'\n",
    "installsFile = location + 'installs.csv'\n",
    "eventsFile = location + 'events.csv'\n",
    "clicksFile = location + 'clicks.csv'\n",
    "auctionsFile = location + 'auctions.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtype = {\n",
    "    'advertiser_id': 'category',\n",
    "    'source_id': 'category',\n",
    "    'created': 'str',\n",
    "    'latitude': 'float32',\n",
    "    'longitude': 'float32',\n",
    "    'wifi_connection': 'bool',\n",
    "    'carrier_id': 'category',\n",
    "    'os_minor': 'category',\n",
    "    'os_major': 'category',\n",
    "    'agent_device': 'category',\n",
    "    'specs_brand': 'category',\n",
    "    'brand': 'category',\n",
    "    'timeToClick': 'float32',\n",
    "    'touchX': 'str',\n",
    "    'touchY': 'str',\n",
    "    'ref_type': 'category',\n",
    "    'ref_hash': 'int64',\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "clicks=pd.read_csv(clicksFile, usecols=dtype.keys(), dtype=dtype, parse_dates=['created'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 64296 entries, 0 to 64295\n",
      "Data columns (total 17 columns):\n",
      "advertiser_id      64296 non-null category\n",
      "source_id          64296 non-null category\n",
      "created            64296 non-null datetime64[ns]\n",
      "latitude           64296 non-null float32\n",
      "longitude          64296 non-null float32\n",
      "wifi_connection    64296 non-null bool\n",
      "carrier_id         63097 non-null category\n",
      "os_minor           64261 non-null category\n",
      "agent_device       8920 non-null category\n",
      "os_major           64261 non-null category\n",
      "specs_brand        64296 non-null category\n",
      "brand              15035 non-null category\n",
      "timeToClick        38178 non-null float32\n",
      "touchX             43678 non-null object\n",
      "touchY             43678 non-null object\n",
      "ref_type           64296 non-null category\n",
      "ref_hash           64296 non-null int64\n",
      "dtypes: bool(1), category(9), datetime64[ns](1), float32(3), int64(1), object(2)\n",
      "memory usage: 3.5+ MB\n"
     ]
    }
   ],
   "source": [
    "clicks.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "clicks=clicks.drop(['latitude','longitude','agent_device','touchX','touchY','brand','carrier_id','os_minor','os_major'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "onehot=pd.get_dummies(clicks['advertiser_id'])\n",
    "onehot.columns=['clicks_0_advertiser','clicks_1_advertiser','clicks_2_advertiser','clicks_3_advertiser','clicks_4_advertiser']\n",
    "clicks=pd.concat([clicks,onehot],axis=1)\n",
    "onehot=pd.get_dummies(clicks['source_id'])\n",
    "onehot.columns=['clicks_0_source_id','clicks_1_source_id','clicks_2_source_id','clicks_3_source_id','clicks_4_source_id','clicks_5_source_id','clicks_6_source_id','clicks_7_source_id','clicks_8_source_id']\n",
    "clicks=pd.concat([clicks,onehot],axis=1)\n",
    "onehot=pd.get_dummies(clicks['specs_brand'])\n",
    "onehot.columns=['clicks_0_specs','clicks_1_specs','clicks_2_specs','clicks_3_specs','clicks_4_specs']\n",
    "clicks=pd.concat([clicks,onehot],axis=1)\n",
    "onehot=pd.get_dummies(clicks['ref_type'])\n",
    "onehot.columns=['clicks_0_ref_type','clicks_1_ref_type']\n",
    "clicks=pd.concat([clicks,onehot],axis=1)\n",
    "onehot=pd.get_dummies(clicks['wifi_connection'])\n",
    "onehot.columns=['clicks_wifi_false','clicks_wifi_true']\n",
    "clicks=pd.concat([clicks,onehot],axis=1)\n",
    "\n",
    "clicks=clicks.drop(['ref_type','advertiser_id','source_id','created','specs_brand','timeToClick','wifi_connection'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtype = {\n",
    "    'created': 'str',\n",
    "    'application_id': 'category',\n",
    "    'ref_type': 'category',\n",
    "    'ref_hash': 'int64',\n",
    "    'click_hash': 'str',\n",
    "    'attributed': 'bool',\n",
    "    'implicit': 'bool',\n",
    "    'device_brand': 'float64',\n",
    "    'device_model': 'float64',\n",
    "    'session_user_agent': 'str',\n",
    "    'user_agent': 'str',\n",
    "    'event_uuid': 'str',\n",
    "    'kind': 'str',\n",
    "    'wifi': 'str',\n",
    "    'trans_id': 'str',\n",
    "    'ip_address': 'int64',\n",
    "    'device_language': 'category',\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "installs=pd.read_csv(installsFile, usecols=dtype.keys(), dtype=dtype, parse_dates=['created'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 481511 entries, 0 to 481510\n",
      "Data columns (total 17 columns):\n",
      "created               481511 non-null datetime64[ns]\n",
      "application_id        481511 non-null category\n",
      "ref_type              481511 non-null category\n",
      "ref_hash              481511 non-null int64\n",
      "click_hash            1142 non-null object\n",
      "attributed            481511 non-null bool\n",
      "implicit              481511 non-null bool\n",
      "device_brand          276443 non-null float64\n",
      "device_model          454619 non-null float64\n",
      "session_user_agent    466672 non-null object\n",
      "user_agent            330768 non-null object\n",
      "event_uuid            103168 non-null object\n",
      "kind                  103168 non-null object\n",
      "wifi                  294829 non-null object\n",
