{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "6c9e8f5d-cce8-d252-0cba-939be9104138"
      },
      "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": "f94d4dd4-435f-c07f-c3e2-bb1b58e54bea"
      },
      "outputs": [],
      "source": [
        "destinations = pd.read_csv(\"../input/destinations.csv\", nrows = 1000)\n",
        "test = pd.read_csv(\"../input/test.csv\", nrows = 1000)\n",
        "train = pd.read_csv(\"../input/train.csv\", nrows = 1000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "ffb642f3-5a6d-eb03-920b-3d6e35cef676"
      },
      "outputs": [],
      "source": [
        "train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "21524f21-1356-9b6b-5ed9-69eda2c61729"
      },
      "outputs": [],
      "source": [
        "train.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "b183d7b0-70bd-07e1-4bbd-4069b82f8977"
      },
      "outputs": [],
      "source": [
        "test.head(5)\n",
        "         "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "f8716727-dd70-1cef-02b9-c484cc9ae25a"
      },
      "outputs": [],
      "source": [
        "destinations.head(5)\n",
        "                "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "d49122cc-a69c-9ae1-5e63-5e61c6146615"
      },
      "outputs": [],
      "source": [
        "train[\"hotel_cluster\"].value_counts()"
      ]
    }
  ],
  "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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}