{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "69a7631d-d04b-fcb6-1017-9090dc75baf3"
      },
      "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.\n",
        "\n",
        "# data analysis and wrangling\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import random as rnd\n",
        "\n",
        "# visualization\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "# machine learning\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC, LinearSVC\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.linear_model import Perceptron\n",
        "from sklearn.linear_model import SGDClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "2099d846-73d5-841d-6647-13131abc4372"
      },
      "outputs": [],
      "source": [
        "train_df = pd.read_csv('../input/train.csv')\n",
        "test_df = pd.read_csv('../input/test.csv')\n",
        "combine = [train_df, test_df]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "9d99dea2-1560-9b9c-4fea-c874f9574a1b"
      },
      "outputs": [],
      "source": [
        "print(train_df.columns.values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "bf81cb20-3cfd-cf10-36eb-28f440ffe0ef"
      },
      "outputs": [],
      "source": [
        "# preview the data\n",
        "train_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "b53598ce-7d92-7cbb-af35-b918f02b3d9c"
      },
      "outputs": [],
      "source": [
        "train_df.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "c88d5867-1a65-b07f-bb12-2127f2aa1164"
      },
      "outputs": [],
      "source": ""
    }
  ],
  "metadata": {
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    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
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