Machine Learning - Hands on Lab - Session #1 - Data Visualisation.ipynb

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    "# Machine Learning - Hands on Lab - Session #1\n",
    "\n",
    "* **Lecturer:** Jonathan DEKHTIAR\n",
    "* **Date:** 2017-03-13\n",
    "<br/><br/>\n",
    "* **Contact:** [contact@jonathandekhtiar.eu](mailto:contact@jonathandekhtiar.eu)\n",
    "* **Twitter:** [@born2data](https://twitter.com/born2data)\n",
    "* **LinkedIn:** [JonathanDEKHTIAR](https://fr.linkedin.com/in/jonathandekhtiar)\n",
    "* **Personal Website:** [JonathanDEKHTIAR](http://www.jonathandekhtiar.eu)\n",
    "* **RSS Feed:** [FeedCrunch.io](https://www.feedcrunch.io/@dataradar/)\n",
    "* **Tech. Blog:** [born2data.com](http://www.born2data.com/)\n",
    "* **Github:** [DEKHTIARJonathan](https://github.com/DEKHTIARJonathan)\n",
    "<br/><br/>\n",
    "\n",
    "```\n",
    "*************************************************************************\n",
    "**\n",
    "** 2017 March 13\n",
    "**\n",
    "** In place of a legal notice, here is a blessing:\n",
    "**\n",
    "**    May you do good and not evil.\n",
    "**    May you find forgiveness for yourself and forgive others.\n",
    "**    May you share freely, never taking more than you give.\n",
    "**\n",
    "*************************************************************************\n",
    "```"
   ]
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   "source": [
    "# 1. Project Initialisation\n",
    "\n",
    "## 1.1 Importing Librairies"
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       "        <span id=\"94ae1d1f-bea4-440a-be9d-fa6822bf89f9\">Loading BokehJS ...</span>\n",
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       "    delete window._bokeh_onload_callbacks\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
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       "\n",
       "  function load_libs(js_urls, callback) {\n",
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       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
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       "      s.onerror = function() {\n",
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       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
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       "\n",
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       "\n",
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       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.css\");\n",
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       "\n",
       "  if (window._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
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       "  }\n",
       "}(this));"
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   "source": [
    "import os\n",
    "from datetime import datetime\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import bokeh.charts as bk\n",
    "import bokeh.plotting as bk_plt\n",
    "import bokeh.models as bk_md\n",
    "\n",
    "bk.output_notebook()"
   ]
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  {
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   "source": [
    "import urllib.request\n",
    " \n",
    "# Download the file from `url` and save it locally under `file_name`:\n",
    "zip_url = \"https://github.com/dataventureutc/Kaggle-HandsOnLab/blob/master/data/data.zip?raw=true\"\n",
    "urllib.request.urlretrieve(zip_url, \"data.zip\")\n",
    " \n",
    "import zipfile\n",
    " \n",
    "zip_ref = zipfile.ZipFile(\"data.zip\", 'r')\n",
    "zip_ref.extractall(\"data/\")\n",
    "zip_ref.close()"
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    "# 2. Dataset Loading & Visualition\n",
    "## 2.1 How does the expected \"output\" look like ?\n",
    "\n",
    "### Sample Submission File - sample_submission_NDF.csv\n",
    "\n",
    "We load the csv file and visualise it straight away"
   ]
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   "execution_count": 3,
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       "<div>\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>5uwns89zht</td>\n",
       "      <td>NDF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>jtl0dijy2j</td>\n",
       "      <td>NDF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>xx0ulgorjt</td>\n",
       "      <td>NDF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6c6puo6ix0</td>\n",
       "      <td>NDF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>czqhjk3yfe</td>\n",
       "      <td>NDF</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "           id country\n",
       "0  5uwns89zht     NDF\n",
       "1  jtl0dijy2j     NDF\n",
       "2  xx0ulgorjt     NDF\n",
       "3  6c6puo6ix0     NDF\n",
       "4  czqhjk3yfe     NDF"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "df_sample_submission = pd.read_csv(\"data/sample_submission_NDF.csv\")\n",
    "\n",
    "df_sample_submission.head(n=5) # Only display a few lines and not the whole dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "As we can see, it is a fairly simple file with only two columns:\n",
    "* **user identifier** \n",
    "* **country code** of destination\n",
    "\n",
    "=> This file represent **how we must format our data** for the delivery. \n",
    "\n",
    "There is *no data* in this file, it only is a **template**.\n",
    "\n",
    "## 2.2 Loading Data Files\n",
    "\n",
    "### 2.2.1 Test Users - test_users.csv\n",
    "\n",
    "We load the csv file and visualise it straight away"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
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     "data": {
      "text/html": [
       "<div>\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id date_account_created  timestamp_first_active  \\\n",
       "0  5uwns89zht           2014-07-01          20140701000006   \n",
       "1  jtl0dijy2j           2014-07-01          20140701000051   \n",
       "2  xx0ulgorjt           2014-07-01          20140701000148   \n",
       "3  6c6puo6ix0           2014-07-01          20140701000215   \n",
       "4  czqhjk3yfe           2014-07-01          20140701000305   \n",
       "\n",
       "   date_first_booking     gender   age signup_method  signup_flow language  \\\n",
       "0                 NaN     FEMALE  35.0      facebook            0       en   \n",
       "1                 NaN  -unknown-   NaN         basic            0       en   \n",
       "2                 NaN  -unknown-   NaN         basic            0       en   \n",
       "3                 NaN  -unknown-   NaN         basic            0       en   \n",
       "4                 NaN  -unknown-   NaN         basic            0       en   \n",
       "\n",
       "  affiliate_channel affiliate_provider first_affiliate_tracked signup_app  \\\n",
       "0            direct             direct               untracked      Moweb   \n",
       "1            direct             direct               untracked      Moweb   \n",
       "2            direct             direct                  linked        Web   \n",
       "3            direct             direct                  linked        Web   \n",
       "4            direct             direct               untracked        Web   \n",
       "\n",
       "  first_device_type  first_browser  \n",
       "0            iPhone  Mobile Safari  \n",
       "1            iPhone  Mobile Safari  \n",
       "2   Windows Desktop         Chrome  \n",
       "3   Windows Desktop             IE  \n",
