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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['train_users_2.csv', 'countries.csv', 'test_users.csv', 'sessions.csv', 'sample_submission_NDF.csv', 'age_gender_bkts.csv', 'sample_submission.csv']\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"import os\n",
"print(os.listdir(\"../input\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_uuid": "8dcb706097d52a55242f9d17827301238d1237d1"
},
"outputs": [],
"source": [
"#invite people for the Kaggle party\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import numpy as np\n",
"from scipy.stats import norm\n",
"from sklearn.preprocessing import StandardScaler\n",
"from scipy import stats\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"%matplotlib inline\n",
"\n",
"from sklearn import preprocessing\n",
"from sklearn.preprocessing import LabelEncoder"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" 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>id</th>\n",
" <th>country</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>000am9932b</td>\n",
" <td>NDF</td>\n",
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" <td>000c16lc82</td>\n",
" <td>NDF</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>000c16lc82</td>\n",
" <td>US</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>000hpr0pqh</td>\n",
" <td>NDF</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>000hpr0pqh</td>\n",
" <td>US</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>000o0iw8io</td>\n",
" <td>NDF</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>000o0iw8io</td>\n",
" <td>US</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>001h113uzc</td>\n",
" <td>NDF</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>001h113uzc</td>\n",
" <td>US</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id country\n",
"0 000am9932b NDF\n",
"1 000am9932b US\n",
"2 000c16lc82 NDF\n",
"3 000c16lc82 US\n",
"4 000hpr0pqh NDF\n",
"5 000hpr0pqh US\n",
"6 000o0iw8io NDF\n",
"7 000o0iw8io US\n",
"8 001h113uzc NDF\n",
"9 001h113uzc US"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sessions = pd.read_csv('../input/sample_submission.csv')\n",
"sessions.head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "4f45a23b26637c58cfea97bb218898aa434a87b8"
},
"source": [
"**Data Initialize**"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
"_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
},
"outputs": [],
"source": [
"# Load the data into DataFrames\n",
"train_users = pd.read_csv('../input/train_users_2.csv')\n",
"test_users = pd.read_csv('../input/test_users.csv')\n",
"age_gender_bkts = pd.read_csv('../input/age_gender_bkts.csv')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_uuid": "158767ba6f89e148ee4c5abc8bd0309529b90cfc"
},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>date_account_created</th>\n",
" <th>timestamp_first_active</th>\n",
" <th>date_first_booking</th>\n",
" <th>gender</th>\n",
" <th>age</th>\n",
" <th>signup_method</th>\n",
" <th>signup_flow</th>\n",
" <th>language</th>\n",
" <th>affiliate_channel</th>\n",
" <th>affiliate_provider</th>\n",
" <th>first_affiliate_tracked</th>\n",
" <th>signup_app</th>\n",
" <th>first_device_type</th>\n",
" <th>first_browser</th>\n",
" <th>country_destination</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>gxn3p5htnn</td>\n",
" <td>2010-06-28</td>\n",
" <td>20090319043255</td>\n",
" <td>NaN</td>\n",
" <td>-unknown-</td>\n",
" <td>NaN</td>\n",
" <td>facebook</td>\n",
" <td>0</td>\n",
" <td>en</td>\n",
" <td>direct</td>\n",
" <td>direct</td>\n",
" <td>untracked</td>\n",
" <td>Web</td>\n",
" <td>Mac Desktop</td>\n",
" <td>Chrome</td>\n",
" <td>NDF</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>820tgsjxq7</td>\n",
" <td>2011-05-25</td>\n",
" <td>20090523174809</td>\n",
" <td>NaN</td>\n",
" <td>MALE</td>\n",
" <td>38.0</td>\n",
" <td>facebook</td>\n",
" <td>0</td>\n",
" <td>en</td>\n",
" <td>seo</td>\n",
" <td>google</td>\n",
" <td>untracked</td>\n",
" <td>Web</td>\n",
" <td>Mac Desktop</td>\n",
" <td>Chrome</td>\n",
" <td>NDF</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4ft3gnwmtx</td>\n",
" <td>2010-09-28</td>\n",
" <td>20090609231247</td>\n",
" <td>2010-08-02</td>\n",
" <td>FEMALE</td>\n",
" <td>56.0</td>\n",
" <td>basic</td>\n",
" <td>3</td>\n",
" <td>en</td>\n",
" <td>direct</td>\n",
" <td>direct</td>\n",
" <td>untracked</td>\n",
" <td>Web</td>\n",
" <td>Windows Desktop</td>\n",
" <td>IE</td>\n",
" <td>US</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>bjjt8pjhuk</td>\n",
" <td>2011-12-05</td>\n",
" <td>20091031060129</td>\n",
" <td>2012-09-08</td>\n",
" <td>FEMALE</td>\n",
" <td>42.0</td>\n",
" <td>facebook</td>\n",
" <td>0</td>\n",
" <td>en</td>\n",
" <td>direct</td>\n",
" <td>direct</td>\n",
" <td>untracked</td>\n",
" <td>Web</td>\n",
" <td>Mac Desktop</td>\n",
" <td>Firefox</td>\n",
" <td>other</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>87mebub9p4</td>\n",
" <td>2010-09-14</td>\n",
" <td>20091208061105</td>\n",
" <td>2010-02-18</td>\n",
" <td>-unknown-</td>\n",
" <td>41.0</td>\n",
" <td>basic</td>\n",
" <td>0</td>\n",
" <td>en</td>\n",
" <td>direct</td>\n",
" <td>direct</td>\n",
" <td>untracked</td>\n",
" <td>Web</td>\n",
" <td>Mac Desktop</td>\n",
" <td>Chrome</td>\n",
" <td>US</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>osr2jwljor</td>\n",
" <td>2010-01-01</td>\n",
" <td>20100101215619</td>\n",
" <td>2010-01-02</td>\n",
" <td>-unknown-</td>\n",
" <td>NaN</td>\n",
" <td>basic</td>\n",
" <td>0</td>\n",
" <td>en</td>\n",
" <td>other</td>\n",
" <td>other</td>\n",
" <td>omg</td>\n",
" <td>Web</td>\n",
" <td>Mac Desktop</td>\n",
" <td>Chrome</td>\n",
" <td>US</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>lsw9q7uk0j</td>\n",
" <td>2010-01-02</td>\n",
