PUBG Model With Limited Computation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "PUBG.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "XmnwTr1DDKUi",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# PUBG\n"
      ]
    },
    {
      "metadata": {
        "id": "JMCSeUaeCMHX",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "![alt text](https://pubgmobile.proximabeta.com/event/pics/youtubegaming2560_1440.jpg)"
      ]
    },
    {
      "metadata": {
        "id": "lWwEqdHWFNP5",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Outline :  what we will work on.\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "1. First & most important, How to handle that huge size of data. \n",
        "2. Then we importing this pubg data directly from kaggle to out working space. Without downloading it.\n",
        "3. Third, Preprocessing the train , test data &  check out the available feature set.\n",
        "4. Forth, Take down the deeper insight of Data, Perform exploratory data analysis & feature analysis.\n",
        "5. Finally, We use RandomForest algorithm .  One of the famous algorithm over kaggle. And using RF we creat a pubg Placement model.  Then create a prediction on given test data.\n"
      ]
    },
    {
      "metadata": {
        "id": "rKVOzomRB0VO",
        "colab_type": "code",
        "outputId": "3f6b3fd2-2898-41a5-d6bb-1c6c5e3b1cc3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        }
      },
      "cell_type": "code",
      "source": [
        "# Intall dependencies\n",
        "# To work with kaggle API, For directly importing the data from kaggle to google colab notebook,\n",
        "\n",
        "!pip install kaggle\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.6/dist-packages (1.5.1.1)\n",
            "Requirement already satisfied: urllib3<1.23.0,>=1.15 in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.22)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.11.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.6/dist-packages (from kaggle) (2018.11.29)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.5.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.18.4)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from kaggle) (4.28.1)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.2.6)\n",
            "Requirement already satisfied: idna<2.7,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle) (2.6)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle) (3.0.4)\n",
            "Requirement already satisfied: Unidecode>=0.04.16 in /usr/local/lib/python3.6/dist-packages (from python-slugify->kaggle) (1.0.23)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "gZXzijFJgqcm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# importing basic libraries\n",
        "#numpy # pandas # matplot #seaborn \n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as pld\n",
        "import seaborn as sns\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gqqThsLyiQMp",
        "colab_type": "code",
        "outputId": "6dbc933e-19f3-4bbf-82b9-d656d0466824",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 91
        }
      },
      "cell_type": "code",
      "source": [
        "# upload the kaggle json file # Kaggle Credential\n",
        "from google.colab import files\n",
        "files.upload()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-98680524-e285-48f1-b578-42fd02c5b115\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-98680524-e285-48f1-b578-42fd02c5b115\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"vin1234\",\"key\":\"929881c20dc9da038e20c07ca1bd7818\"}'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "metadata": {
        "id": "ldoHmeAhvEZa",
        "colab_type": "code",
        "outputId": "c6471c3b-333d-47c3-e431-6f81fb74fa7c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "# check the file.\n",
        "!ls"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "kaggle.json  sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "eHWFY1Z5JO45",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "Check out the github repo for kaggle api [Kaggle API](https://github.com/Kaggle/kaggle-api) "
      ]
    },
    {
      "metadata": {
        "id": "AzWpGVUbd0wb",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# specified by the kaggle\n",
        "# Ensure kaggle.json is in the location ~/.kaggle/kaggle.json to use the API.\n",
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "\n",
        "# Now to secure your credentials. Run a Unix based Command\n",
        "!chmod 600 ~/.kaggle/kaggle.json\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4xiS-ECrd1Z5",
        "colab_type": "code",
        "outputId": "30b86e68-b4be-4610-dc05-b74871fd4fd4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        }
      },
      "cell_type": "code",
      "source": [
        "# Copy the kaggle API paste it here. To directly import the data form kaggle to google colab.\n",
        "\n",
        "!kaggle competitions download -c pubg-finish-placement-prediction"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading sample_submission_V2.csv.zip to /content\n",
            " 30% 5.00M/16.9M [00:00<00:00, 15.4MB/s]\n",
            "100% 16.9M/16.9M [00:00<00:00, 38.1MB/s]\n",
            "Downloading test_V2.csv.zip to /content\n",
            " 91% 89.0M/98.3M [00:03<00:00, 17.5MB/s]\n",
            "100% 98.3M/98.3M [00:03<00:00, 32.5MB/s]\n",
            "Downloading train_V2.csv.zip to /content\n",
            "100% 239M/239M [00:03<00:00, 67.0MB/s]\n",
            "100% 239M/239M [00:04<00:00, 62.3MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "YrMBCmSvfDgl",
        "colab_type": "code",
        "outputId": "d03f4d8b-ec97-436c-b186-de0d146223e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "# check the files.\n",
        "\n",
        "!ls\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "kaggle.json  sample_submission_V2.csv.zip  train_V2.csv.zip\n",
            "sample_data  test_V2.csv.zip\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "y6jDWDfftU0S",
        "colab_type": "code",
        "outputId": "c74983d9-799d-4433-fcb3-da65c459ab0d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "!unzip train_V2.csv.zip\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  train_V2.csv.zip\n",
            "  inflating: train_V2.csv            \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "h6-vq4Z2tuFS",
        "colab_type": "code",
        "outputId": "b1119be6-6079-40ef-8534-f698b547eb08",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "!unzip test_V2.csv.zip"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  test_V2.csv.zip\n",