       "4       Mac Desktop         Safari  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test_users = pd.read_csv(\"data/test_users.csv\")\n",
    "df_test_users.head(n=5) # Only display a few lines and not the whole dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Here are the information available on our **test users**:\n",
    "\n",
    "* **id**: user ID\n",
    "* **date_account_created**: date of account creation\n",
    "* **timestamp_first_active**: timestamp of the first activity, note that it can be earlier than date_account_created or date_first_booking because a user can search before signing up\n",
    "* **date_first_booking**: date of first booking\n",
    "* **gender**\n",
    "* **age**\n",
    "* **signup_method**: What method was used to signup (Facebook, basic, etc.)\n",
    "* **signup_flow**: the page a user came to signup up from\n",
    "* **language**: international language preference\n",
    "* **affiliate_channel**: what kind of paid marketing\n",
    "* **affiliate_provider**: where the marketing is e.g. google, craigslist, other\n",
    "* **first_affiliate_tracked**: whats the first marketing the user interacted with before the signing up\n",
    "* **signup_app**\n",
    "* **first_device_type**\n",
    "* **first_browser**\n",
    "\n",
    "### 1.2.2 countries.csv\n",
    "\n",
    "We load the csv file and visualise it straight away"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country_destination</th>\n",
       "      <th>lat_destination</th>\n",
       "      <th>lng_destination</th>\n",
       "      <th>distance_km</th>\n",
       "      <th>destination_km2</th>\n",
       "      <th>destination_language</th>\n",
       "      <th>language_levenshtein_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AU</td>\n",
       "      <td>-26.853388</td>\n",
       "      <td>133.275160</td>\n",
       "      <td>15297.7440</td>\n",
       "      <td>7741220.0</td>\n",
       "      <td>eng</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA</td>\n",
       "      <td>62.393303</td>\n",
       "      <td>-96.818146</td>\n",
       "      <td>2828.1333</td>\n",
       "      <td>9984670.0</td>\n",
       "      <td>eng</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DE</td>\n",
       "      <td>51.165707</td>\n",
       "      <td>10.452764</td>\n",
       "      <td>7879.5680</td>\n",
       "      <td>357022.0</td>\n",
       "      <td>deu</td>\n",
       "      <td>72.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ES</td>\n",
       "      <td>39.896027</td>\n",
       "      <td>-2.487694</td>\n",
       "      <td>7730.7240</td>\n",
       "      <td>505370.0</td>\n",
       "      <td>spa</td>\n",
       "      <td>92.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FR</td>\n",
       "      <td>46.232193</td>\n",
       "      <td>2.209667</td>\n",
       "      <td>7682.9450</td>\n",
       "      <td>643801.0</td>\n",
       "      <td>fra</td>\n",
       "      <td>92.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country_destination  lat_destination  lng_destination  distance_km  \\\n",
       "0                  AU       -26.853388       133.275160   15297.7440   \n",
       "1                  CA        62.393303       -96.818146    2828.1333   \n",
       "2                  DE        51.165707        10.452764    7879.5680   \n",
       "3                  ES        39.896027        -2.487694    7730.7240   \n",
       "4                  FR        46.232193         2.209667    7682.9450   \n",
       "\n",
       "   destination_km2 destination_language   language_levenshtein_distance  \n",
       "0        7741220.0                   eng                           0.00  \n",
       "1        9984670.0                   eng                           0.00  \n",
       "2         357022.0                   deu                          72.61  \n",
       "3         505370.0                   spa                          92.25  \n",
       "4         643801.0                   fra                          92.06  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_countries = pd.read_csv(\"data/countries.csv\")\n",
    "df_countries.head(n=5) # Only display a few lines and not the whole dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "We have a file containing a list of countries with the following features:\n",
    "\n",
    "* country code\n",
    "* latitude\n",
    "* longitude\n",
    "* distance in km\n",
    "* surface in km2\n",
    "* language\n",
    "* levenshtein distance with English Language\n",
    "\n",
    "=> **General information about countries of destination**\n",
    "\n",
    "### 1.2.3 age_gender_bkts.csv\n",
    "\n",
    "We load the csv file and visualise it straight away"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100+</td>\n",
       "      <td>AU</td>\n",
       "      <td>male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>95-99</td>\n",
       "      <td>AU</td>\n",
       "      <td>male</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90-94</td>\n",
       "      <td>AU</td>\n",
       "      <td>male</td>\n",
       "      <td>47.0</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>85-89</td>\n",
       "      <td>AU</td>\n",
       "      <td>male</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>80-84</td>\n",
       "      <td>AU</td>\n",
       "      <td>male</td>\n",
       "      <td>199.0</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</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"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_age_gender_bkts = pd.read_csv(\"data/age_gender_bkts.csv\")\n",
    "df_age_gender_bkts.head(n=5) # Only display a few lines and not the whole dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Displayed as followed, it is fairly hard to really see what is in this file.\n",
    "\n",
    "Let's try to sort the file in regards with the first column (\"age_bucket\") and then by the third one (\"gender\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0-4</td>\n",
       "      <td>AU</td>\n",
       "      <td>female</td>\n",
       "      <td>781.0</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0-4</td>\n",
       "      <td>CA</td>\n",
       "      <td>female</td>\n",
       "      <td>991.0</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>0-4</td>\n",
       "      <td>DE</td>\n",
       "      <td>female</td>\n",
       "      <td>1713.0</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>0-4</td>\n",
       "      <td>ES</td>\n",
       "      <td>female</td>\n",
       "      <td>1198.0</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>0-4</td>\n",
       "      <td>FR</td>\n",
       "      <td>female</td>\n",
       "      <td>1938.0</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>0-4</td>\n",
       "      <td>GB</td>\n",
       "      <td>female</td>\n",
       "      <td>1888.0</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>0-4</td>\n",
       "      <td>IT</td>\n",
       "      <td>female</td>\n",
       "      <td>1383.0</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>0-4</td>\n",
       "      <td>NL</td>\n",
       "      <td>female</td>\n",
       "      <td>438.0</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>0-4</td>\n",
       "      <td>PT</td>\n",
       "      <td>female</td>\n",
       "      <td>225.0</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>0-4</td>\n",
       "      <td>US</td>\n",
       "      <td>female</td>\n",