" <td>20100102012558</td>\n",
" <td>2010-01-05</td>\n",
" <td>FEMALE</td>\n",
" <td>46.0</td>\n",
" <td>basic</td>\n",
" <td>0</td>\n",
" <td>en</td>\n",
" <td>other</td>\n",
" <td>craigslist</td>\n",
" <td>untracked</td>\n",
" <td>Web</td>\n",
" <td>Mac Desktop</td>\n",
" <td>Safari</td>\n",
" <td>US</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0d01nltbrs</td>\n",
" <td>2010-01-03</td>\n",
" <td>20100103191905</td>\n",
" <td>2010-01-13</td>\n",
" <td>FEMALE</td>\n",
" <td>47.0</td>\n",
" <td>basic</td>\n",
" <td>0</td>\n",
" <td>en</td>\n",
" <td>direct</td>\n",
" <td>direct</td>\n",
" <td>omg</td>\n",
" <td>Web</td>\n",
" <td>Mac Desktop</td>\n",
" <td>Safari</td>\n",
" <td>US</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>a1vcnhxeij</td>\n",
" <td>2010-01-04</td>\n",
" <td>20100104004211</td>\n",
" <td>2010-07-29</td>\n",
" <td>FEMALE</td>\n",
" <td>50.0</td>\n",
" <td>basic</td>\n",
" <td>0</td>\n",
" <td>en</td>\n",
" <td>other</td>\n",
" <td>craigslist</td>\n",
" <td>untracked</td>\n",
" <td>Web</td>\n",
" <td>Mac Desktop</td>\n",
" <td>Safari</td>\n",
" <td>US</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>6uh8zyj2gn</td>\n",
" <td>2010-01-04</td>\n",
" <td>20100104023758</td>\n",
" <td>2010-01-04</td>\n",
" <td>-unknown-</td>\n",
" <td>46.0</td>\n",
" <td>basic</td>\n",
" <td>0</td>\n",
" <td>en</td>\n",
" <td>other</td>\n",
" <td>craigslist</td>\n",
" <td>omg</td>\n",
" <td>Web</td>\n",
" <td>Mac Desktop</td>\n",
" <td>Firefox</td>\n",
" <td>US</td>\n",
" </tr>\n",
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],
"text/plain": [
" id ... country_destination\n",
"0 gxn3p5htnn ... NDF\n",
"1 820tgsjxq7 ... NDF\n",
"2 4ft3gnwmtx ... US\n",
"3 bjjt8pjhuk ... other\n",
"4 87mebub9p4 ... US\n",
"5 osr2jwljor ... US\n",
"6 lsw9q7uk0j ... US\n",
"7 0d01nltbrs ... US\n",
"8 a1vcnhxeij ... US\n",
"9 6uh8zyj2gn ... US\n",
"\n",
"[10 rows x 16 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_users.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_uuid": "383cc58f6bec2ad3580d4fdc7863d6f65a96da81"
},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age_bucket</th>\n",
" <th>country_destination</th>\n",
" <th>gender</th>\n",
" <th>population_in_thousands</th>\n",
" <th>year</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>100+</td>\n",
" <td>AU</td>\n",
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" <td>2015.0</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>95-99</td>\n",
" <td>AU</td>\n",
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" <td>9.0</td>\n",
" <td>2015.0</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>90-94</td>\n",
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" <td>47.0</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>85-89</td>\n",
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" <td>2015.0</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>80-84</td>\n",
" <td>AU</td>\n",
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" <td>2015.0</td>\n",
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" <th>5</th>\n",
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" <tr>\n",
" <th>6</th>\n",
" <td>70-74</td>\n",
" <td>AU</td>\n",
" <td>male</td>\n",
" <td>415.0</td>\n",
" <td>2015.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>65-69</td>\n",
" <td>AU</td>\n",
" <td>male</td>\n",
" <td>574.0</td>\n",
" <td>2015.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>60-64</td>\n",
" <td>AU</td>\n",
" <td>male</td>\n",
" <td>636.0</td>\n",
" <td>2015.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>55-59</td>\n",
" <td>AU</td>\n",
" <td>male</td>\n",
" <td>714.0</td>\n",
" <td>2015.0</td>\n",
" </tr>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" age_bucket country_destination gender population_in_thousands year\n",
"0 100+ AU male 1.0 2015.0\n",
"1 95-99 AU male 9.0 2015.0\n",
"2 90-94 AU male 47.0 2015.0\n",
"3 85-89 AU male 118.0 2015.0\n",
"4 80-84 AU male 199.0 2015.0\n",
"5 75-79 AU male 298.0 2015.0\n",
"6 70-74 AU male 415.0 2015.0\n",
"7 65-69 AU male 574.0 2015.0\n",
"8 60-64 AU male 636.0 2015.0\n",
"9 55-59 AU male 714.0 2015.0"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"age_gender_bkts.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"df_train = train_users.copy()\n",
"df_test = test_users.copy()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_kg_hide-input": false
},
"source": [
"2.Data Cleaning\n",
"============="
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2.1.Missing Data\n",
"-----------"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_uuid": "4640fa872c1bf32173641344409586297b2e2091"
},
"outputs": [
{
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" <th>country_destination</th>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
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" <th>first_device_type</th>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>signup_app</th>\n",
" <td>0</td>\n",
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" <th>affiliate_provider</th>\n",
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" <th>language</th>\n",
" <td>0</td>\n",
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" <th>signup_flow</th>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>signup_method</th>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gender</th>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>timestamp_first_active</th>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>date_account_created</th>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>id</th>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Total Percent\n",
"date_first_booking 124543 0.583473\n",
"age 87990 0.412226\n",
"first_affiliate_tracked 6065 0.028414\n",
"country_destination 0 0.000000\n",
