{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "564a53f0-18f1-71bc-6d5d-f67a3751a2c6"
      },
      "source": [
        "#EDA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "5240101f-d4a0-7d9e-9550-f74c380d800d"
      },
      "outputs": [],
      "source": [
        "# This Python 3 environment comes with many helpful analytics libraries installed\n",
        "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
        "# For example, here's several helpful packages to load in \n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "\n",
        "# Input data files are available in the \"../input/\" directory.\n",
        "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
        "\n",
        "from subprocess import check_output\n",
        "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
        "\n",
        "# Any results you write to the current directory are saved as output."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "c5fd9455-60a2-da9d-feac-2e506d50c41e"
      },
      "outputs": [],
      "source": [
        "clicks_train = pd.read_csv(\"../input/clicks_train.csv\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "16c55413-7322-21de-238f-f4c43c9e79d9"
      },
      "source": [
        "### Explore train Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "b172ce3e-e502-d2d6-9876-b4c2854038c0"
      },
      "outputs": [],
      "source": [
        "clicks_train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "0e5bd601-6792-7922-8d91-48975121f7e8"
      },
      "outputs": [],
      "source": [
        "clicks_train.count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "68c44217-8e0d-f220-38c1-107cb91da88f"
      },
      "outputs": [],
      "source": [
        "clicks_train['display_id'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "0c7593ca-4e59-dcb9-8a9c-987c47cf00c0"
      },
      "outputs": [],
      "source": [
        "clicks_train['ad_id'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "076cc8c8-8965-5145-c849-17e88213dacb"
      },
      "outputs": [],
      "source": [
        "clicked = clicks_train['clicked']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "13fce7b6-4175-0656-a10d-e0bd0413078d"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "d97994a0-3d42-289b-0328-5aba98e7a0ec"
      },
      "outputs": [],
      "source": [
        "clicks_test = pd.read_csv(\"../input/clicks_test.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "fb049125-533e-0192-adee-c8e3d7ebe261"
      },
      "outputs": [],
      "source": ""
    }
  ],
  "metadata": {
    "_change_revision": 0,
    "_is_fork": false,
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
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      },
      "file_extension": ".py",
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      "pygments_lexer": "ipython3",
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