       "      <td>10306.0</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0-4</td>\n",
       "      <td>AU</td>\n",
       "      <td>male</td>\n",
       "      <td>824.0</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>0-4</td>\n",
       "      <td>CA</td>\n",
       "      <td>male</td>\n",
       "      <td>1045.0</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age_bucket country_destination  gender  population_in_thousands    year\n",
       "41         0-4                  AU  female                    781.0  2015.0\n",
       "80         0-4                  CA  female                    991.0  2015.0\n",
       "95         0-4                  DE  female                   1713.0  2015.0\n",
       "135        0-4                  ES  female                   1198.0  2015.0\n",
       "201        0-4                  FR  female                   1938.0  2015.0\n",
       "235        0-4                  GB  female                   1888.0  2015.0\n",
       "276        0-4                  IT  female                   1383.0  2015.0\n",
       "324        0-4                  NL  female                    438.0  2015.0\n",
       "363        0-4                  PT  female                    225.0  2015.0\n",
       "408        0-4                  US  female                  10306.0  2015.0\n",
       "20         0-4                  AU    male                    824.0  2015.0\n",
       "60         0-4                  CA    male                   1045.0  2015.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_sorted = df_age_gender_bkts.sort_values(by=['age_bucket', 'gender'])\n",
    "tmp_sorted.head(n=12) # Only display a few lines and not the whole dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now it appears that we have a file containing demographic information by country, by age and by gender.\n",
    "\n",
    "For each \"age-bucket\", \"country\" and gender, we have the following information:\n",
    "* Population in thousands\n",
    "* year of the statistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2015.])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_sorted.year.unique() # We query the distinct values over the last column \"year\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "=> We can conclude that we only have data about the year 2015 !\n",
    "\n",
    "### 1.2.4 train_users_2.csv\n",
    "\n",
    "We load the csv file and visualise it straight away"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id date_account_created  timestamp_first_active date_first_booking  \\\n",
       "0  gxn3p5htnn           2010-06-28          20090319043255                NaN   \n",
       "1  820tgsjxq7           2011-05-25          20090523174809                NaN   \n",
       "2  4ft3gnwmtx           2010-09-28          20090609231247         2010-08-02   \n",
       "3  bjjt8pjhuk           2011-12-05          20091031060129         2012-09-08   \n",
       "4  87mebub9p4           2010-09-14          20091208061105         2010-02-18   \n",
       "\n",
       "      gender   age signup_method  signup_flow language affiliate_channel  \\\n",
       "0  -unknown-   NaN      facebook            0       en            direct   \n",
       "1       MALE  38.0      facebook            0       en               seo   \n",
       "2     FEMALE  56.0         basic            3       en            direct   \n",
       "3     FEMALE  42.0      facebook            0       en            direct   \n",
       "4  -unknown-  41.0         basic            0       en            direct   \n",
       "\n",
       "  affiliate_provider first_affiliate_tracked signup_app first_device_type  \\\n",
       "0             direct               untracked        Web       Mac Desktop   \n",
       "1             google               untracked        Web       Mac Desktop   \n",
       "2             direct               untracked        Web   Windows Desktop   \n",
       "3             direct               untracked        Web       Mac Desktop   \n",
       "4             direct               untracked        Web       Mac Desktop   \n",
       "\n",
       "  first_browser country_destination  \n",
       "0        Chrome                 NDF  \n",
       "1        Chrome                 NDF  \n",
       "2            IE                  US  \n",
       "3       Firefox               other  \n",
       "4        Chrome                  US  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_users = pd.read_csv(\"data/train_users_2.csv\")\n",
    "df_train_users.head(n=5) # Only display a few lines and not the whole dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Here are the information available on our **test users**:\n",
    "\n",
    "* **id**: user ID\n",
    "* **date_account_created**: date of account creation\n",
    "* **timestamp_first_active**: timestamp of the first activity, note that it can be earlier than date_account_created or date_first_booking because a user can search before signing up\n",
    "* **date_first_booking**: date of first booking\n",
    "* **gender**\n",
    "* **age**\n",
    "* **signup_method**: What method was used to signup (Facebook, basic, etc.)\n",
    "* **signup_flow**: the page a user came to signup up from\n",
    "* **language**: international language preference\n",
    "* **affiliate_channel**: what kind of paid marketing\n",
    "* **affiliate_provider**: where the marketing is e.g. google, craigslist, other\n",
    "* **first_affiliate_tracked**: whats the first marketing the user interacted with before the signing up\n",
    "* **signup_app**\n",
    "* **first_device_type**\n",
    "* **first_browser**\n",
    "* <span style=\"color:red\">**country_destination**: this is the **target variable** that we want to predict </span>\n",
    "\n",
    "**Remark on country_destination => ** 12 Possibilities: **US**, **FR**, **CA**, **GB**, **ES**, **IT**, **PT**, **NL**,**DE**, **AU**, **NDF** (no destination found), and **other**\n",
    "\n",
    "### 1.2.5 session.csv\n",
    "\n",
    "We load the csv file and visualise it straight away"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 10567737 rows in the dataset\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>action</th>\n",
       "      <th>action_type</th>\n",
       "      <th>action_detail</th>\n",
       "      <th>device_type</th>\n",
       "      <th>secs_elapsed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>d1mm9tcy42</td>\n",
       "      <td>lookup</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Windows Desktop</td>\n",
       "      <td>319.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>d1mm9tcy42</td>\n",
       "      <td>search_results</td>\n",
       "      <td>click</td>\n",
       "      <td>view_search_results</td>\n",
       "      <td>Windows Desktop</td>\n",
       "      <td>67753.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>d1mm9tcy42</td>\n",
       "      <td>lookup</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Windows Desktop</td>\n",
       "      <td>301.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d1mm9tcy42</td>\n",
       "      <td>search_results</td>\n",
       "      <td>click</td>\n",
       "      <td>view_search_results</td>\n",
       "      <td>Windows Desktop</td>\n",
       "      <td>22141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>d1mm9tcy42</td>\n",
       "      <td>lookup</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Windows Desktop</td>\n",
       "      <td>435.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id          action action_type        action_detail  \\\n",