"first_browser 0 0.000000\n",
"first_device_type 0 0.000000\n",
"signup_app 0 0.000000\n",
"affiliate_provider 0 0.000000\n",
"affiliate_channel 0 0.000000\n",
"language 0 0.000000\n",
"signup_flow 0 0.000000\n",
"signup_method 0 0.000000\n",
"gender 0 0.000000\n",
"timestamp_first_active 0 0.000000\n",
"date_account_created 0 0.000000\n",
"id 0 0.000000"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#missing data\n",
"total = df_train.isnull().sum().sort_values(ascending=False)\n",
"percent = (df_train.isnull().sum()/df_train.isnull().count()).sort_values(ascending=False)\n",
"missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
"missing_data.head(20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Age**"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_uuid": "9b13c6b578128582eec0b9fdbb46fb42efc5e34f"
},
"outputs": [
{
"data": {
"text/plain": [
"count 125461.000000\n",
"mean 49.668335\n",
"std 155.666612\n",
"min 1.000000\n",
"25% 28.000000\n",
"50% 34.000000\n",
"75% 43.000000\n",
"max 2014.000000\n",
"Name: age, dtype: float64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#descriptive statistics summary\n",
"df_train['age'].describe()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_uuid": "4bb633155209b0cd3093d686961d404a39cf2a78"
},
"outputs": [],
"source": [
"df_train = df_train[df_train['age'] <= 100]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_uuid": "532a69756f7622f64a3a1563d99d01b6753bd3b0"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#histogram\n",
"sns.distplot(df_train['age'].dropna());"
]
},
{
"cell_type": "code",
"execution_count": 12,
"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>id</th>\n",
" <th>date_account_created</th>\n",
" <th>timestamp_first_active</th>\n",
" <th>date_first_booking</th>\n",
" <th>gender</th>\n",
" <th>age</th>\n",
" <th>signup_method</th>\n",
" <th>signup_flow</th>\n",
" <th>language</th>\n",
" <th>affiliate_channel</th>\n",
" <th>affiliate_provider</th>\n",
" <th>first_affiliate_tracked</th>\n",
" <th>signup_app</th>\n",
" <th>first_device_type</th>\n",
" <th>first_browser</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5uwns89zht</td>\n",
" <td>2014-07-01</td>\n",
" <td>20140701000006</td>\n",
" <td>NaN</td>\n",
" <td>FEMALE</td>\n",
" <td>35.0</td>\n",
" <td>facebook</td>\n",
" <td>0</td>\n",
" <td>en</td>\n",
" <td>direct</td>\n",
" <td>direct</td>\n",
" <td>untracked</td>\n",
" <td>Moweb</td>\n",
" <td>iPhone</td>\n",
" <td>Mobile Safari</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>jtl0dijy2j</td>\n",
" <td>2014-07-01</td>\n",
" <td>20140701000051</td>\n",
" <td>NaN</td>\n",
" <td>-unknown-</td>\n",
" <td>NaN</td>\n",
" <td>basic</td>\n",
" <td>0</td>\n",
" <td>en</td>\n",
" <td>direct</td>\n",
" <td>direct</td>\n",
" <td>untracked</td>\n",
" <td>Moweb</td>\n",
" <td>iPhone</td>\n",
" <td>Mobile Safari</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>xx0ulgorjt</td>\n",
" <td>2014-07-01</td>\n",
" <td>20140701000148</td>\n",
" <td>NaN</td>\n",
" <td>-unknown-</td>\n",
" <td>NaN</td>\n",
" <td>basic</td>\n",
" <td>0</td>\n",
" <td>en</td>\n",
" <td>direct</td>\n",
" <td>direct</td>\n",
" <td>linked</td>\n",
" <td>Web</td>\n",
" <td>Windows Desktop</td>\n",
" <td>Chrome</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>6c6puo6ix0</td>\n",
" <td>2014-07-01</td>\n",
" <td>20140701000215</td>\n",
" <td>NaN</td>\n",
" <td>-unknown-</td>\n",
" <td>NaN</td>\n",
" <td>basic</td>\n",
" <td>0</td>\n",
" <td>en</td>\n",
" <td>direct</td>\n",
" <td>direct</td>\n",
" <td>linked</td>\n",
" <td>Web</td>\n",
" <td>Windows Desktop</td>\n",
" <td>IE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>czqhjk3yfe</td>\n",
" <td>2014-07-01</td>\n",
" <td>20140701000305</td>\n",
" <td>NaN</td>\n",
" <td>-unknown-</td>\n",
" <td>NaN</td>\n",
" <td>basic</td>\n",
" <td>0</td>\n",
" <td>en</td>\n",
" <td>direct</td>\n",
" <td>direct</td>\n",
" <td>untracked</td>\n",
" <td>Web</td>\n",
" <td>Mac Desktop</td>\n",
" <td>Safari</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>szx28ujmhf</td>\n",
" <td>2014-07-01</td>\n",
" <td>20140701000336</td>\n",
" <td>NaN</td>\n",
" <td>FEMALE</td>\n",
" <td>28.0</td>\n",
" <td>basic</td>\n",
" <td>0</td>\n",
" <td>en</td>\n",
" <td>sem-brand</td>\n",
" <td>google</td>\n",
" <td>omg</td>\n",
" <td>Web</td>\n",
" <td>Windows Desktop</td>\n",
" <td>Chrome</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>guenkfjcbq</td>\n",
" <td>2014-07-01</td>\n",
" <td>20140701000514</td>\n",
" <td>NaN</td>\n",
" <td>MALE</td>\n",
" <td>48.0</td>\n",
" <td>basic</td>\n",
" <td>25</td>\n",
" <td>en</td>\n",
" <td>direct</td>\n",
" <td>direct</td>\n",
" <td>untracked</td>\n",
" <td>iOS</td>\n",
" <td>iPhone</td>\n",
" <td>-unknown-</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>tkpq0mlugk</td>\n",
" <td>2014-07-01</td>\n",
" <td>20140701000649</td>\n",
" <td>NaN</td>\n",
" <td>-unknown-</td>\n",
" <td>NaN</td>\n",
" <td>basic</td>\n",
" <td>0</td>\n",
" <td>en</td>\n",
" <td>direct</td>\n",
" <td>direct</td>\n",
" <td>untracked</td>\n",
" <td>Web</td>\n",
" <td>Mac Desktop</td>\n",
" <td>Chrome</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>3xtgd5p9dn</td>\n",
" <td>2014-07-01</td>\n",
" <td>20140701000837</td>\n",
" <td>NaN</td>\n",
" <td>-unknown-</td>\n",
" <td>NaN</td>\n",
" <td>basic</td>\n",
" <td>0</td>\n",
" <td>en</td>\n",
" <td>direct</td>\n",
" <td>direct</td>\n",
" <td>untracked</td>\n",
" <td>Web</td>\n",
" <td>Mac Desktop</td>\n",
" <td>Chrome</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>md9aj22l5a</td>\n",
" <td>2014-07-01</td>\n",
" <td>20140701002245</td>\n",
" <td>NaN</td>\n",
" <td>-unknown-</td>\n",
" <td>NaN</td>\n",
" <td>basic</td>\n",
" <td>0</td>\n",
" <td>en</td>\n",
" <td>sem-non-brand</td>\n",
" <td>google</td>\n",