      "trans_id              8933 non-null object\n",
      "ip_address            481511 non-null int64\n",
      "device_language       453934 non-null category\n",
      "dtypes: bool(2), category(3), datetime64[ns](1), float64(2), int64(2), object(7)\n",
      "memory usage: 47.3+ MB\n"
     ]
    }
   ],
   "source": [
    "installs.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "installs=installs.drop(['application_id','ip_address','kind','device_brand','device_model','session_user_agent','event_uuid','trans_id','click_hash','user_agent','device_language','created','ref_type'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "installs['wifi']=installs['wifi'].fillna('unknown')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "onehot=pd.get_dummies(installs['attributed'])\n",
    "onehot.columns=['installs_attributed_false','installs_attributed_true']\n",
    "installs=pd.concat([installs,onehot],axis=1)\n",
    "onehot=pd.get_dummies(installs['implicit'])\n",
    "onehot.columns=['installs_implicit_false','installs_implicit_true']\n",
    "installs=pd.concat([installs,onehot],axis=1)\n",
    "onehot=pd.get_dummies(installs['wifi'])\n",
    "onehot.columns=['installs_wifi_false','installs_wifi_true','installs_wifi_unknown']\n",
    "installs=pd.concat([installs,onehot],axis=1)\n",
    "\n",
    "installs=installs.drop(['attributed','implicit','wifi'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtype = {\n",
    "    'date': 'str',\n",
    "    'event_id': 'int8',\n",
    "    'ref_type': 'category',\n",
    "    'ref_hash': 'int64',\n",
    "    'application_id': 'int64',\n",
    "    'attributed': 'bool',\n",
    "    'device_os_version': 'float64',\n",
    "    'device_brand': 'float64',\n",
    "    'device_model': 'float64',\n",
    "    'device_city': 'float64',\n",
    "    'session_user_agent': 'float64',\n",
    "    'trans_id': 'str',\n",
    "    'user_agent': 'float64',\n",
    "    'event_uuid': 'str',\n",
    "    'carrier': 'float64',\n",
    "    'kind': 'float64',\n",
    "    'device_os': 'category',\n",
    "    'wifi': 'bool',\n",
    "    'connection_type': 'category',\n",
    "    'ip_address': 'int64',\n",
    "    'device_language': 'category'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "events=pd.read_csv(eventsFile, usecols=dtype.keys(), dtype=dtype, parse_dates=['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7744581 entries, 0 to 7744580\n",
      "Data columns (total 21 columns):\n",
      "date                  datetime64[ns]\n",
      "event_id              int8\n",
      "ref_type              category\n",
      "ref_hash              int64\n",
      "application_id        int64\n",
      "attributed            bool\n",
      "device_os_version     float64\n",
      "device_brand          float64\n",
      "device_model          float64\n",
      "device_city           float64\n",
      "session_user_agent    float64\n",
      "trans_id              object\n",
      "user_agent            float64\n",
      "event_uuid            object\n",
      "carrier               float64\n",
      "kind                  float64\n",
      "device_os             category\n",
      "wifi                  bool\n",
      "connection_type       category\n",
      "ip_address            int64\n",
      "device_language       category\n",
      "dtypes: bool(2), category(4), datetime64[ns](1), float64(8), int64(3), int8(1), object(2)\n",
      "memory usage: 886.3+ MB\n"
     ]
    }
   ],
   "source": [
    "events.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "events=events.astype({'device_os':'object','connection_type':'object'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "events['device_os']=events['device_os'].fillna('unknown')\n",
    "events['connection_type']=events['connection_type'].fillna('unknown')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "events=events.drop(['date','event_id','device_os_version','device_brand','device_model','device_city','session_user_agent','trans_id','user_agent','event_uuid','carrier','kind','ip_address','device_language','application_id'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#events['ref_type'].value_counts().count()#2\n",
    "#events['attributed'].value_counts().count()#2\n",
    "#events['device_os'].value_counts()\n",
    "#events['wifi'].value_counts().count()#2\n",
    "#events['connection_type'].value_counts().count()#5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "onehot=pd.get_dummies(events['attributed'])\n",
    "onehot.columns=['event_attributed_false','event_attributed_true']\n",
    "events=pd.concat([events,onehot],axis=1)\n",
    "onehot=pd.get_dummies(events['ref_type'])\n",
    "onehot.columns=['event_ref_type0','event_ref_type1']\n",
    "events=pd.concat([events,onehot],axis=1)\n",
    "onehot=pd.get_dummies(events['device_os'])\n",
    "onehot.columns=['event_device_os0','event_device_os0','unknown']\n",
    "events=pd.concat([events,onehot],axis=1)\n",
    "onehot=pd.get_dummies(events['wifi'])\n",
    "onehot.columns=['event_wifiFalse','event_wifiTrue']\n",
    "events=pd.concat([events,onehot],axis=1)\n",
    "onehot=pd.get_dummies(events['connection_type'])\n",
    "onehot.columns=['event_connection_type_Cable/DSL','event_connection_type_Cellular','event_connection_type_Corporate','event_connection_type_Dialup','event_connection_type_Unknown']\n",
    "events=pd.concat([events,onehot],axis=1)\n",