       "0  d1mm9tcy42          lookup         NaN                  NaN   \n",
       "1  d1mm9tcy42  search_results       click  view_search_results   \n",
       "2  d1mm9tcy42          lookup         NaN                  NaN   \n",
       "3  d1mm9tcy42  search_results       click  view_search_results   \n",
       "4  d1mm9tcy42          lookup         NaN                  NaN   \n",
       "\n",
       "       device_type  secs_elapsed  \n",
       "0  Windows Desktop         319.0  \n",
       "1  Windows Desktop       67753.0  \n",
       "2  Windows Desktop         301.0  \n",
       "3  Windows Desktop       22141.0  \n",
       "4  Windows Desktop         435.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sessions = pd.read_csv(\"data/sessions.csv\")\n",
    "\n",
    "print(\"There are \" + str(df_sessions.shape[0])+ \" rows in the dataset\")\n",
    "\n",
    "df_sessions.head(n=5) # Only display a few lines and not the whole dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 1.3 Summary - Important Information\n",
    "\n",
    "### 1.3.1 Train - Test Split Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Date Range : [2010-01-01, 2014-06-30]\n",
      "Testing Date Range : [2014-07-01, 2014-09-30]\n"
     ]
    }
   ],
   "source": [
    "training_min_date = df_train_users.date_account_created.min()\n",
    "training_max_date = df_train_users.date_account_created.max()\n",
    "\n",
    "print (\"Training Date Range : [\" + training_min_date +\", \" + training_max_date +\"]\")\n",
    "\n",
    "# ===============\n",
    "\n",
    "testing_min_date = df_test_users.date_account_created.min()\n",
    "testing_max_date = df_test_users.date_account_created.max()\n",
    "\n",
    "print (\"Testing Date Range : [\" + testing_min_date +\", \" + testing_max_date +\"]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 1.3.2 - Summary\n",
    "\n",
    "- All the users in this dataset are from the USA (written on Kaggle)\n",
    "- 12 Possibilities for the country of destination\n",
    "- Data are split by data : \n",
    "    - _Train data_: [2010, June 2014] \n",
    "    - _Test data_: [July 2014, September 2014]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# 1.4 Train Data Analysis\n",
    "\n",
    "Let us focus on training data. \n",
    "\n",
    "We transpose the df for a better reading and stop horinzontally scrolling.\n",
    "We take the 6 first examples in order to have a sense of what's happening."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>gxn3p5htnn</td>\n",
       "      <td>820tgsjxq7</td>\n",
       "      <td>4ft3gnwmtx</td>\n",
       "      <td>bjjt8pjhuk</td>\n",
       "      <td>87mebub9p4</td>\n",
       "      <td>osr2jwljor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_account_created</th>\n",
       "      <td>2010-06-28</td>\n",
       "      <td>2011-05-25</td>\n",
       "      <td>2010-09-28</td>\n",
       "      <td>2011-12-05</td>\n",
       "      <td>2010-09-14</td>\n",
       "      <td>2010-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp_first_active</th>\n",
       "      <td>20090319043255</td>\n",
       "      <td>20090523174809</td>\n",
       "      <td>20090609231247</td>\n",
       "      <td>20091031060129</td>\n",
       "      <td>20091208061105</td>\n",
       "      <td>20100101215619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_first_booking</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010-08-02</td>\n",
       "      <td>2012-09-08</td>\n",
       "      <td>2010-02-18</td>\n",
       "      <td>2010-01-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>-unknown-</td>\n",
       "      <td>MALE</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>-unknown-</td>\n",
       "      <td>-unknown-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>NaN</td>\n",
       "      <td>38</td>\n",
       "      <td>56</td>\n",
       "      <td>42</td>\n",
       "      <td>41</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>signup_method</th>\n",
       "      <td>facebook</td>\n",
       "      <td>facebook</td>\n",
       "      <td>basic</td>\n",
       "      <td>facebook</td>\n",
       "      <td>basic</td>\n",
       "      <td>basic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>signup_flow</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>language</th>\n",
       "      <td>en</td>\n",
       "      <td>en</td>\n",
       "      <td>en</td>\n",
       "      <td>en</td>\n",
       "      <td>en</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>affiliate_channel</th>\n",
       "      <td>direct</td>\n",
       "      <td>seo</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>affiliate_provider</th>\n",
       "      <td>direct</td>\n",
       "      <td>google</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>direct</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_affiliate_tracked</th>\n",
       "      <td>untracked</td>\n",
       "      <td>untracked</td>\n",
       "      <td>untracked</td>\n",
       "      <td>untracked</td>\n",
       "      <td>untracked</td>\n",
       "      <td>omg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>signup_app</th>\n",
       "      <td>Web</td>\n",
       "      <td>Web</td>\n",
       "      <td>Web</td>\n",
       "      <td>Web</td>\n",
       "      <td>Web</td>\n",
       "      <td>Web</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_device_type</th>\n",
       "      <td>Mac Desktop</td>\n",
       "      <td>Mac Desktop</td>\n",
       "      <td>Windows Desktop</td>\n",
       "      <td>Mac Desktop</td>\n",
       "      <td>Mac Desktop</td>\n",
       "      <td>Mac Desktop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_browser</th>\n",
       "      <td>Chrome</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>IE</td>\n",
       "      <td>Firefox</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>Chrome</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country_destination</th>\n",
       "      <td>NDF</td>\n",
       "      <td>NDF</td>\n",
       "      <td>US</td>\n",
       "      <td>other</td>\n",
       "      <td>US</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      0               1                2  \\\n",
       "id                           gxn3p5htnn      820tgsjxq7       4ft3gnwmtx   \n",
       "date_account_created         2010-06-28      2011-05-25       2010-09-28   \n",
       "timestamp_first_active   20090319043255  20090523174809   20090609231247   \n",
       "date_first_booking                  NaN             NaN       2010-08-02   \n",
       "gender                        -unknown-            MALE           FEMALE   \n",
       "age                                 NaN              38               56   \n",
       "signup_method                  facebook        facebook            basic   \n",
       "signup_flow                           0               0                3   \n",
       "language                             en              en               en   \n",
       "affiliate_channel                direct             seo           direct   \n",
       "affiliate_provider               direct          google           direct   \n",
       "first_affiliate_tracked       untracked       untracked        untracked   \n",
       "signup_app                          Web             Web              Web   \n",
       "first_device_type           Mac Desktop     Mac Desktop  Windows Desktop   \n",
       "first_browser                    Chrome          Chrome               IE   \n",