" <td>omg</td>\n",
" <td>Web</td>\n",
" <td>Windows Desktop</td>\n",
" <td>Firefox</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id ... first_browser\n",
"0 5uwns89zht ... Mobile Safari\n",
"1 jtl0dijy2j ... Mobile Safari\n",
"2 xx0ulgorjt ... Chrome\n",
"3 6c6puo6ix0 ... IE\n",
"4 czqhjk3yfe ... Safari\n",
"5 szx28ujmhf ... Chrome\n",
"6 guenkfjcbq ... -unknown-\n",
"7 tkpq0mlugk ... Chrome\n",
"8 3xtgd5p9dn ... Chrome\n",
"9 md9aj22l5a ... Firefox\n",
"\n",
"[10 rows x 15 columns]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_test.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"df_train = df_train.dropna(subset=['age'])\n",
"df_test['age'] = df_test['age'].fillna(36.0)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"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>id</th>\n",
" <th>date_account_created</th>\n",
" <th>timestamp_first_active</th>\n",
" <th>date_first_booking</th>\n",
" <th>gender</th>\n",
" <th>age</th>\n",
" <th>signup_method</th>\n",
" <th>signup_flow</th>\n",
" <th>language</th>\n",
" <th>affiliate_channel</th>\n",
" <th>affiliate_provider</th>\n",
" <th>first_affiliate_tracked</th>\n",
" <th>signup_app</th>\n",
" <th>first_device_type</th>\n",
" <th>first_browser</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5uwns89zht</td>\n",
" <td>2014-07-01</td>\n",
" <td>20140701000006</td>\n",
" <td>NaN</td>\n",
" <td>FEMALE</td>\n",
" <td>35.0</td>\n",
" <td>facebook</td>\n",
" <td>0</td>\n",
" <td>en</td>\n",
" <td>direct</td>\n",
" <td>direct</td>\n",
" <td>untracked</td>\n",
" <td>Moweb</td>\n",
" <td>iPhone</td>\n",
" <td>Mobile Safari</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>jtl0dijy2j</td>\n",
" <td>2014-07-01</td>\n",
" <td>20140701000051</td>\n",
" <td>NaN</td>\n",
" <td>-unknown-</td>\n",
" <td>36.0</td>\n",
" <td>basic</td>\n",
" <td>0</td>\n",
" <td>en</td>\n",
" <td>direct</td>\n",
" <td>direct</td>\n",
" <td>untracked</td>\n",
" <td>Moweb</td>\n",
" <td>iPhone</td>\n",
" <td>Mobile Safari</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>xx0ulgorjt</td>\n",
" <td>2014-07-01</td>\n",
" <td>20140701000148</td>\n",
" <td>NaN</td>\n",
" <td>-unknown-</td>\n",
" <td>36.0</td>\n",
" <td>basic</td>\n",
" <td>0</td>\n",
" <td>en</td>\n",
" <td>direct</td>\n",
" <td>direct</td>\n",
" <td>linked</td>\n",
" <td>Web</td>\n",
" <td>Windows Desktop</td>\n",
" <td>Chrome</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>6c6puo6ix0</td>\n",
" <td>2014-07-01</td>\n",
" <td>20140701000215</td>\n",
" <td>NaN</td>\n",
" <td>-unknown-</td>\n",
" <td>36.0</td>\n",
" <td>basic</td>\n",
" <td>0</td>\n",
" <td>en</td>\n",
" <td>direct</td>\n",
" <td>direct</td>\n",
" <td>linked</td>\n",
" <td>Web</td>\n",
" <td>Windows Desktop</td>\n",
" <td>IE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>czqhjk3yfe</td>\n",
" <td>2014-07-01</td>\n",
" <td>20140701000305</td>\n",
" <td>NaN</td>\n",
" <td>-unknown-</td>\n",
" <td>36.0</td>\n",
" <td>basic</td>\n",
" <td>0</td>\n",
" <td>en</td>\n",
" <td>direct</td>\n",
" <td>direct</td>\n",
" <td>untracked</td>\n",
" <td>Web</td>\n",
" <td>Mac Desktop</td>\n",
" <td>Safari</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>szx28ujmhf</td>\n",
" <td>2014-07-01</td>\n",
" <td>20140701000336</td>\n",
" <td>NaN</td>\n",
" <td>FEMALE</td>\n",
" <td>28.0</td>\n",
" <td>basic</td>\n",
" <td>0</td>\n",
" <td>en</td>\n",
" <td>sem-brand</td>\n",
" <td>google</td>\n",
" <td>omg</td>\n",
" <td>Web</td>\n",
" <td>Windows Desktop</td>\n",
" <td>Chrome</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>guenkfjcbq</td>\n",
" <td>2014-07-01</td>\n",
" <td>20140701000514</td>\n",
" <td>NaN</td>\n",
" <td>MALE</td>\n",
" <td>48.0</td>\n",
" <td>basic</td>\n",
" <td>25</td>\n",
" <td>en</td>\n",
" <td>direct</td>\n",
" <td>direct</td>\n",
" <td>untracked</td>\n",
" <td>iOS</td>\n",
" <td>iPhone</td>\n",
" <td>-unknown-</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>tkpq0mlugk</td>\n",
" <td>2014-07-01</td>\n",
" <td>20140701000649</td>\n",
" <td>NaN</td>\n",
" <td>-unknown-</td>\n",
" <td>36.0</td>\n",
" <td>basic</td>\n",
" <td>0</td>\n",
" <td>en</td>\n",
" <td>direct</td>\n",
" <td>direct</td>\n",
" <td>untracked</td>\n",
" <td>Web</td>\n",
" <td>Mac Desktop</td>\n",
" <td>Chrome</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>3xtgd5p9dn</td>\n",
" <td>2014-07-01</td>\n",
" <td>20140701000837</td>\n",
" <td>NaN</td>\n",
" <td>-unknown-</td>\n",
" <td>36.0</td>\n",
" <td>basic</td>\n",
" <td>0</td>\n",
" <td>en</td>\n",
" <td>direct</td>\n",
" <td>direct</td>\n",
" <td>untracked</td>\n",
" <td>Web</td>\n",
" <td>Mac Desktop</td>\n",
" <td>Chrome</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>md9aj22l5a</td>\n",
" <td>2014-07-01</td>\n",
" <td>20140701002245</td>\n",
" <td>NaN</td>\n",
" <td>-unknown-</td>\n",
" <td>36.0</td>\n",
" <td>basic</td>\n",
" <td>0</td>\n",
" <td>en</td>\n",
" <td>sem-non-brand</td>\n",
" <td>google</td>\n",
" <td>omg</td>\n",
" <td>Web</td>\n",
" <td>Windows Desktop</td>\n",
" <td>Firefox</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id ... first_browser\n",
"0 5uwns89zht ... Mobile Safari\n",
"1 jtl0dijy2j ... Mobile Safari\n",
"2 xx0ulgorjt ... Chrome\n",
"3 6c6puo6ix0 ... IE\n",
"4 czqhjk3yfe ... Safari\n",
"5 szx28ujmhf ... Chrome\n",
"6 guenkfjcbq ... -unknown-\n",
"7 tkpq0mlugk ... Chrome\n",
"8 3xtgd5p9dn ... Chrome\n",
"9 md9aj22l5a ... Firefox\n",
"\n",
"[10 rows x 15 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_test.head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**first_affiliate_tracked** \n",
"\n",
"whats the first marketing the user interacted with before the signing up"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_uuid": "7b058337ff9b20e53ab64107fbf9a0bba262cbd0"
},
"outputs": [
{
"data": {
"text/plain": [