            "  inflating: test_V2.csv             \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "0oGDJKzCfHg0",
        "colab_type": "code",
        "outputId": "6710090e-83ef-4c3e-f564-0303fe4b1a12",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "# Unzip the train and test file.\n",
        "\n",
        "!ls"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "kaggle.json  sample_submission_V2.csv.zip  test_V2.csv.zip  train_V2.csv.zip\n",
            "sample_data  test_V2.csv\t\t   train_V2.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "L5GecMlGfHln",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# read train and test file.\n",
        "\n",
        "train=pd.read_csv('train_V2.csv')\n",
        "test=pd.read_csv('test_V2.csv')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "42nhMaO1vWYB",
        "colab_type": "code",
        "outputId": "562293ba-7a89-4f14-875b-7699dc9ab13e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "# shape of train data\n",
        "train.head()\n",
        "train.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4446966, 29)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "metadata": {
        "id": "7H9LN3XFvzxM",
        "colab_type": "code",
        "outputId": "d916160f-8d01-4c1c-bcb4-a838fdd0408e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "#shape of test data.\n",
        "test.head()\n",
        "test.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1934174, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "metadata": {
        "id": "JzLtdJyAf3U5",
        "colab_type": "code",
        "outputId": "8d37465e-0bae-4884-df6e-11f3605b5738",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        }
      },
      "cell_type": "code",
      "source": [
        "# Columns or features.\n",
        "train.columns\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Id', 'groupId', 'matchId', 'assists', 'boosts', 'damageDealt', 'DBNOs',\n",
              "       'headshotKills', 'heals', 'killPlace', 'killPoints', 'kills',\n",
              "       'killStreaks', 'longestKill', 'matchDuration', 'matchType', 'maxPlace',\n",
              "       'numGroups', 'rankPoints', 'revives', 'rideDistance', 'roadKills',\n",
              "       'swimDistance', 'teamKills', 'vehicleDestroys', 'walkDistance',\n",
              "       'weaponsAcquired', 'winPoints', 'winPlacePerc'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "id": "xOc0nlEHfc0_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Feature set :\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "Let's talk about availabe feature in the given PubG Data."
      ]
    },
    {
      "metadata": {
        "id": "a0iXx28ffpf0",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Data** fields\n",
        "\n",
        "**DBNOs** - Number of enemy players knocked.\n",
        "\n",
        "**assists** - Number of enemy players this player damaged that were killed by teammates.\n",
        "\n",
        "**boosts** - Number of boost items used.\n",
        "\n",
        "**damageDealt** - Total damage dealt. Note: Self inflicted damage is subtracted.\n",
        "\n",
        "**headshotKills** - Number of enemy players killed with headshots.\n",
        "\n",
        "**heals** - Number of healing items used.\n",
        "\n",
        "**Id** - Player’s Id\n",
        "\n",
        "**killPlace** - Ranking in match of number of enemy players killed.\n",
        "\n",
        "\n",
        "**killPoints **- Kills-based external ranking of player. (Think of this as an Elo ranking where only kills matter.) If there is a value other than -1 in rankPoints, then any 0 in killPoints should be treated as a “None”.\n",
        "\n",
        "\n",
        "**killStreaks** - Max number of enemy players killed in a short amount of time.\n",
        "\n",
        "\n",
        "**kills** - Number of enemy players killed.\n",
        "\n",
        "**longestKill** - Longest distance between player and player killed at time of death. This may be misleading, as downing a player and driving away may lead to a large longestKill stat.\n",
        "\n",
        "\n",
        "**matchDuration** - Duration of match in seconds.\n",
        "\n",
        "**matchId** - ID to identify match. There are no matches that are in both the training and testing set.\n",
        "\n",
        "\n",
        "**matchType **- String identifying the game mode that the data comes from. The standard modes are “solo”, “duo”, “squad”, “solo-fpp”, “duo-fpp”, and “squad-fpp”; other modes are from events or custom matches.\n",
        "\n",
        "**rankPoints **- Elo-like ranking of player. This ranking is inconsistent and is being deprecated in the API’s next version, so use with caution. Value of -1 takes place of “None”.\n",
        "\n",
        "\n",
        "**revives **- Number of times this player revived teammates.\n",
        "\n",
        "**rideDistance** - Total distance traveled in vehicles measured in meters.\n",
        "\n",
        "**roadKills **- Number of kills while in a vehicle.\n",
        "\n",
        "**swimDistance ** Total distance traveled by swimming measured in meters.\n",
        "\n",
        "**teamKills **- Number of times this player killed a teammate.\n",
        "\n",
        "**vehicleDestroys **- Number of vehicles destroyed.\n",
        "\n",
        "**walkDistance **- Total distance traveled on foot measured in meters.\n",
        "\n",
        "**weaponsAcquired **- Number of weapons picked up.\n",
        "\n",
        "**winPoints **- Win-based external ranking of player. (Think of this as an Elo ranking where only winning matters.) If there is a value other than -1 in rankPoints, then any 0 in winPoints should be treated as a “None”.\n",
        "\n",
        "**groupId - ID** to identify a group within a match. If the same group of players plays in different matches, they will have a different groupId each time.\n",
        "\n",
        "**numGroups **- Number of groups we have data for in the match.\n",
        "\n",
        "**maxPlace **- Worst placement we have data for in the match. This may not match with numGroups, as sometimes the data skips over placements.\n",
        "\n",
        "\n",
        "**winPlacePerc **- The target of prediction. This is a percentile winning placement, where 1 corresponds to 1st place, and 0 corresponds to last place in the match. It is calculated off of maxPlace, not numGroups, so it is possible to have missing chunks in a match."
