{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "85bd53cd-6da6-cf34-ef8a-c652a784c2e0"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from pandas import DataFrame\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "7c1c8f44-c5e8-a286-99f2-90fdae581eba"
      },
      "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": "44d1e72f-4f08-32e1-f060-53b7676fadbd"
      },
      "outputs": [],
      "source": [
        "train = pd.read_csv('../input/train.csv')\n",
        "test = pd.read_csv('../input/test.csv')\n",
        "\n",
        "train.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "21056622-9188-067a-e6a6-ba97c3a2ecb8"
      },
      "source": [
        "#Test Data Hd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "5d5f76d8-2af6-cade-41a5-976a0f2f1d3e"
      },
      "outputs": [],
      "source": [
        "test.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "d1acacfb-11d4-5965-379a-9807496924ea"
      },
      "outputs": [],
      "source": [
        "train.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "4f246560-bbca-b175-7ff3-c6e30486c4ed"
      },
      "outputs": [],
      "source": [
        "test.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "07c4aeaa-21b5-d050-5cd8-ab7599e4ee02"
      },
      "outputs": [],
      "source": [
        "plt.figure(figsize=(20,10))\n",
        "sns.distplot(bins=200, a=train.accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "725e2d7f-63a0-e99c-bc19-bc74b55695b9"
      },
      "outputs": [],
      "source": [
        "train_head = train[:10000]\n",
        "\n",
        "plt.figure(figsize=(20,15))\n",
        "plt.scatter(x=train_head.x, y=train_head.y, c=train_head.time)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "70bf4e31-2aa4-bdb8-372e-1e831e7f217c"
      },
      "outputs": [],
      "source": [
        "plt.figure(figsize=(20,10))\n",
        "plt.scatter(x=train_head.time, y=train_head.accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "1b747339-9bfd-ccbc-94dc-fde3a3e7c4fb"
      },
      "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",
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