"array(['untracked', 'linked', 'omg', 'product', 'marketing',\n",
" 'tracked-other', nan, 'local ops'], dtype=object)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_train['first_affiliate_tracked'].unique()\n",
"df_test['first_affiliate_tracked'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"untracked 33949\n",
"linked 15777\n",
"omg 10877\n",
"product 797\n",
"tracked-other 499\n",
"marketing 142\n",
"local ops 35\n",
"Name: first_affiliate_tracked, dtype: int64"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_train['first_affiliate_tracked'].value_counts()\n",
"df_test['first_affiliate_tracked'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"df_train['first_affiliate_tracked'] = df_train['first_affiliate_tracked'].fillna('untracked')\n",
"df_test['first_affiliate_tracked'] = df_test['first_affiliate_tracked'].fillna('untracked')"
]
},
{
"cell_type": "code",
"execution_count": 18,
"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>Total</th>\n",
" <th>Percent</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>date_first_booking</th>\n",
" <td>55905</td>\n",
" <td>0.454084</td>\n",
" </tr>\n",
" <tr>\n",
" <th>country_destination</th>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>first_browser</th>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>first_device_type</th>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>signup_app</th>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>first_affiliate_tracked</th>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>affiliate_provider</th>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>affiliate_channel</th>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>language</th>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>signup_flow</th>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>signup_method</th>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>age</th>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gender</th>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>timestamp_first_active</th>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>date_account_created</th>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>id</th>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Total Percent\n",
"date_first_booking 55905 0.454084\n",
"country_destination 0 0.000000\n",
"first_browser 0 0.000000\n",
"first_device_type 0 0.000000\n",
"signup_app 0 0.000000\n",
"first_affiliate_tracked 0 0.000000\n",
"affiliate_provider 0 0.000000\n",
"affiliate_channel 0 0.000000\n",
"language 0 0.000000\n",
"signup_flow 0 0.000000\n",
"signup_method 0 0.000000\n",
"age 0 0.000000\n",
"gender 0 0.000000\n",
"timestamp_first_active 0 0.000000\n",
"date_account_created 0 0.000000\n",
"id 0 0.000000"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#missing data\n",
"total = df_train.isnull().sum().sort_values(ascending=False)\n",
"percent = (df_train.isnull().sum()/df_train.isnull().count()).sort_values(ascending=False)\n",
"missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
"missing_data.head(20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**date_first_booking**\n",
"\n",
"date of first booking"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"df_train = df_train.drop(['date_first_booking','id'], 1)\n",
"df_test = df_test.drop(['date_first_booking','id'], 1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2.2.Label Encoding\n",
"--------"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**country_destination**"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"_uuid": "9aff754c3ca9131df11f0f88b950da0c5b63764c"
},
"outputs": [],
"source": [
"\n",
"\n",
"# label_encoder.fit(df_train['country_destination'])\n",
"# print(label_encoder.classes_)\n",
"\n",
"# df_train['country_destination'] = label_encoder.transform(df_train['country_destination'])\n",
"# print(df_train.head(10))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Gender**"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"FEMALE 56765\n",
"MALE 49987\n",
"-unknown- 16139\n",
"OTHER 225\n",
"Name: gender, dtype: int64"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_train['gender'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"# df_train = df_train.drop(df_train[(df_train['gender'] == '-unknown-')|(df_train['gender'] == 'OTHER')].index)\n",
"# df_train['gender'].value_counts()\n",
"\n",
"# df_test = df_test.drop(df_test[(df_test['gender'] == '-unknown-')|(df_test['gender'] == 'OTHER')].index)\n",
"# df_test['gender'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['-unknown-' 'FEMALE' 'MALE' 'OTHER']\n"
]
}
],
"source": [
"# #Label Encoding\n",
"label_encoder = preprocessing.LabelEncoder() \n",
"\n",
"label_encoder.fit(df_train['gender'])\n",
"print(label_encoder.classes_)\n",
"\n",
"df_train['gender'] = label_encoder.transform(df_train['gender']) \n",
"df_train.head(10)\n",
"\n",
"df_test['gender'] = label_encoder.transform(df_test['gender']) "
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "cee4b59e0ee3d31650846905896dcc57a0adc7b0"
},
"source": [
"**signup_method**\n",
"\n",
"Test's data doesn't have a data class."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['basic' 'facebook' 'google']\n"
]
}
],
"source": [
"label_encoder.fit(df_train['signup_method'])\n",
"print(label_encoder.classes_)\n",
"\n",
"df_train['signup_method'] = label_encoder.transform(df_train['signup_method']) \n",
"df_train.head(10)\n",
"\n",
"df_test.loc[df_test.signup_method == 'weibo', 'signup_method'] = 'google'\n",
"df_test['signup_method'] = label_encoder.transform(df_test['signup_method']) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**language**"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['ca' 'cs' 'da' 'de' 'el' 'en' 'es' 'fi' 'fr' 'hr' 'hu' 'id' 'is' 'it'\n",
