{
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    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "a64cc163-a76d-7b6c-8292-e997097e6c8e"
      },
      "source": [
        "# Ok Let's Go!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "cdf1f583-491f-ab24-05d3-fa62a05cc89b"
      },
      "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": "81a4339d-0150-aa0c-00eb-28af2fc41804"
      },
      "outputs": [],
      "source": [
        "# load data\n",
        "data = pd.read_csv('../input/train.csv')\n",
        "print(data.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "5532b0dd-ac15-5610-22ba-12349a38d30b"
      },
      "outputs": [],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "fe4041ec-2a7c-8d95-4480-c12f0973e93c"
      },
      "outputs": [],
      "source": [
        "data.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "836d54bf-5d4c-f1a8-9774-bd2af41e5fb9"
      },
      "outputs": [],
      "source": [
        "# preparing the data for modeling, training and testing.\n"
      ]
    }
  ],
  "metadata": {
    "_change_revision": 0,
    "_is_fork": false,
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
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        "version": 3
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      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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