       "country_destination                 NDF             NDF               US   \n",
       "\n",
       "                                      3               4               5  \n",
       "id                           bjjt8pjhuk      87mebub9p4      osr2jwljor  \n",
       "date_account_created         2011-12-05      2010-09-14      2010-01-01  \n",
       "timestamp_first_active   20091031060129  20091208061105  20100101215619  \n",
       "date_first_booking           2012-09-08      2010-02-18      2010-01-02  \n",
       "gender                           FEMALE       -unknown-       -unknown-  \n",
       "age                                  42              41             NaN  \n",
       "signup_method                  facebook           basic           basic  \n",
       "signup_flow                           0               0               0  \n",
       "language                             en              en              en  \n",
       "affiliate_channel                direct          direct           other  \n",
       "affiliate_provider               direct          direct           other  \n",
       "first_affiliate_tracked       untracked       untracked             omg  \n",
       "signup_app                          Web             Web             Web  \n",
       "first_device_type           Mac Desktop     Mac Desktop     Mac Desktop  \n",
       "first_browser                   Firefox          Chrome          Chrome  \n",
       "country_destination               other              US              US  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_users.transpose().ix[:,:5] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 1.4.1 Feature Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Row Count = 213451\n",
      "\n",
      "\n",
      "ID Length = 10\n",
      "\n",
      "\n",
      "date_first_booking NaN Count = 124543 && 58.35%\n",
      "\n",
      "\n",
      "Gender: -unknown- && Count: 95688 && 44.83%\n",
      "Gender: FEMALE && Count: 63041 && 29.53%\n",
      "Gender: MALE && Count: 54440 && 25.50%\n",
      "Gender: OTHER && Count: 282 && 0.13%\n",
      "\n",
      "\n",
      "age NaN Count = 87990 && 41.22%\n",
      "\n",
      "\n",
      "Method: basic && Count: 152897 && 71.63%\n",
      "Method: facebook && Count: 60008 && 28.11%\n",
      "Method: google && Count: 546 && 0.26%\n",
      "\n",
      "\n",
      "language: en && Count: 206314 && 96.66%\n",
      "\n",
      "\n",
      "affiliate_channel: direct && Count: 137727 && 64.52%\n",
      "affiliate_channel: sem-brand && Count: 26045 && 12.20%\n",
      "affiliate_channel: sem-non-brand && Count: 18844 && 8.83%\n",
      "\n",
      "\n",
      "first_affiliate_tracked: untracked && Count: 109232 && 51.17%\n",
      "first_affiliate_tracked: linked && Count: 46287 && 21.69%\n",
      "first_affiliate_tracked: omg && Count: 43982 && 20.61%\n",
      "\n",
      "\n",
      "signup_app: Web && Count: 182717 && 85.60%\n",
      "signup_app: iOS && Count: 19019 && 8.91%\n",
      "signup_app: Moweb && Count: 6261 && 2.93%\n",
      "signup_app: Android && Count: 5454 && 2.56%\n",
      "\n",
      "\n",
      "first_browser: Chrome && Count: 63845 && 29.91%\n",
      "first_browser: Safari && Count: 45169 && 21.16%\n",
      "first_browser: Firefox && Count: 33655 && 15.77%\n",
      "first_browser: -unknown- && Count: 27266 && 12.77%\n",
      "first_browser: IE && Count: 21068 && 9.87%\n",
      "first_browser: Mobile Safari && Count: 19274 && 9.03%\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# How many rows in the DataFrame \n",
    "\n",
    "row_count = len(df_train_users.index)\n",
    "print (\"Row Count = \" + str(row_count))\n",
    "print(\"\\n\")\n",
    "# ===============================\n",
    "\n",
    "# Print what's the size of the ID\n",
    "field_length = df_train_users.id.astype(str).map(len)\n",
    "\n",
    "id_maxlength = len(df_train_users.loc[field_length.argmax(), 'id'])\n",
    "id_minlength = len(df_train_users.loc[field_length.argmin(), 'id'])\n",
    "\n",
    "if (id_maxlength != id_minlength):\n",
    "    print (\"ID Length = [\" + str(id_minlength) + \", \" + str(id_maxlength) + \"]\")\n",
    "else:\n",
    "    print (\"ID Length = \" + str(id_maxlength))\n",
    "\n",
    "print(\"\\n\")\n",
    "\n",
    "# ===============================\n",
    "\n",
    "# Count NaN Values for date_first_booking\n",
    "NaN_Count_date_first_booking = df_train_users.date_first_booking.isnull().sum()\n",
    "print (\"date_first_booking NaN Count = \" + str(NaN_Count_date_first_booking) \\\n",
    "+ \" && \" +  str(\"%.2f\" % (float(NaN_Count_date_first_booking)/row_count*100)) +\"%\")\n",
    "\n",
    "print(\"\\n\")\n",
    "# ===============================\n",
    "\n",
    "# Possible Values for gender\n",
    "\n",
    "gender_repartition = df_train_users.gender.value_counts()\n",
    "for gender, count in gender_repartition.iteritems():\n",
    "    print (\"Gender: \" + gender + \" && Count: \" + str(count) + \" && \" \\\n",
    "           + str(\"%.2f\" % (float(count)/row_count*100)) +\"%\")\n",
    "\n",
    "print(\"\\n\")\n",
    "# ===============================\n",
    "\n",
    "# Count NaN Values for age\n",
    "NaN_Count_age = df_train_users.age.isnull().sum()\n",
    "print (\"age NaN Count = \" + str(NaN_Count_age)  + \" && \"  \\\n",
    "       +  str(\"%.2f\" % (float(NaN_Count_age)/row_count*100)) +\"%\")\n",
    "\n",
    "print(\"\\n\")\n",
    "# ===============================\n",
    "\n",
    "# Possible Values for signup_method\n",
    "\n",
    "signup_method_repartition = df_train_users.signup_method.value_counts()\n",
    "for method, count in signup_method_repartition.iteritems():\n",
    "    print (\"Method: \" + method + \" && Count: \" + str(count) + \" && \" \\\n",
    "           + str(\"%.2f\" % (float(count)/row_count*100)) +\"%\")\n",
    "    \n",
    "print(\"\\n\")\n",
    "# ===============================\n",
    "\n",
    "# Possible Values for language\n",
    "\n",
    "language_repartition = df_train_users.language.value_counts()\n",
    "for language, count in language_repartition.iteritems():\n",
    "    print (\"language: \" + language + \" && Count: \" + str(count)  \\\n",
    "           + \" && \" + str(\"%.2f\" % (float(count)/row_count*100)) +\"%\")\n",
    "    break\n",
    "    \n",
    "print(\"\\n\")\n",
    "\n",
    "# ===============================\n",
    "\n",
    "# Possible Values for affiliate_channel\n",
    "\n",
    "affiliate_channel_repartition = df_train_users.affiliate_channel.value_counts()\n",
    "i = 0\n",
    "for channel, count in affiliate_channel_repartition.iteritems():\n",
    "    print (\"affiliate_channel: \" + channel + \" && Count: \" + str(count) + \" && \" \\\n",
    "           + str(\"%.2f\" % (float(count)/row_count*100)) +\"%\")\n",
    "    i += 1\n",
    "    if (i == 3):\n",
    "        break\n",
    "print(\"\\n\")\n",
    "\n",
    "# ===============================\n",
    "\n",
    "# Possible Values for first_affiliate_tracked\n",
    "\n",
    "first_affiliate_tracked_repartition = df_train_users.first_affiliate_tracked.value_counts()\n",
    "i = 0\n",
    "for affiliate, count in first_affiliate_tracked_repartition.iteritems():\n",
    "    print (\"first_affiliate_tracked: \" + affiliate + \" && Count: \" + str(count) + \\\n",
    "    \" && \" + str(\"%.2f\" % (float(count)/row_count*100)) +\"%\")\n",
    "    \n",
    "    i += 1\n",
    "    if (i == 3):\n",