" 'ja' 'ko' 'nl' 'no' 'pl' 'pt' 'ru' 'sv' 'th' 'tr' 'zh']\n"
]
}
],
"source": [
"label_encoder.fit(df_train['language'])\n",
"print(label_encoder.classes_)\n",
"\n",
"df_train['language'] = label_encoder.transform(df_train['language']) \n",
"df_train.head(10)\n",
"\n",
"df_test.loc[df_test.language == '-unknown-', 'language'] = 'en'\n",
"df_test['language'] = label_encoder.transform(df_test['language']) \n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**affiliate_channel**"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['api' 'content' 'direct' 'other' 'remarketing' 'sem-brand'\n",
" 'sem-non-brand' 'seo']\n"
]
}
],
"source": [
"label_encoder.fit(df_train['affiliate_channel'])\n",
"print(label_encoder.classes_)\n",
"\n",
"df_train['affiliate_channel'] = label_encoder.transform(df_train['affiliate_channel']) \n",
"df_train.head(10)\n",
"\n",
"df_test['affiliate_channel'] = label_encoder.transform(df_test['affiliate_channel']) "
]
},
{
"cell_type": "markdown",
"metadata": {
"_uuid": "62e684f41012b40e5bacbc942741b7b29ee0101e"
},
"source": [
"**affiliate_provider**"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['baidu' 'bing' 'craigslist' 'direct' 'email-marketing' 'facebook'\n",
" 'facebook-open-graph' 'google' 'gsp' 'meetup' 'naver' 'other' 'padmapper'\n",
" 'vast' 'wayn' 'yahoo' 'yandex']\n"
]
}
],
"source": [
"label_encoder.fit(df_train['affiliate_provider'])\n",
"print(label_encoder.classes_)\n",
"\n",
"df_train['affiliate_provider'] = label_encoder.transform(df_train['affiliate_provider']) \n",
"df_train.head(10)\n",
"\n",
"df_test.loc[df_test.affiliate_provider == 'daum', 'affiliate_provider'] = 'other'\n",
"df_test['affiliate_provider'] = label_encoder.transform(df_test['affiliate_provider'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**first_affiliate_tracked**"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['linked' 'local ops' 'marketing' 'omg' 'product' 'tracked-other'\n",
" 'untracked']\n"
]
}
],
"source": [
"label_encoder.fit(df_train['first_affiliate_tracked'])\n",
"print(label_encoder.classes_)\n",
"\n",
"df_train['first_affiliate_tracked'] = label_encoder.transform(df_train['first_affiliate_tracked']) \n",
"df_train.head(10)\n",
"\n",
"df_test['first_affiliate_tracked'] = label_encoder.transform(df_test['first_affiliate_tracked']) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**signup_app**"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Android' 'Moweb' 'Web' 'iOS']\n"
]
}
],
"source": [
"label_encoder.fit(df_train['signup_app'])\n",
"print(label_encoder.classes_)\n",
"\n",
"df_train['signup_app'] = label_encoder.transform(df_train['signup_app']) \n",
"df_train.head(10)\n",
"\n",
"df_test['signup_app'] = label_encoder.transform(df_test['signup_app']) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**first_device_type**"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Android Phone' 'Android Tablet' 'Desktop (Other)' 'Mac Desktop'\n",
" 'Other/Unknown' 'SmartPhone (Other)' 'Windows Desktop' 'iPad' 'iPhone']\n"
]
}
],
"source": [
"label_encoder.fit(df_train['first_device_type'])\n",
"print(label_encoder.classes_)\n",
"\n",
"df_train['first_device_type'] = label_encoder.transform(df_train['first_device_type']) \n",
"df_train.head(10)\n",
"\n",
"df_test['first_device_type'] = label_encoder.transform(df_test['first_device_type']) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**first_browser**"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['-unknown-' 'AOL Explorer' 'Android Browser' 'Apple Mail' 'Avant Browser'\n",
" 'BlackBerry Browser' 'Camino' 'Chrome' 'Chrome Mobile' 'Chromium'\n",
" 'CometBird' 'Comodo Dragon' 'CoolNovo' 'Firefox' 'IE' 'IE Mobile'\n",
" 'IceWeasel' 'Iron' 'Kindle Browser' 'Maxthon' 'Mobile Firefox'\n",
" 'Mobile Safari' 'Mozilla' 'NetNewsWire' 'Opera' 'Opera Mini'\n",
" 'Opera Mobile' 'PS Vita browser' 'Pale Moon' 'RockMelt' 'Safari'\n",
" 'SeaMonkey' 'Silk' 'SiteKiosk' 'SlimBrowser' 'Sogou Explorer' 'Stainless'\n",
" 'TenFourFox' 'TheWorld Browser' 'Yandex.Browser' 'wOSBrowser']\n"
]
}
],
"source": [
"label_encoder.fit(df_train['first_browser'])\n",
"print(label_encoder.classes_)\n",
"\n",
"df_train['first_browser'] = label_encoder.transform(df_train['first_browser']) \n",
"df_train.head(10)\n",
"\n",
"df_test.loc[df_test.first_browser == 'wOSBrowser', 'first_browser'] = '-unknown-'\n",
"df_test.loc[df_test.first_browser == 'Mobile Safari', 'first_browser'] = '-unknown-'\n",
"df_test.loc[df_test.first_browser == 'UC Browser', 'first_browser'] = '-unknown-'\n",
"df_test.loc[df_test.first_browser == 'IBrowse', 'first_browser'] = '-unknown-'\n",
"df_test.loc[df_test.first_browser == 'Nintendo Browser', 'first_browser'] = '-unknown-'\n",
"\n",
"\n",
"df_test['first_browser'] = label_encoder.transform(df_test['first_browser']) "
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"df_train['date_account_created_year'] = pd.DatetimeIndex(df_train['date_account_created']).year\n",
"df_train['date_account_created_month'] = pd.DatetimeIndex(df_train['date_account_created']).month\n",
"\n",
"df_test['date_account_created_year'] = pd.DatetimeIndex(df_test['date_account_created']).year\n",
"df_test['date_account_created_month'] = pd.DatetimeIndex(df_test['date_account_created']).month"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"df_train = df_train.drop(['date_account_created','timestamp_first_active'], 1)\n",
"df_train.head(10)\n",
"\n",
"df_test = df_test.drop(['date_account_created','timestamp_first_active'], 1)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"gender int64\n",
"age float64\n",
"signup_method int64\n",
"signup_flow int64\n",
"language int64\n",
"affiliate_channel int64\n",
"affiliate_provider int64\n",
"first_affiliate_tracked int64\n",
"signup_app int64\n",
"first_device_type int64\n",