      ]
    },
    {
      "metadata": {
        "id": "35d0eTAcfHoW",
        "colab_type": "code",
        "outputId": "69acb628-bd3f-4de1-96f1-7ef35eed1e48",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 635
        }
      },
      "cell_type": "code",
      "source": [
        "#Summary of a DataFrame. Test|Train\n",
        "\n",
        "train.info()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4446966 entries, 0 to 4446965\n",
            "Data columns (total 29 columns):\n",
            "Id                 object\n",
            "groupId            object\n",
            "matchId            object\n",
            "assists            int64\n",
            "boosts             int64\n",
            "damageDealt        float64\n",
            "DBNOs              int64\n",
            "headshotKills      int64\n",
            "heals              int64\n",
            "killPlace          int64\n",
            "killPoints         int64\n",
            "kills              int64\n",
            "killStreaks        int64\n",
            "longestKill        float64\n",
            "matchDuration      int64\n",
            "matchType          object\n",
            "maxPlace           int64\n",
            "numGroups          int64\n",
            "rankPoints         int64\n",
            "revives            int64\n",
            "rideDistance       float64\n",
            "roadKills          int64\n",
            "swimDistance       float64\n",
            "teamKills          int64\n",
            "vehicleDestroys    int64\n",
            "walkDistance       float64\n",
            "weaponsAcquired    int64\n",
            "winPoints          int64\n",
            "winPlacePerc       float64\n",
            "dtypes: float64(6), int64(19), object(4)\n",
            "memory usage: 983.9+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "otNznFbzgjgs",
        "colab_type": "code",
        "outputId": "43378058-3d08-4f13-d08e-7f3439bdb4d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        }
      },
      "cell_type": "code",
      "source": [
        "#Test data\n",
        "test.info()\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1934174 entries, 0 to 1934173\n",
            "Data columns (total 28 columns):\n",
            "Id                 object\n",
            "groupId            object\n",
            "matchId            object\n",
            "assists            int64\n",
            "boosts             int64\n",
            "damageDealt        float64\n",
            "DBNOs              int64\n",
            "headshotKills      int64\n",
            "heals              int64\n",
            "killPlace          int64\n",
            "killPoints         int64\n",
            "kills              int64\n",
            "killStreaks        int64\n",
            "longestKill        float64\n",
            "matchDuration      int64\n",
            "matchType          object\n",
            "maxPlace           int64\n",
            "numGroups          int64\n",
            "rankPoints         int64\n",
            "revives            int64\n",
            "rideDistance       float64\n",
            "roadKills          int64\n",
            "swimDistance       float64\n",
            "teamKills          int64\n",
            "vehicleDestroys    int64\n",
            "walkDistance       float64\n",
            "weaponsAcquired    int64\n",
            "winPoints          int64\n",
            "dtypes: float64(5), int64(19), object(4)\n",
            "memory usage: 413.2+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "xwDebNjGfHsU",
        "colab_type": "code",
        "outputId": "573daffe-eb0a-4c0c-890b-0ab81744f07b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        }
      },
      "cell_type": "code",
      "source": [
        "# Check out for the missing value.\n",
        "#train\n",
        "\n",
        "train.isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id                 0\n",
              "groupId            0\n",
              "matchId            0\n",
              "assists            0\n",
              "boosts             0\n",
              "damageDealt        0\n",
              "DBNOs              0\n",
              "headshotKills      0\n",
              "heals              0\n",
              "killPlace          0\n",
              "killPoints         0\n",
              "kills              0\n",
              "killStreaks        0\n",
              "longestKill        0\n",
              "matchDuration      0\n",
              "matchType          0\n",
              "maxPlace           0\n",
              "numGroups          0\n",
              "rankPoints         0\n",
              "revives            0\n",
              "rideDistance       0\n",
              "roadKills          0\n",
              "swimDistance       0\n",
              "teamKills          0\n",
              "vehicleDestroys    0\n",
              "walkDistance       0\n",
              "weaponsAcquired    0\n",
              "winPoints          0\n",
              "winPlacePerc       1\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "metadata": {
        "id": "ExAC98Qygs7e",
        "colab_type": "code",
        "outputId": "5769e061-7e1b-4815-9b56-8dba20a536ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        }
      },
      "cell_type": "code",
      "source": [
        "#test\n",
        "\n",
        "test.isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id                 0\n",
              "groupId            0\n",
              "matchId            0\n",
              "assists            0\n",
              "boosts             0\n",