    "        break\n",
    "print(\"\\n\")\n",
    "\n",
    "# ===============================\n",
    "\n",
    "# Possible Values for signup_app\n",
    "\n",
    "signup_app_repartition = df_train_users.signup_app.value_counts()\n",
    "for signup_app, count in signup_app_repartition.iteritems():\n",
    "    print (\"signup_app: \" + signup_app + \" && Count: \" + str(count) + \" && \" \\\n",
    "           + str(\"%.2f\" % (float(count)/row_count*100)) +\"%\")\n",
    "print(\"\\n\")\n",
    "\n",
    "# ===============================\n",
    "\n",
    "# Possible Values for first_browser\n",
    "\n",
    "first_browser_repartition = df_train_users.first_browser.value_counts()\n",
    "i = 0\n",
    "for first_browser, count in first_browser_repartition.iteritems():\n",
    "    print (\"first_browser: \" + first_browser + \" && Count: \" + str(count) + \\\n",
    "    \" && \" + str(\"%.2f\" % (float(count)/row_count*100)) +\"%\")\n",
    "    \n",
    "    i += 1\n",
    "    if (i == 6):\n",
    "        break\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 1.4.2 Output Analysis - Is the dataset balanced ?\n",
    "\n",
    "### 1.4.2.1 Dummy Analysis\n",
    "\n",
    "Let's plot the the bookings distribution by destination in order to see whether the dataset is balanced or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Repartition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NDF</td>\n",
       "      <td>58.347349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US</td>\n",
       "      <td>29.222632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>other</td>\n",
       "      <td>4.728954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FR</td>\n",
       "      <td>2.353233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>IT</td>\n",
       "      <td>1.328174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country  Repartition\n",
       "2      NDF    58.347349\n",
       "3       US    29.222632\n",
       "4    other     4.728954\n",
       "5       FR     2.353233\n",
       "10      IT     1.328174"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_output = dict()\n",
    "\n",
    "country_destination_repartition = df_train_users.country_destination.value_counts()\n",
    "\n",
    "for country_destination, count in country_destination_repartition.iteritems():\n",
    "    dict_output[country_destination] = float(count)/row_count*100\n",
    "\n",
    "df_output = pd.DataFrame(list(dict_output.items()),\n",
    "                      columns=['Country', 'Repartition'])\n",
    "\n",
    "df_output.sort_values(by=['Repartition'], ascending=False).head(n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
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       "            var render_items = [{\"docid\":\"c9bf2488-8db6-490c-83cd-325b3fe58055\",\"elementid\":\"7a482493-5721-4fdf-9a65-ab7a2867f7b6\",\"modelid\":\"e69c90ed-abea-4485-a56c-3a911a5a4621\",\"notebook_comms_target\":\"44f2fe53-1bad-4f84-a279-ac405d22a791\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "        });\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\n",
       "      }\n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<p><code>&lt;Bokeh Notebook handle for <strong>In[15]</strong>&gt;</code></p>"
      ],
      "text/plain": [
       "<bokeh.io._CommsHandle at 0x7fa2b4cee588>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = bk.Bar(df_output, label=['Country'], values='Repartition', ylabel='Booking Proportion in %')\n",
    "\n",
    "bk.show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 1.4.2.2 Same visualisation, with data grouped by Booking-year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country_destination</th>\n",
       "      <th>date_account_created</th>\n",
       "      <th>year_account_created</th>\n",
       "      <th>month_account_created</th>\n",
       "      <th>year_month_account_created</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NDF</td>\n",
       "      <td>2010-06-28</td>\n",
       "      <td>2010</td>\n",
       "      <td>06</td>\n",
       "      <td>2010 - 06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NDF</td>\n",
       "      <td>2011-05-25</td>\n",
       "      <td>2011</td>\n",
       "      <td>05</td>\n",
       "      <td>2011 - 05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US</td>\n",
       "      <td>2010-09-28</td>\n",
       "      <td>2010</td>\n",
       "      <td>09</td>\n",
       "      <td>2010 - 09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country_destination date_account_created year_account_created  \\\n",
       "0                 NDF           2010-06-28                 2010   \n",
       "1                 NDF           2011-05-25                 2011   \n",
       "2                  US           2010-09-28                 2010   \n",
       "\n",
       "  month_account_created year_month_account_created  \n",
       "0                    06                  2010 - 06  \n",
       "1                    05                  2011 - 05  \n",
       "2                    09                  2010 - 09  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_output_by_year_and_month = df_train_users.loc[:,['country_destination','date_account_created']]\n",
    "\n",
    "df_output_by_year_and_month.loc[:,'year_account_created'] = df_output_by_year_and_month['date_account_created'].apply(\n",
    "    lambda x: datetime.strptime(x, \"%Y-%m-%d\").strftime(\"%Y\")\n",
    ")\n",
    "\n",
    "df_output_by_year_and_month.loc[:,'month_account_created'] = df_output_by_year_and_month['date_account_created'].apply(\n",
    "    lambda x: datetime.strptime(x, \"%Y-%m-%d\").strftime(\"%m\")\n",
    ")\n",
    "\n",
    "df_output_by_year_and_month.loc[:,'year_month_account_created'] = df_output_by_year_and_month['date_account_created'].apply(\n",
    "    lambda x: datetime.strptime(x, \"%Y-%m-%d\").strftime(\"%Y - %m\")\n",
    ")\n",
    "\n",
    "df_output_by_year_and_month.head(n=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7</td>\n",
       "      <td>AU</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>38</td>\n",
       "      <td>AU</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>103</td>\n",
       "      <td>AU</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>239</td>\n",
       "      <td>AU</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>152</td>\n",
       "      <td>AU</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Count Destination  Year\n",
       "10      7          AU  2010\n",
       "22     38          AU  2011\n",
       "34    103          AU  2012\n",
       "46    239          AU  2013\n",
       "58    152          AU  2014"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_df = df_output_by_year_and_month.loc[:,['country_destination','year_account_created']]\n",
    "rslt = tmp_df.groupby(\"year_account_created\")['country_destination'].value_counts()\n",
    "\n",
    "list_year = [x[0] for x in rslt.index.values]\n",
    "list_dest = [x[1] for x in rslt.index.values]\n",
    "list_count = rslt.values\n",
    "\n",
    "df_tmp = pd.DataFrame(\n",
    "    {'Year': list_year,\n",
    "     'Destination': list_dest,\n",
    "     'Count': list_count\n",
    "    }\n",
    ")\n",
    "\n",
    "df_tmp.sort_values(by=[\"Destination\", \"Year\"]).head(n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
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    "deletable": true,