"first_browser int64\n",
"country_destination object\n",
"date_account_created_year int64\n",
"date_account_created_month int64\n",
"dtype: object\n"
]
}
],
"source": [
"print(df_train.dtypes)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"gender object\n",
"age float64\n",
"signup_method object\n",
"signup_flow object\n",
"language object\n",
"affiliate_channel object\n",
"affiliate_provider object\n",
"first_affiliate_tracked object\n",
"signup_app object\n",
"first_device_type object\n",
"first_browser object\n",
"country_destination object\n",
"date_account_created_year object\n",
"date_account_created_month object\n",
"dtype: object\n"
]
}
],
"source": [
"for col in ['gender',\n",
"'signup_method',\n",
"'signup_flow',\n",
"'language',\n",
"'affiliate_channel',\n",
"'affiliate_provider',\n",
"'first_affiliate_tracked',\n",
"'signup_app',\n",
"'first_device_type',\n",
"'first_browser',\n",
"'country_destination',\n",
"'date_account_created_year',\n",
"'date_account_created_month']:\n",
" df_train[col] = df_train[col].astype('str')\n",
"print(df_train.dtypes)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"feature = ['gender',\n",
" 'age',\n",
"'signup_method',\n",
"'signup_flow',\n",
"'language',\n",
"'affiliate_channel',\n",
"'affiliate_provider',\n",
"'first_affiliate_tracked',\n",
"'signup_app',\n",
"'first_device_type',\n",
"'first_browser',\n",
"'date_account_created_year',\n",
"'date_account_created_month']\n",
"target = 'country_destination'"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"df = df_train.copy()\n",
"\n",
"df_t = df_test.copy()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>gender</th>\n",
" <th>age</th>\n",
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" <th>6</th>\n",
" <td>2</td>\n",
" <td>48.0</td>\n",
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"</div>"
],
"text/plain": [
" gender ... date_account_created_month\n",
"0 1 ... 7\n",
"1 0 ... 7\n",
"2 0 ... 7\n",
"3 0 ... 7\n",
"4 0 ... 7\n",
"5 1 ... 7\n",
"6 2 ... 7\n",
"7 0 ... 7\n",
"8 0 ... 7\n",
"9 0 ... 7\n",
"\n",
"[10 rows x 13 columns]"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_test.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
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" <td>0</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>14</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>30</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1</td>\n",
" <td>28.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2</td>\n",
" <td>48.0</td>\n",
" <td>0</td>\n",
" <td>25</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>13</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" gender ... date_account_created_month\n",
"0 1 ... 7\n",
"1 0 ... 7\n",
"2 0 ... 7\n",
"3 0 ... 7\n",
"4 0 ... 7\n",
"5 1 ... 7\n",
"6 2 ... 7\n",
"7 0 ... 7\n",
"8 0 ... 7\n",
"9 0 ... 7\n",
"\n",
"[10 rows x 13 columns]"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_t.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"df_train, df_test = train_test_split(df, test_size=0.3, random_state=0)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"df_train_target = df_train['country_destination']\n",
"df_train = df_train.drop(['country_destination'],1)\n",
"df_test_target = df_test['country_destination']\n",
"df_test = df_test.drop(['country_destination'],1)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
" max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=None,\n",
" oob_score=True, random_state=123456, verbose=0,\n",
" warm_start=False)"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"\n",
"rf = RandomForestClassifier(n_estimators=100, oob_score=True, random_state=123456)\n",
"rf.fit(df_train, df_train_target)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Out-of-bag score estimate: 0.467\n",
"Mean accuracy score: 0.473\n"
]
}
],
"source": [
"from sklearn.metrics import accuracy_score\n",
"\n",
"predicted = rf.predict(df_test)\n",
"accuracy = accuracy_score(df_test_target, predicted)\n",
"\n",
"print(f'Out-of-bag score estimate: {rf.oob_score_:.3}')\n",
"print(f'Mean accuracy score: {accuracy:.3}')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Test**"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
" max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=None,\n",
" oob_score=True, random_state=123456, verbose=0,\n",
" warm_start=False)"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"\n",
"rf = RandomForestClassifier(n_estimators=100, oob_score=True, random_state=123456)\n",
"rf.fit(df_train, df_train_target)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"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>gender</th>\n",
" <th>age</th>\n",
" <th>signup_method</th>\n",
" <th>signup_flow</th>\n",
" <th>language</th>\n",
" <th>affiliate_channel</th>\n",
" <th>affiliate_provider</th>\n",
" <th>first_affiliate_tracked</th>\n",
" <th>signup_app</th>\n",
" <th>first_device_type</th>\n",
" <th>first_browser</th>\n",
" <th>date_account_created_year</th>\n",
" <th>date_account_created_month</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>14</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>30</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1</td>\n",
" <td>28.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2</td>\n",
" <td>48.0</td>\n",
" <td>0</td>\n",
" <td>25</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>13</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>13</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>1</td>\n",
" <td>30.0</td>\n",
" <td>0</td>\n",
" <td>25</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>2</td>\n",
" <td>24.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>30</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>1</td>\n",