    "\n",
    "events=events.drop(['ref_type','attributed','device_os','wifi','connection_type'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
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      "text/plain": [
       "              ref_hash  event_attributed_false  event_attributed_true  \\\n",
       "0  5857744372586891366                       1                      0   \n",
       "1  7642521036780133571                       1                      0   \n",
       "2  2548841562898283198                       1                      0   \n",
       "3   609402887625919085                       1                      0   \n",
       "4  9114651763556439823                       1                      0   \n",
       "\n",
       "   event_ref_type0  event_ref_type1  event_device_os0  event_device_os0  \\\n",
       "0                1                0                 0                 0   \n",
       "1                1                0                 0                 0   \n",
       "2                1                0                 0                 0   \n",
       "3                1                0                 0                 0   \n",
       "4                1                0                 0                 0   \n",
       "\n",
       "   unknown  event_wifiFalse  event_wifiTrue  event_connection_type_Cable/DSL  \\\n",
       "0        1                1               0                                0   \n",
       "1        1                1               0                                0   \n",
       "2        1                1               0                                0   \n",
       "3        1                1               0                                0   \n",
       "4        1                1               0                                0   \n",
       "\n",
       "   event_connection_type_Cellular  event_connection_type_Corporate  \\\n",
       "0                               0                                0   \n",
       "1                               0                                0   \n",
       "2                               0                                0   \n",
       "3                               0                                0   \n",
       "4                               0                                0   \n",
       "\n",
       "   event_connection_type_Dialup  event_connection_type_Unknown  \n",
       "0                             0                              1  \n",
       "1                             0                              1  \n",
       "2                             0                              1  \n",
       "3                             0                              1  \n",
       "4                             0                              1  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtype = {\n",
    "    'device_id': 'int64',\n",
    "    'date': 'str',\n",
    "    'ref_type_id': 'category',\n",
    "    'source_id': 'category',\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "auctions=pd.read_csv(auctionsFile, usecols=dtype.keys(), dtype=dtype, parse_dates=['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 47409528 entries, 0 to 47409527\n",
      "Data columns (total 4 columns):\n",
      "date           datetime64[ns]\n",
      "device_id      int64\n",
      "ref_type_id    category\n",
      "source_id      category\n",
      "dtypes: category(2), datetime64[ns](1), int64(1)\n",
      "memory usage: 813.8 MB\n"
     ]
    }
   ],
   "source": [
    "auctions.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "onehot=pd.get_dummies(auctions['ref_type_id'])\n",
    "onehot.columns=['auctions_ref_type_id1','auctions_ref_type_id7']\n",
    "auctions=pd.concat([auctions,onehot],axis=1)\n",
    "onehot=pd.get_dummies(auctions['source_id'])\n",
    "onehot.columns=['auctions_source_id0','auctions_source_id1','auctions_source_id2','auctions_source_id3','auctions_source_id4','auctions_source_id5','auctions_source_id6','auctions_source_id7','auctions_source_id8','auctions_source_id9']\n",
    "auctions=pd.concat([auctions,onehot],axis=1)\n",
    "\n",
    "\n",
    "\n",
    "auctions=auctions.drop(['ref_type_id','source_id','date'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "clicks=clicks.rename(columns={'ref_hash':'device_id'})\n",
    "installs=installs.rename(columns={'ref_hash':'device_id'})\n",
    "events=events.rename(columns={'ref_hash':'device_id'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
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       "             device_id  clicks_0_advertiser  clicks_1_advertiser  \\\n",
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       "4                  0                  1                  0                 1  \n",
       "\n",
       "[5 rows x 24 columns]"
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     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clicks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "             device_id  installs_attributed_false  installs_attributed_true  \\\n",
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     "execution_count": 28,
     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
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       "             device_id  event_attributed_false  event_attributed_true  \\\n",