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    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Year</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Destination</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NDF</th>\n",
       "      <td>42.503587</td>\n",
       "      <td>45.367304</td>\n",
       "      <td>55.022553</td>\n",
       "      <td>59.156220</td>\n",
       "      <td>61.762090</td>\n",
       "      <td>58.347349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>44.045911</td>\n",
       "      <td>38.097665</td>\n",
       "      <td>31.131215</td>\n",
       "      <td>28.854870</td>\n",
       "      <td>26.729527</td>\n",
       "      <td>29.222632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>2.761836</td>\n",
       "      <td>4.738854</td>\n",
       "      <td>4.852770</td>\n",
       "      <td>4.634764</td>\n",
       "      <td>4.837444</td>\n",
       "      <td>4.728954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FR</th>\n",
       "      <td>4.304161</td>\n",
       "      <td>3.983015</td>\n",
       "      <td>2.840708</td>\n",
       "      <td>2.232401</td>\n",
       "      <td>1.910653</td>\n",
       "      <td>2.353233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>1.076040</td>\n",
       "      <td>1.690021</td>\n",
       "      <td>1.459632</td>\n",
       "      <td>1.248795</td>\n",
       "      <td>1.299924</td>\n",
       "      <td>1.328174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GB</th>\n",
       "      <td>1.004304</td>\n",
       "      <td>1.469214</td>\n",
       "      <td>1.310121</td>\n",
       "      <td>1.042671</td>\n",
       "      <td>0.969058</td>\n",
       "      <td>1.088774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES</th>\n",
       "      <td>1.542324</td>\n",
       "      <td>1.673036</td>\n",
       "      <td>1.191019</td>\n",
       "      <td>0.994455</td>\n",
       "      <td>0.933748</td>\n",
       "      <td>1.053638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <td>1.506456</td>\n",
       "      <td>1.061571</td>\n",
       "      <td>0.737418</td>\n",
       "      <td>0.626808</td>\n",
       "      <td>0.588497</td>\n",
       "      <td>0.669006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>0.573888</td>\n",
       "      <td>0.815287</td>\n",
       "      <td>0.734884</td>\n",
       "      <td>0.486982</td>\n",
       "      <td>0.333482</td>\n",
       "      <td>0.497070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NL</th>\n",
       "      <td>0.394548</td>\n",
       "      <td>0.577495</td>\n",
       "      <td>0.375044</td>\n",
       "      <td>0.342334</td>\n",
       "      <td>0.328250</td>\n",
       "      <td>0.356991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AU</th>\n",
       "      <td>0.251076</td>\n",
       "      <td>0.322718</td>\n",
       "      <td>0.261011</td>\n",
       "      <td>0.288091</td>\n",
       "      <td>0.198781</td>\n",
       "      <td>0.252517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PT</th>\n",
       "      <td>0.035868</td>\n",
       "      <td>0.203822</td>\n",
       "      <td>0.083625</td>\n",
       "      <td>0.091610</td>\n",
       "      <td>0.108545</td>\n",
       "      <td>0.101663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Year              2010       2011       2012       2013       2014        All\n",
       "Destination                                                                  \n",
       "NDF          42.503587  45.367304  55.022553  59.156220  61.762090  58.347349\n",
       "US           44.045911  38.097665  31.131215  28.854870  26.729527  29.222632\n",
       "other         2.761836   4.738854   4.852770   4.634764   4.837444   4.728954\n",
       "FR            4.304161   3.983015   2.840708   2.232401   1.910653   2.353233\n",
       "IT            1.076040   1.690021   1.459632   1.248795   1.299924   1.328174\n",
       "GB            1.004304   1.469214   1.310121   1.042671   0.969058   1.088774\n",
       "ES            1.542324   1.673036   1.191019   0.994455   0.933748   1.053638\n",
       "CA            1.506456   1.061571   0.737418   0.626808   0.588497   0.669006\n",
       "DE            0.573888   0.815287   0.734884   0.486982   0.333482   0.497070\n",
       "NL            0.394548   0.577495   0.375044   0.342334   0.328250   0.356991\n",
       "AU            0.251076   0.322718   0.261011   0.288091   0.198781   0.252517\n",
       "PT            0.035868   0.203822   0.083625   0.091610   0.108545   0.101663"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pivot = pd.pivot_table(df_tmp, values='Count', index=['Destination'], columns=['Year'], \n",
    "                          aggfunc=np.sum, margins=True).astype(int)\n",
    "\n",
    "total_by_years = df_pivot.loc[['All']]\n",
    "\n",
    "for year, col in total_by_years.iteritems():\n",
    "    subtotal_by_year = col[0]\n",
    "    \n",
    "    df_pivot[year] = df_pivot[year]/subtotal_by_year*100\n",
    "    \n",
    "df_pivot = df_pivot.sort_values(by=\"All\", ascending=False).drop(\"All\") # We sort by the column \"All\" and remove the line \"All\"\n",
    "df_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
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    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Destination</th>\n",
       "      <th>Year</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NDF</td>\n",
       "      <td>2010</td>\n",
       "      <td>42.503587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US</td>\n",
       "      <td>2010</td>\n",
       "      <td>44.045911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>other</td>\n",
       "      <td>2010</td>\n",
       "      <td>2.761836</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Destination  Year      value\n",
       "0         NDF  2010  42.503587\n",
       "1          US  2010  44.045911\n",
       "2       other  2010   2.761836"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pivot2 = df_pivot.copy()\n",
    "\n",
    "df_pivot2[\"Destination\"] = df_pivot.index\n",
    "df_pivot2 = pd.melt(df_pivot2, id_vars=\"Destination\")\n",
    "df_pivot2 = df_pivot2.drop(df_pivot2[df_pivot2.Year == \"All\"].index) # We remove the column 'All' from the plotting\n",
    "\n",
    "df_pivot2.head(n=3)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
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    "deletable": true,
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       "<p><code>&lt;Bokeh Notebook handle for <strong>In[20]</strong>&gt;</code></p>"
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       "<bokeh.io._CommsHandle at 0x7fa2b27b5588>"
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   "source": [
    "p = bk.Bar(df_pivot2, label=['Destination'], values='value', group=\"Year\", legend='top_right', ylabel='Booking Proportion in %')\n",
    "bk.show(p)"
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    "### 1.4.2.3 Conclusion\n",
    "\n",
    "Over the years the repartition has changed. As we are doing predictions with user data from **July 2014**, this changes may require us to focus on the most recent data during training. *It is most likely to have similar pattern than test data*.\n",
    "\n",
    "## 1.4.3 Account Creation Analysis\n",
    "\n",
    "We want to evaluate the quantity of data we would have left if we only consider the N-last months of the data.\n",
    "We have seen that the \"_early users_\" are very different of the actual ones. Let's see how much of data we can delete of the dataset."