              "damageDealt        0\n",
              "DBNOs              0\n",
              "headshotKills      0\n",
              "heals              0\n",
              "killPlace          0\n",
              "killPoints         0\n",
              "kills              0\n",
              "killStreaks        0\n",
              "longestKill        0\n",
              "matchDuration      0\n",
              "matchType          0\n",
              "maxPlace           0\n",
              "numGroups          0\n",
              "rankPoints         0\n",
              "revives            0\n",
              "rideDistance       0\n",
              "roadKills          0\n",
              "swimDistance       0\n",
              "teamKills          0\n",
              "vehicleDestroys    0\n",
              "walkDistance       0\n",
              "weaponsAcquired    0\n",
              "winPoints          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "metadata": {
        "id": "qBHOaLhSfHw2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# find the missing rows and drop them.\n",
        "train[train.winPlacePerc.isnull()]\n",
        "train.drop(2744604,inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bRFadjj9CoQY",
        "colab_type": "code",
        "outputId": "7d8e00c7-c4a4-4bf3-9e36-a6b447ab46bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        }
      },
      "cell_type": "code",
      "source": [
        "train.isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id                 0\n",
              "groupId            0\n",
              "matchId            0\n",
              "assists            0\n",
              "boosts             0\n",
              "damageDealt        0\n",
              "DBNOs              0\n",
              "headshotKills      0\n",
              "heals              0\n",
              "killPlace          0\n",
              "killPoints         0\n",
              "kills              0\n",
              "killStreaks        0\n",
              "longestKill        0\n",
              "matchDuration      0\n",
              "matchType          0\n",
              "maxPlace           0\n",
              "numGroups          0\n",
              "rankPoints         0\n",
              "revives            0\n",
              "rideDistance       0\n",
              "roadKills          0\n",
              "swimDistance       0\n",
              "teamKills          0\n",
              "vehicleDestroys    0\n",
              "walkDistance       0\n",
              "weaponsAcquired    0\n",
              "winPoints          0\n",
              "winPlacePerc       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "metadata": {
        "id": "8SP-OFcSfH0Q",
        "colab_type": "code",
        "outputId": "70e3544c-1cd1-4280-8511-216c2ec769f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "# Number of matches played in each train and test data.\n",
        "#matchId\n",
        "len(train.matchId.unique())\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "47964"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "metadata": {
        "id": "coMRxT2lDEhV",
        "colab_type": "code",
        "outputId": "551be5e8-8398-4179-89c5-d95f584350f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "len(test.matchId.unique())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20556"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "metadata": {
        "id": "JAQGf4CRfHu1",
        "colab_type": "code",
        "outputId": "429f762c-7f19-4f1f-d3a0-4436d4a5daf1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1163
        }
      },
      "cell_type": "code",
      "source": [
        "# Player in each match.\n",
        "# According to the rules of the pubG  Maximum of 100 players can only participate in a match.\n",
        "# Lets find out the number of player participated in each match\n",
        "train.groupby('matchId')['matchId'].count()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "matchId\n",
              "0000a43bce5eec     95\n",
              "0000eb01ea6cdd     98\n",
              "0002912fe5ed71     95\n",
              "0003b92987589e    100\n",
              "0006eb8c17708d     93\n",
              "00077604e50a63     98\n",
              "00086c74bb4efc     98\n",
              "00086e740a5804     98\n",
              "001125344b660c     96\n",
              "001360264d4b5f     91\n",
              "0014d9d1b0aff6     94\n",
              "001616ed5da99b     97\n",
              "0016fe3ee17ce7     97\n",
              "00177a6ce4dfb5     92\n",
              "00188d50e054f5     92\n",
              "001937f739426c     95\n",
              "0019bc34b3c58e     97\n",
              "0019d729577e9c     94\n",
              "001cd8e7e6b737     24\n",
              "001e5e4799a31c     92\n",
              "001e7bc06b1611     59\n",
              "001eeedf57047a     99\n",
              "00200fe5d9aae5     99\n",
              "0022adebf59be6     96\n",
              "0022e660571ee8     97\n",
              "00232f9d9c5421     96\n",
              "0025477f88bbd9     94\n",
              "002771bd25fe0e     92\n",
              "0027a504cd3b0c     99\n",
              "0027b1ffb2e346     17\n",
              "                 ... \n",
              "ffdebe280440e8     86\n",
              "ffdf8e9dc18596     98\n",
              "ffdff4a21177ea     92\n",
              "ffe08b9747a59f     94\n",
              "ffe21d8fafaf43     96\n",
              "ffe2a50b7023fb     89\n",
              "ffe2ee39f210ff     92\n",
              "ffe6c9fd39b935     98\n",
              "ffe71c86ca1d81     97\n",
              "ffe7d37a82297b     98\n",
              "ffe94ff48407e7     93\n",