" <td>56.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>0</td>\n",
" <td>30.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>1</td>\n",
" <td>33.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>14</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>2</td>\n",
" <td>31.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>1</td>\n",
" <td>53.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>30</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>13</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>2</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>13</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>2</td>\n",
" <td>34.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>1</td>\n",
" <td>25.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>14</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>0</td>\n",
" <td>27.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>1</td>\n",
" <td>28.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>1</td>\n",
" <td>42.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>2</td>\n",
" <td>52.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>13</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>1</td>\n",
" <td>51.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>30</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td>2</td>\n",
" <td>105.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>13</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>2</td>\n",
" <td>24.0</td>\n",
" <td>0</td>\n",
" <td>25</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>2</td>\n",
" <td>12</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>2</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>30</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>0</td>\n",
" <td>30.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>2</td>\n",
" <td>26.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>25</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>2</td>\n",
" <td>29.0</td>\n",
" <td>1</td>\n",
" <td>25</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>2</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>2</td>\n",
" <td>59.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>30</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92</th>\n",
" <td>1</td>\n",
" <td>32.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>30</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>94</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96</th>\n",
" <td>1</td>\n",
" <td>54.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>30</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td>0</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98</th>\n",
" <td>2</td>\n",
" <td>30.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99</th>\n",
" <td>0</td>\n",
" <td>27.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>2014</td>\n",
" <td>7</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>100 rows × 13 columns</p>\n",
"</div>"
],
"text/plain": [
" gender ... date_account_created_month\n",
"0 1 ... 7\n",
"1 0 ... 7\n",
"2 0 ... 7\n",
"3 0 ... 7\n",
"4 0 ... 7\n",
"5 1 ... 7\n",
"6 2 ... 7\n",
"7 0 ... 7\n",
"8 0 ... 7\n",
"9 0 ... 7\n",
"10 0 ... 7\n",
"11 1 ... 7\n",
"12 2 ... 7\n",
"13 0 ... 7\n",
"14 0 ... 7\n",
"15 1 ... 7\n",
"16 0 ... 7\n",
"17 0 ... 7\n",
"18 1 ... 7\n",
"19 2 ... 7\n",
"20 1 ... 7\n",
"21 0 ... 7\n",
"22 2 ... 7\n",
"23 0 ... 7\n",
"24 0 ... 7\n",
"25 2 ... 7\n",
"26 1 ... 7\n",
"27 0 ... 7\n",
"28 0 ... 7\n",
"29 0 ... 7\n",
".. ... ... ...\n",
"70 1 ... 7\n",
"71 0 ... 7\n",
"72 1 ... 7\n",
"73 0 ... 7\n",
"74 2 ... 7\n",
"75 1 ... 7\n",
"76 2 ... 7\n",
"77 2 ... 7\n",
"78 0 ... 7\n",
"79 2 ... 7\n",
"80 0 ... 7\n",
"81 0 ... 7\n",
"82 0 ... 7\n",
"83 2 ... 7\n",
"84 0 ... 7\n",
"85 0 ... 7\n",
"86 0 ... 7\n",
"87 2 ... 7\n",
"88 2 ... 7\n",
"89 2 ... 7\n",
"90 0 ... 7\n",
"91 0 ... 7\n",
"92 1 ... 7\n",
"93 0 ... 7\n",
"94 0 ... 7\n",
"95 0 ... 7\n",
"96 1 ... 7\n",
"97 0 ... 7\n",
"98 2 ... 7\n",
"99 0 ... 7\n",
"\n",
"[100 rows x 13 columns]"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_t.head(100)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(62096, 13)\n"
]
}
],
"source": [
"print(df_t.shape)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import accuracy_score\n",
"\n",
"predicted = rf.predict(df_t)\n",
"test = pd.DataFrame(columns=['id', 'country'])\n",
"test['id'] = test_users['id']\n",
"test['country'] = predicted\n",
"\n",
"test.to_csv('submission.csv')"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"#le.transform([1, 1, 2, 6]) "
]
}
],
"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": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| 0011/662/11662843.ipynb | s3://data-agents/kaggle-outputs/sharded/021_00011.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\(...TRUNCATED) | 0011/665/11665030.ipynb | s3://data-agents/kaggle-outputs/sharded/021_00011.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\(...TRUNCATED) | 0011/665/11665056.ipynb | s3://data-agents/kaggle-outputs/sharded/021_00011.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\(...TRUNCATED) | 0011/665/11665181.ipynb | s3://data-agents/kaggle-outputs/sharded/021_00011.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_uuid\": \"6a091d(...TRUNCATED) | 0011/665/11665207.ipynb | s3://data-agents/kaggle-outputs/sharded/021_00011.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"b(...TRUNCATED) | 0011/665/11665355.ipynb | s3://data-agents/kaggle-outputs/sharded/021_00011.jsonl.gz |
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"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\(...TRUNCATED) | 0011/666/11666213.ipynb | s3://data-agents/kaggle-outputs/sharded/021_00011.jsonl.gz |
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