       "0  5857744372586891366                       1                      0   \n",
       "1  7642521036780133571                       1                      0   \n",
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       "2        1                1               0                                0   \n",
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       "\n",
       "   event_connection_type_Cellular  event_connection_type_Corporate  \\\n",
       "0                               0                                0   \n",
       "1                               0                                0   \n",
       "2                               0                                0   \n",
       "3                               0                                0   \n",
       "4                               0                                0   \n",
       "\n",
       "   event_connection_type_Dialup  event_connection_type_Unknown  \n",
       "0                             0                              1  \n",
       "1                             0                              1  \n",
       "2                             0                              1  \n",
       "3                             0                              1  \n",
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      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
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       "             device_id  auctions_ref_type_id1  auctions_ref_type_id7  \\\n",
       "0  2564673204772915246                      1                      0   \n",
       "1  4441121667607578179                      0                      1   \n",
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       "0                    1                    0                    0   \n",
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     "metadata": {},
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    "auctions.head()"
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "events=events.groupby('device_id').sum().reset_index()\n",
    "installs=installs.groupby('device_id').sum().reset_index()\n",
    "clicks=clicks.groupby('device_id').sum().reset_index()\n",
    "auctions=auctions.groupby('device_id').sum().reset_index()"
   ]
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
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       "          device_id  clicks_0_advertiser  clicks_1_advertiser  \\\n",
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       "\n",
       "[5 rows x 24 columns]"
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     },
     "execution_count": 32,
     "metadata": {},
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   "source": [
    "clicks.head()"
   ]
  },
  {
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   "execution_count": 33,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "         device_id  installs_attributed_false  installs_attributed_true  \\\n",
       "0   40621409780134                          1                         0   \n",
       "1   41863526108385                          4                         0   \n",
       "2   90072729247980                          1                         0   \n",
       "3  135153013040192                          1                         0   \n",
       "4  161514654074162                          1                         0   \n",
       "\n",
       "   installs_implicit_false  installs_implicit_true  installs_wifi_false  \\\n",
       "0                        1                       0                    0   \n",
       "1                        2                       2                    4   \n",
       "2                        1                       0                    0   \n",
       "3                        1                       0                    0   \n",
       "4                        0                       1                    0   \n",
       "\n",
       "   installs_wifi_true  installs_wifi_unknown  \n",
       "0                   0                      1  \n",
       "1                   0                      0  \n",
       "2                   0                      1  \n",
       "3                   0                      1  \n",
       "4                   1                      0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "installs.head()"
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  {
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       "         device_id  event_attributed_false  event_attributed_true  \\\n",
       "0   40621409780134                     9.0                    0.0   \n",
       "1   41863526108385                   153.0                    0.0   \n",
       "2   69039685746313                    20.0                    0.0   \n",
       "3   90072729247980                     3.0                    0.0   \n",
       "4  161514654074162                    68.0                    0.0   \n",
       "\n",
       "   event_ref_type0  event_ref_type1  event_device_os0  event_device_os0  \\\n",
       "0              9.0              0.0               0.0               9.0   \n",
       "1            153.0              0.0               0.0               0.0   \n",
       "2             20.0              0.0               0.0               0.0   \n",
       "3              3.0              0.0               0.0               0.0   \n",