              "ffead5be0b50e8     98\n",
              "ffeaf5f7073d41     89\n",
              "ffeb2131284e45     94\n",
              "ffed6b75ebe4ce     91\n",
              "ffed79cf43d7d7     97\n",
              "ffef306edf6b36     98\n",
              "fff0a2994a6f3a     87\n",
              "fff2c218352941     98\n",
              "fff2dfadd0635e     40\n",
              "fff497dff0186d     90\n",
              "fff4d3cbb6c317    100\n",
              "fffa170cf8ed83     93\n",
              "fffa3a29e38094     93\n",
              "fffb576fad6509     95\n",
              "fffd74b5150cb4     97\n",
              "fffe53015be300     97\n",
              "fffe562611d981     94\n",
              "fffe721f841f85     97\n",
              "fffe92232706aa     93\n",
              "Name: matchId, Length: 47964, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "metadata": {
        "id": "BbwXwEBKh50v",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# create new feature player_played\n",
        "\n",
        "train['player_played']=train.groupby('matchId')['matchId'].transform('count')\n",
        "\n",
        "\n",
        "# test\n",
        "\n",
        "test['player_played']=test.groupby('matchId')['matchId'].transform('count')\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "T7m4TnFism5U",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Exploratory Data Analysis (EDA)   "
      ]
    },
    {
      "metadata": {
        "id": "9tkcdBc6iC7V",
        "colab_type": "code",
        "outputId": "6317fa9d-a9dc-40b1-ddc2-64e2d16ab84b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 572
        }
      },
      "cell_type": "code",
      "source": [
        "#Let's visualize the feature set.\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "# Plotting matches having player more that 60.\n",
        "plt.subplots(figsize=(15,8))\n",
        "sns.countplot(train[train['player_played']>=60]['player_played'])\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1428: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
            "  stat_data = remove_na(group_data)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd363965080>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4oAAAHgCAYAAADjfZPtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRETEQIOiiIiIiIiIGPwGqqR3COKYUhsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd363965ac8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "QjM9nbWX-lMM",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Outliers Detectors\n",
        "\n",
        "\n",
        "### What are Outliers. \n",
        "Any data point or a set of data point which shows different  behavious compare to the rest of  data set.\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "![alt text](https://cdn-images-1.medium.com/max/1600/1*TbUF_HTQ6jOhO8EoPnmekQ.jpeg)\n",
        "\n",
        "## Pubg Outliers \n",
        "\n",
        "\n",
        "\n",
        "- According to the rules of the game you need to find the players and then kill them. That's what the basic strategy of the game . \n",
        "- But few people do what they just hide and start their killing streak . But data tells everything.\n",
        "- So we need to scrap out these cheaters."
      ]
    },
    {
      "metadata": {
        "id": "M9faxAa7DkjQ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "## Distance Covered \n",
        "\n",
        "---\n",
        "\n",
        "In pubg We have three way to cover a particular distance to reach to a destination.\n",
        "### walkDistance.\n",
        "![alt text](https://blog.turtlebeach.com/wp-content/uploads/2017/04/media_img_1490255446609-1024x576.jpg)\n",
        "\n",
        "### swimDistance\n",
        "\n",
        "![alt text](https://thumbs.gfycat.com/EvenSpiffyFerret-small.gif)\n",
        "\n",
        "### rideDistance \n",
        "\n",
        "![alt text](https://i.ytimg.com/vi/atlOIN_stzI/maxresdefault.jpg)\n",
        "\n",
        " So ingeneral we have three mode.\n",
        " \n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "rpLSV0HAiDAS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#rideDistance\n",
        "#walkDistance\n",
        "#swimDistance"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Wp3kKquLIT6d",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##  Find the cheaters|Outliers\n",
        "To identify outliers we gonna do what . \n",
        "Create two new features .\n",
        " - Total Distance\n",
        " \n",
        " -  kills without moving"
      ]
    },
    {
      "metadata": {
        "id": "jzW7-8YoiDCz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#total_distance   #train.\n",
        "train['total_distance']=train.rideDistance+train.walkDistance+train.swimDistance\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "0wD_9Lq3x5Yo",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# total distance  #test\n",
        "test['total_distance']=test.rideDistance+test.walkDistance+test.swimDistance"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "nwMT_wGgiDHy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# kills_without_moving\n",
        "\n",
        "#kills\n",
        "#total_distance\n",
        "\n",
        "#train\n",
        "train['kills_without_moving']=((train['kills']>0)&(train['total_distance']==0))\n",
        "\n",
        "#test\n",
        "test['kills_without_moving']=((test['kills']>0)&(test['total_distance']==0))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "wDrxzDyXJXF4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Now we drop all those players who have kills the they didn't cover the some least distance."