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country_destination</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year_month_account_created</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010 - 01</th>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010 - 02</th>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010 - 03</th>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010 - 04</th>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            country_destination\n",
       "year_month_account_created                     \n",
       "2010 - 01                                    61\n",
       "2010 - 02                                   102\n",
       "2010 - 03                                   163\n",
       "2010 - 04                                   157"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_df = df_output_by_year_and_month[['country_destination','year_month_account_created']].sort_values(\n",
    "    by=\"year_month_account_created\"\n",
    ")\n",
    "\n",
    "tmp_df = tmp_df.groupby([\"year_month_account_created\"]).agg(\"count\")\n",
    "tmp_df.head(n=4)"
   ]
  },
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   "execution_count": 22,
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    "editable": true
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       "            var render_items = [{\"docid\":\"52471635-2363-4a31-88b9-ae679f5d6bf1\",\"elementid\":\"b5f0ad01-0eee-43d3-8f69-24c622b30276\",\"modelid\":\"cea8ca97-0b5a-4cf5-9915-dd0b28319bb1\",\"notebook_comms_target\":\"e1b33152-ca3c-4e8b-a6db-b4902d6b89aa\"}];\n",
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       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
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       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
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       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
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      "text/html": [
       "<p><code>&lt;Bokeh Notebook handle for <strong>In[22]</strong>&gt;</code></p>"
      ],
      "text/plain": [
       "<bokeh.io._CommsHandle at 0x7fa2b0f5cba8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
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   ],
   "source": [
    "# Plot Data\n",
    "x_data = [str(x) for x in tmp_df[\"country_destination\"].index.values]\n",
    "y_data = tmp_df[\"country_destination\"].values\n",
    "\n",
    "p = bk_plt.figure(plot_width=980, plot_height=400, y_range=[0, y_data.max()+1000], x_range=x_data)\n",
    "\n",
    "p.line(x_data, y_data, line_width=2)\n",
    "p.circle(x_data, y_data, fill_color=\"white\", size=8)\n",
    "\n",
    "\n",
    "# Draw a line when X% of the users of the dataset have sign-up\n",
    "split_1 = 20 # 80% left\n",
    "split_2 = 30 # 70% left\n",
    "split_3 = 50 # 50% left\n",
    "split_4 = 70 # 30% left\n",
    "\n",
    "thrs_value_1 = 213450 * split_1 / 100\n",
    "thrs_value_2 = 213450 * split_2 / 100\n",
    "thrs_value_3 = 213450 * split_3 / 100\n",
    "thrs_value_4 = 213450 * split_4 / 100\n",
    "\n",
    "index_1 = 0\n",
    "index_2 = 0\n",
    "index_3 = 0\n",
    "index_4 = 0\n",
    "\n",
    "tmp_sum = 0\n",
    "for val in y_data:\n",
    "    tmp_sum += val\n",
    "    \n",
    "    if tmp_sum <= thrs_value_1:\n",
    "        index_1 += 1     \n",
    "        index_2 += 1\n",
    "        index_3 += 1\n",
    "        index_4 += 1\n",
    "    elif tmp_sum <= thrs_value_2:    \n",
    "        index_2 += 1\n",
    "        index_3 += 1\n",
    "        index_4 += 1\n",
    "    elif tmp_sum <= thrs_value_3:\n",
    "        index_3 += 1\n",
    "        index_4 += 1\n",
    "    elif tmp_sum <= thrs_value_4:\n",
    "        index_4 += 1\n",
    "    else:\n",
    "        break\n",
    "           \n",
    "        \n",
    "vline_1 = bk_md.Span(location=index_1, dimension='height', line_color='blue', line_width=3) # 80% Data Left\n",
    "vline_2 = bk_md.Span(location=index_2, dimension='height', line_color='green', line_width=3) # 70% Data Left\n",
    "vline_3 = bk_md.Span(location=index_3, dimension='height', line_color='orange', line_width=3) # 50% Data Left\n",
    "vline_4 = bk_md.Span(location=index_4, dimension='height', line_color='red', line_width=3) # 30% Data Left\n",
    "\n",
    "p.renderers.extend([vline_1, vline_2, vline_3, vline_4])\n",
    "\n",
    "# We set label orientation\n",
    "from math import pi\n",
    "p.xaxis.major_label_orientation = pi/2\n",
    "\n",
    "# We display the graph\n",
    "bk_plt.show(p)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "If we ony consider of 2013 and 2014, we still have more than 70% of the data for training.\n",
    "This is **important**, because _session data_ start in **January 2014**.\n",
    "\n",
    "_\"In the sessions dataset, the data only dates back to 1/1/2014, while the users dataset dates back to 2010.\"_\n",
    "\n",
    "## 1.4.4 Reported Ages of User"
   ]
  },
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   "execution_count": 23,
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     "output_type": "stream",
     "text": [
      "Count of NaN Values = 87990 => 41.22%\n",
      "Total Row Count = 213451\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age\n",
       "0  38.0\n",
       "1  56.0\n",
       "2  42.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_age = df_train_users[[\"age\"]]\n",
    "\n",
    "na_count = df_age.isnull().sum()[0]\n",
    "row_count = df_age.shape[0]\n",
    "na_percent = \"%.2f\" % (float(na_count) / row_count * 100)\n",
    "\n",
    "print (\"Count of NaN Values = \" + str(na_count) + \" => \" + str(na_percent) + \"%\")\n",
    "print (\"Total Row Count = \" + str(row_count))\n",
    "\n",
    "df_age = df_age.dropna().reset_index(drop=True)\n",
    "df_age.head(n=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bucket</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(000,005]</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(005,010]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(010,015]</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(015,020]</td>\n",
       "      <td>2404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(020,025]</td>\n",
       "      <td>12825</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Bucket  Count\n",
       "0  (000,005]     57\n",
       "1  (005,010]      0\n",
       "2  (010,015]      8\n",
       "3  (015,020]   2404\n",
       "4  (020,025]  12825"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_age_bucket = df_age.groupby(pd.cut(df_age['age'], np.arange(0,999999,5))).count()\n",
    "\n",
    "idx_tmp = df_age_bucket.index.values\n",
    "bckt_count_tmp = df_age_bucket.age.values\n",
    "\n",
    "data = dict()\n",
    "\n",
    "for bck, cnt in zip(idx_tmp, bckt_count_tmp):\n",
    "    start_point = int(bck[1:].partition(\",\")[0])\n",
    "    end_point = int(bck[:-1].partition(\",\")[2])\n",
    "    \n",
    "    range_name = \"(\"+ \"%03d\" % (start_point) + \",\" + \"%03d\" % (end_point) + \"]\"\n",
    "    if start_point < 100:\n",
    "        data[range_name] = cnt\n",
    "    elif start_point >= 100 and start_point < 1000:\n",
    "        if not \"(100, 999]\" in data:\n",
    "            data[\"(100, 999]\"] = cnt\n",
    "        else:\n",
    "            data[\"(100, 999]\"] += cnt\n",
    "    else:\n",
    "        if not \"+1000\" in data:\n",
    "            data[\"+1000\"] = cnt\n",
    "        else:\n",
    "            data[\"+1000\"] += cnt\n",
    "            \n",
    "df_age_stats = pd.DataFrame({\n",
    "    'Bucket': list(data.keys()),\n",
    "    'Count': list(data.values())\n",
    "}).sort_values(by=\"Bucket\").reset_index(drop=True)\n",
    "\n",
    "df_age_stats.head(n=5)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
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    "editable": true
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       "      console.info(\"Bokeh: all callbacks have finished\");\n",
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       "    function load_libs(js_urls, callback) {\n",
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       "      if (window._bokeh_is_loading > 0) {\n",
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      "text/html": [
       "<p><code>&lt;Bokeh Notebook handle for <strong>In[25]</strong>&gt;</code></p>"
      ],
      "text/plain": [
       "<bokeh.io._CommsHandle at 0x7fa2b0177b00>"
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   ],
   "source": [
    "x_range = df_age_stats[\"Bucket\"].values\n",
    "\n",
    "bar2 = bk.Bar(\n",
    "    df_age_stats, \n",
    "    values = 'Count', \n",
    "    label  = ['Bucket'],\n",
    "    title  = \"Reported Ages of Users\", \n",
    "    width  = 980,\n",
    "    ylabel = \"Number of Users by Bucket\"\n",
    ")\n",
    "\n",
    "bk.show(bar2)"
   ]
  }
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