       "4             68.0              0.0               0.0               0.0   \n",
       "\n",
       "   unknown  event_wifiFalse  event_wifiTrue  event_connection_type_Cable/DSL  \\\n",
       "0      0.0              9.0             0.0                              9.0   \n",
       "1    153.0            153.0             0.0                              0.0   \n",
       "2     20.0              3.0            17.0                              0.0   \n",
       "3      3.0              3.0             0.0                              0.0   \n",
       "4     68.0              2.0            66.0                              0.0   \n",
       "\n",
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       "2                             0.0                              0.0   \n",
       "3                             0.0                              0.0   \n",
       "4                             0.0                              0.0   \n",
       "\n",
       "   event_connection_type_Dialup  event_connection_type_Unknown  \n",
       "0                           0.0                            0.0  \n",
       "1                           0.0                          153.0  \n",
       "2                           0.0                           20.0  \n",
       "3                           0.0                            3.0  \n",
       "4                           0.0                           68.0  "
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     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "events.head()"
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  {
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   "execution_count": 35,
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       "         device_id  auctions_ref_type_id1  auctions_ref_type_id7  \\\n",
       "0   40621409780134                    1.0                    0.0   \n",
       "1   41863526108385                   35.0                    0.0   \n",
       "2   69039685746313                    4.0                    0.0   \n",
       "3  135153013040192                    8.0                    0.0   \n",
       "4  161514654074162                    6.0                    0.0   \n",
       "\n",
       "   auctions_source_id0  auctions_source_id1  auctions_source_id2  \\\n",
       "0                  1.0                  0.0                  0.0   \n",
       "1                  0.0                  0.0                  0.0   \n",
       "2                  0.0                  0.0                  0.0   \n",
       "3                  0.0                  8.0                  0.0   \n",
       "4                  4.0                  1.0                  0.0   \n",
       "\n",
       "   auctions_source_id3  auctions_source_id4  auctions_source_id5  \\\n",
       "0                  0.0                  0.0                  0.0   \n",
       "1                 28.0                  0.0                  3.0   \n",
       "2                  0.0                  0.0                  0.0   \n",
       "3                  0.0                  0.0                  0.0   \n",
       "4                  0.0                  0.0                  0.0   \n",
       "\n",
       "   auctions_source_id6  auctions_source_id7  auctions_source_id8  \\\n",
       "0                  0.0                  0.0                  0.0   \n",
       "1                  0.0                  0.0                  4.0   \n",
       "2                  0.0                  4.0                  0.0   \n",
       "3                  0.0                  0.0                  0.0   \n",
       "4                  0.0                  0.0                  1.0   \n",
       "\n",
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       "1                  0.0  \n",
       "2                  0.0  \n",
       "3                  0.0  \n",
       "4                  0.0  "
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     "execution_count": 35,
     "metadata": {},
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31779</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31780</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31781</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31782</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31783</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31784</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31785</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31786</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31787</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31788</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31789</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31790</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31791</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31792</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31793</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31794</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31795</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31796</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31797</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31798</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31799</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31800</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31801</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31802</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31803</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31804</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31805</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31806</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31807</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31808</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>31809 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       clicks_1_specs  clicks_2_specs  clicks_3_specs  clicks_4_specs\n",