      ]
    },
    {
      "metadata": {
        "id": "0BLibRpbiDLP",
        "colab_type": "code",
        "outputId": "a685580e-83a7-4db2-f9c6-89d4d8b285c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "cell_type": "code",
      "source": [
        "# find the count.\n",
        "train['kills_without_moving'].value_counts()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    4445430\n",
              "True        1535\n",
              "Name: kills_without_moving, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "metadata": {
        "id": "cIuulo2xzQHp",
        "colab_type": "code",
        "outputId": "c2292a96-8189-4cb0-ce60-e5930ef6266d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "cell_type": "code",
      "source": [
        "# find the count for test data.\n",
        "\n",
        "test['kills_without_moving'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    1933863\n",
              "True         311\n",
              "Name: kills_without_moving, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "metadata": {
        "id": "WQtpziEEzDnc",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "So there are 1535 outliers in train data and 311 outliers in test data .\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "hUETMqKOiDOY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# drop them.\n",
        "\n",
        "train.drop(train[train['kills_without_moving']==True].index,inplace=True)\n",
        "\n",
        "\n",
        "test.drop(test[test['kills_without_moving']==True].index,inplace=True)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xSzbh3MsLIjS",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Now let's work on the given feature set of train and test data."
      ]
    },
    {
      "metadata": {
        "id": "lrX4eym3Kr2u",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**roadKills** - Number of kills while in a vehicle."
      ]
    },
    {
      "metadata": {
        "id": "lyN_pkaaiDSy",
        "colab_type": "code",
        "outputId": "00eb0870-7362-410c-b086-90416b9f6933",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        }
      },
      "cell_type": "code",
      "source": [
        "# roadKills count\n",
        "\n",
        "\n",
        "train['roadKills'].value_counts()\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     4432434\n",
              "1       11247\n",
              "2        1298\n",
              "3         297\n",
              "4          72\n",
              "5          36\n",
              "6          20\n",
              "7          11\n",
              "8           7\n",
              "11          2\n",
              "10          2\n",
              "9           2\n",
              "18          1\n",
              "14          1\n",
              "Name: roadKills, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "metadata": {
        "id": "VQTH7Y0viDaQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# see most of the player have kills from  zero to 10 \n",
        "# so to remove the outliers from our data we drop all the player who have more than 10 roadkills.\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BdTuRQAm0dFe",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# drop all the road kills above 10\n",
        "\n",
        "\n",
        "#test\n",
        "\n",
        "train.drop(train[train['roadKills']>=10].index,inplace=True)\n",
        "\n",
        "#test \n",
        "\n",
        "test.drop(test[test['roadKills']>=10].index,inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "77IR5QbILY6o",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**kills** - Number of enemy players killed."
