{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "d28a52f4-1425-5ebd-40ea-a081e41d24de"
      },
      "source": [
        "Lets take a quick look at the data first:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "59c229a9-4419-9994-e3f4-a88fb5de48c5"
      },
      "outputs": [],
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn\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",
        "from subprocess import check_output\n",
        "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "e8c787cb-8d85-2543-a0e7-a9e5c796a899"
      },
      "outputs": [],
      "source": [
        "clicks = pd.read_csv('../input/clicks_train.csv')\n",
        "doc_cat = pd.read_csv('../input/documents_categories.csv')\n",
        "doc_ent = pd.read_csv('../input/documents_entities.csv')\n",
        "doc_meta = pd.read_csv('../input/documents_meta.csv')\n",
        "doc_topics = pd.read_csv('../input/documents_topics.csv')\n",
        "events = pd.read_csv('../input/events.csv')\n",
        "pageviews = pd.read_csv('../input/page_views_sample.csv')\n",
        "promoted_content = pd.read_csv('../input/promoted_content.csv')\n",
        "sample_submission = pd.read_csv('../input/sample_submission.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "03f7122c-79ed-3d43-dced-292003887159"
      },
      "outputs": [],
      "source": [
        "clicks.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "6b34130e-63b2-3a92-dd7a-4ca5ffc11876"
      },
      "outputs": [],
      "source": [
        "doc_cat.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "38a51e16-0704-db08-20d5-cd72258eb297"
      },
      "outputs": [],
      "source": [
        "doc_ent.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "a67b9576-c75e-8a09-65de-f2dc30e3e004"
      },
      "outputs": [],
      "source": [
        "doc_meta.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "d6c07207-72a6-5ec5-9eb3-45317c65688d"
      },
      "outputs": [],
      "source": [
        "doc_topics.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "c498fd03-8a0e-6690-3a4b-ad7417024224"
      },
      "outputs": [],
      "source": [
        "events.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "51a563d5-9d98-38bd-ccac-44d929ab1c50"
      },
      "outputs": [],
      "source": ""
    }
  ],
  "metadata": {
    "_change_revision": 0,
    "_is_fork": false,
    "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": "3.5.2"
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}