       "0                   0               0               0               0\n",
       "1                   0               0               0               0\n",
       "2                   0               0               1               0\n",
       "3                   0               0               2               0\n",
       "4                   0               0               4               0\n",
       "5                   0               0               1               0\n",
       "6                   0               0               0               0\n",
       "7                   0               0               1               0\n",
       "8                   0               0               3               0\n",
       "9                   0               0               2               0\n",
       "10                  0               0               2               0\n",
       "11                  0               0               0               0\n",
       "12                  0               0               0               0\n",
       "13                  0               0               3               0\n",
       "14                  0               0               2               0\n",
       "15                  0               0               1               0\n",
       "16                  0               0               3               0\n",
       "17                  0               0               0               0\n",
       "18                  0               0               0               0\n",
       "19                  0               0               2               0\n",
       "20                  0               0               1               0\n",
       "21                  0               0               3               0\n",
       "22                  0               0               2               0\n",
       "23                  0               0               1               0\n",
       "24                  0               0               2               0\n",
       "25                  0               0               2               0\n",
       "26                  0               0               2               0\n",
       "27                  0               0               1               0\n",
       "28                  0               0               3               0\n",
       "29                  0               0               1               0\n",
       "...               ...             ...             ...             ...\n",
       "31779               0               0               0               0\n",
       "31780               0               0               0               0\n",
       "31781               0               0               0               0\n",
       "31782               0               0               1               0\n",
       "31783               0               0               1               0\n",
       "31784               0               0               1               0\n",
       "31785               0               0               1               0\n",
       "31786               0               0               0               0\n",
       "31787               0               0               4               0\n",
       "31788               0               0               1               0\n",
       "31789               0               0               1               0\n",
       "31790               0               0               1               0\n",
       "31791               0               0               1               0\n",
       "31792               0               0               1               0\n",
       "31793               0               0               0               0\n",
       "31794               0               0               3               0\n",
       "31795               0               0               0               0\n",
       "31796               0               0               3               0\n",
       "31797               0               0               1               0\n",
       "31798               0               0               0               0\n",
       "31799               0               0               0               0\n",
       "31800               0               0               0               0\n",
       "31801               0               0               2               0\n",
       "31802               0               0               0               0\n",
       "31803               0               0               1               0\n",
       "31804               0               0               2               0\n",
       "31805               0               0               0               0\n",
       "31806               0               0               1               0\n",