      ]
    },
    {
      "metadata": {
        "id": "XrJBBK8JiDmA",
        "colab_type": "code",
        "outputId": "cec3a3f3-99f6-4210-e685-ce2608a1b883",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1090
        }
      },
      "cell_type": "code",
      "source": [
        "# kills count\n",
        "\n",
        "train['kills'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     2529721\n",
              "1      927648\n",
              "2      472291\n",
              "3      232280\n",
              "4      124414\n",
              "5       66480\n",
              "6       37867\n",
              "7       21744\n",
              "8       12716\n",
              "9        7581\n",
              "10       4562\n",
              "11       2756\n",
              "12       1726\n",
              "13       1108\n",
              "14        742\n",
              "15        468\n",
              "16        319\n",
              "17        226\n",
              "18        159\n",
              "19        105\n",
              "20         94\n",
              "22         74\n",
              "21         61\n",
              "23         40\n",
              "24         40\n",
              "26         24\n",
              "25         24\n",
              "28         22\n",
              "27         15\n",
              "33         12\n",
              "29         11\n",
              "30         11\n",
              "31         11\n",
              "35          6\n",
              "36          6\n",
              "38          6\n",
              "34          5\n",
              "37          5\n",
              "41          5\n",
              "53          4\n",
              "32          4\n",
              "39          4\n",
              "40          4\n",
              "42          3\n",
              "43          2\n",
              "44          2\n",
              "46          2\n",
              "55          2\n",
              "56          2\n",
              "57          2\n",
              "45          1\n",
              "47          1\n",
              "48          1\n",
              "49          1\n",
              "50          1\n",
              "66          1\n",
              "65          1\n",
              "72          1\n",
              "Name: kills, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "metadata": {
        "id": "ONaYjDQt06Td",
        "colab_type": "code",
        "outputId": "842af6f7-4da1-4110-f7cc-1d2ad17cffb1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 872
        }
      },
      "cell_type": "code",
      "source": [
        "#test\n",
        "test['kills'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     1099421\n",
              "1      404819\n",
              "2      206319\n",
              "3      101149\n",
              "4       54100\n",
              "5       28877\n",
              "6       16468\n",
              "7        9262\n",
              "8        5278\n",
              "9        3281\n",
              "10       1902\n",
              "11       1155\n",
              "12        673\n",
              "13        417\n",
              "14        230\n",
              "15        150\n",
              "16        119\n",
              "17         71\n",
              "18         37\n",
              "19         33\n",
              "20         21\n",
              "22         11\n",
              "30         11\n",
              "21          8\n",
              "24          8\n",
              "23          5\n",
              "25          5\n",
              "26          4\n",
              "28          3\n",
              "31          3\n",
              "32          2\n",
              "37          2\n",
              "36          2\n",
              "33          2\n",
              "56          1\n",
              "34          1\n",
              "29          1\n",
              "27          1\n",
              "40          1\n",
              "41          1\n",
              "43          1\n",
              "46          1\n",
              "47          1\n",
              "52          1\n",
              "55          1\n",
              "58          1\n",
              "Name: kills, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "metadata": {
        "id": "vvcA92jBiDrz",
        "colab_type": "code",
        "outputId": "d7ac5786-548b-490a-86cd-d5913d8e9c23",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        }
      },
      "cell_type": "code",
      "source": [
        "#Plot the kills data.\n",
        "\n",
        "plt.subplots(figsize=(15,8))\n",
        "sns.countplot(data=train,x=train['kills'])\n",
        "plt.title('Kills')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1428: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
            "  stat_data = remove_na(group_data)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA48AAAHvCAYAAAD0Aup8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABkfQPoOcnHpnX2BAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd355102048>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "-crg3Do6L4AO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "See players who kills more that 35 are pretty rare so they all act as outliners. so lets drop all of them"
      ]
    },
    {
      "metadata": {
        "id": "Jdzy7GaviDhZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# drop the outliers.\n",
        "\n",
        "#train\n",
        "train.drop(train[train['kills']>=35].index,inplace=True)\n",
        "\n",
        "#test\n",
        "\n",
        "test.drop(test[test['kills']>=35].index,inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8GPIFKbHOvLB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Should we remove all these outliers from our dataset or not what's your take on that."
      ]
    },
    {
      "metadata": {
        "id": "nwJIKoXiO63K",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "In DataScience their is no predefined strategy . Where you can say for sure this is the right way to do it.  All of us have different way, different methods and differnet openion.\n",
        "\n",
        "\n",
        "--------------------------------\n",
        "\n",
        "According to me in that particular case we have a millions of rows , So we can afford to loose few thousand . If it helps to create a better model."
      ]
    },
    {
      "metadata": {
        "id": "ilP1IirHPKcp",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        ".\n",
        "\n",
        "\n",
        "\n",
        "**longestKill** - Longest distance between player and player killed at time of death. This may be misleading, as downing a player and driving away may lead to a large longestKill stat"
      ]
    },
    {
      "metadata": {
        "id": "Gv6t8nVriDfl",
        "colab_type": "code",
        "outputId": "e6ec7674-238b-4c61-c70c-28aa1f5d7cb6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        }
      },
      "cell_type": "code",
      "source": [
        "#longetKill \n",
        "#distplot\n",
        "plt.subplots(figsize=(15,8))\n",
        "sns.distplot(train['longestKill'],bins=20)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd35502e048>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3MAAAHgCAYAAAAPJDS+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd3563b4f60>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "XwQuzayHQCSW",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Most kills are made from a distance of 100 meters or closer. There are however some outliers who make a kill from more than 1km away. This is probably done by cheaters."