       "31807               0               0               0               0\n",
       "31808               0               0               0               0\n",
       "\n",
       "[31809 rows x 4 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clicks[['clicks_1_specs','clicks_2_specs','clicks_3_specs','clicks_4_specs']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "categoricoTotal=auctions.merge(clicks,how='outer')\n",
    "categoricoTotal=categoricoTotal.merge(installs,how='outer')\n",
    "categoricoTotal=categoricoTotal.merge(events,how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>device_id</th>\n",
       "      <th>auctions_ref_type_id1</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <th>4</th>\n",
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       "  </tbody>\n",
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       "<p>5 rows × 57 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         device_id  auctions_ref_type_id1  auctions_ref_type_id7  \\\n",
       "0   40621409780134                    1.0                    0.0   \n",
       "1   41863526108385                   35.0                    0.0   \n",
       "2   69039685746313                    4.0                    0.0   \n",
       "3  135153013040192                    8.0                    0.0   \n",
       "4  161514654074162                    6.0                    0.0   \n",
       "\n",
       "   auctions_source_id0  auctions_source_id1  auctions_source_id2  \\\n",
       "0                  1.0                  0.0                  0.0   \n",
       "1                  0.0                  0.0                  0.0   \n",
       "2                  0.0                  0.0                  0.0   \n",
       "3                  0.0                  8.0                  0.0   \n",
       "4                  4.0                  1.0                  0.0   \n",
       "\n",
       "   auctions_source_id3  auctions_source_id4  auctions_source_id5  \\\n",
       "0                  0.0                  0.0                  0.0   \n",
       "1                 28.0                  0.0                  3.0   \n",
       "2                  0.0                  0.0                  0.0   \n",
       "3                  0.0                  0.0                  0.0   \n",
       "4                  0.0                  0.0                  0.0   \n",
       "\n",
       "   auctions_source_id6              ...                event_device_os0  \\\n",
       "0                  0.0              ...                             0.0   \n",
       "1                  0.0              ...                             0.0   \n",
       "2                  0.0              ...                             0.0   \n",
       "3                  0.0              ...                             NaN   \n",
       "4                  0.0              ...                             0.0   \n",
       "\n",
       "   event_device_os0  unknown  event_wifiFalse  event_wifiTrue  \\\n",
       "0               9.0      0.0              9.0             0.0   \n",
       "1               0.0    153.0            153.0             0.0   \n",
       "2               0.0     20.0              3.0            17.0   \n",
       "3               NaN      NaN              NaN             NaN   \n",
       "4               0.0     68.0              2.0            66.0   \n",
       "\n",
       "   event_connection_type_Cable/DSL  event_connection_type_Cellular  \\\n",
       "0                              9.0                             0.0   \n",
       "1                              0.0                             0.0   \n",
       "2                              0.0                             0.0   \n",
       "3                              NaN                             NaN   \n",
       "4                              0.0                             0.0   \n",
       "\n",
       "   event_connection_type_Corporate  event_connection_type_Dialup  \\\n",
       "0                              0.0                           0.0   \n",
       "1                              0.0                           0.0   \n",
       "2                              0.0                           0.0   \n",
       "3                              NaN                           NaN   \n",
       "4                              0.0                           0.0   \n",
       "\n",
       "   event_connection_type_Unknown  \n",
       "0                            0.0  \n",
       "1                          153.0  \n",
       "2                           20.0  \n",
       "3                            NaN  \n",
       "4                           68.0  \n",
       "\n",
       "[5 rows x 57 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categoricoTotal.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "clicks.to_csv('data/clicksCategorico.csv',index=False)\n",
    "installs.to_csv('data/installsCategorico.csv',index=False)\n",
    "events.to_csv('data/eventsCategorico.csv',index=False)\n",
    "auctions.to_csv('data/auctionsCategorico.csv',index=False)\n",
    "categoricoTotal.to_csv('data/featuresCategorico.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "categoricoTotal.to_csv('data/featuresCategorico.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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