      ]
    },
    {
      "metadata": {
        "id": "5H982EG_3yc8",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        ""
      ]
    },
    {
      "metadata": {
        "id": "GzuDatpz41nH",
        "colab_type": "code",
        "outputId": "b63a905b-b7b7-4d05-a65e-021d5a04457a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        }
      },
      "cell_type": "code",
      "source": [
        "# Let's take a look at the players who make these shots. \n",
        "train[train['longestKill']>=1000]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>groupId</th>\n",
              "      <th>matchId</th>\n",
              "      <th>assists</th>\n",
              "      <th>boosts</th>\n",
              "      <th>damageDealt</th>\n",
              "      <th>DBNOs</th>\n",
              "      <th>headshotKills</th>\n",
              "      <th>heals</th>\n",
              "      <th>killPlace</th>\n",
              "      <th>...</th>\n",
              "      <th>swimDistance</th>\n",
              "      <th>teamKills</th>\n",
              "      <th>vehicleDestroys</th>\n",
              "      <th>walkDistance</th>\n",
              "      <th>weaponsAcquired</th>\n",
              "      <th>winPoints</th>\n",
              "      <th>winPlacePerc</th>\n",
              "      <th>player_played</th>\n",
              "      <th>total_distance</th>\n",
              "      <th>kills_without_moving</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>202281</th>\n",
              "      <td>88e2af7d78af5a</td>\n",
              "      <td>34ddeede52c042</td>\n",
              "      <td>4346bc63bc67fa</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>783.9</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3052.00</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>0.9231</td>\n",
              "      <td>88</td>\n",
              "      <td>3775.20</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>240005</th>\n",
              "      <td>41c2f5c0699807</td>\n",
              "      <td>9faecf87ab4275</td>\n",
              "      <td>634edab75860b3</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>1284.0</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>18</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>48.87</td>\n",
              "      <td>38</td>\n",
              "      <td>1500</td>\n",
              "      <td>0.5385</td>\n",
              "      <td>29</td>\n",
              "      <td>48.87</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>324313</th>\n",
              "      <td>ef390c152bcc3d</td>\n",
              "      <td>30fd444be3bbc1</td>\n",
              "      <td>4f7f8d6cf558b4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1028.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1264.00</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>51</td>\n",
              "      <td>2981.00</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>656553</th>\n",
              "      <td>9948b058562163</td>\n",
              "      <td>c8cb8491112bf6</td>\n",
              "      <td>0104eeb664494d</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1410.0</td>\n",
              "      <td>17</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>29.21</td>\n",
              "      <td>30</td>\n",
              "      <td>1500</td>\n",
              "      <td>0.6000</td>\n",
              "      <td>41</td>\n",
              "      <td>29.21</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>803632</th>\n",
              "      <td>4e7e6c74e3c57d</td>\n",
              "      <td>94698690918933</td>\n",
              "      <td>da91b0c3d875f8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>196.8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>51</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1074.00</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>61</td>\n",
              "      <td>3159.00</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>895411</th>\n",
              "      <td>1f5ba6e0cfb968</td>\n",
              "      <td>512ea24b831be3</td>\n",
              "      <td>5fb0d8b1fc16cf</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1012.0</td>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>569.50</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>0.9091</td>\n",
              "      <td>86</td>\n",
              "      <td>569.50</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1172437</th>\n",
              "      <td>303a93cfa1f46c</td>\n",
              "      <td>8795d39fd0df86</td>\n",
              "      <td>9c8962b58bb3e3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>329.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>45</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>832.50</td>\n",
              "      <td>9</td>\n",
              "      <td>1500</td>\n",
              "      <td>0.2857</td>\n",
              "      <td>58</td>\n",
              "      <td>832.50</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1209416</th>\n",
              "      <td>528659ff1c1aec</td>\n",
              "      <td>7d1ba83423551d</td>\n",
              "      <td>ea9386587d5888</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>1640.0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2848.00</td>\n",
              "      <td>4</td>\n",
              "      <td>1500</td>\n",
              "      <td>0.9412</td>\n",
              "      <td>52</td>\n",
              "      <td>2848.00</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1642712</th>\n",
              "      <td>91966848e08e2f</td>\n",
              "      <td>0ee4fbd27657c9</td>\n",
              "      <td>17dea22cefe62a</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2103.0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>235.30</td>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>28</td>\n",
              "      <td>235.30</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015559</th>\n",
              "      <td>5ff0c1a9fab2ba</td>\n",
              "      <td>2d8119b1544f87</td>\n",
              "      <td>904cecf36217df</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1302.0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>15</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>133.20</td>\n",
              "      <td>43</td>\n",
              "      <td>0</td>\n",
              "      <td>0.6000</td>\n",
              "      <td>42</td>\n",
              "      <td>133.20</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2122128</th>\n",
              "      <td>42df3102cb540b</td>\n",
              "      <td>7d9b2be15b355b</td>\n",
              "      <td>610d78f3affd2e</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>464.50</td>\n",
              "      <td>52</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>10</td>\n",
              "      <td>464.50</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2152425</th>\n",
              "      <td>4b9f61bac5eb0a</td>\n",
              "      <td>bc717b964f3bbe</td>\n",
              "      <td>838cb9a3c94598</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>945.4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>844.70</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>0.5714</td>\n",
              "      <td>60</td>\n",
              "      <td>844.70</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2592718</th>\n",
              "      <td>24e0fec84c18e9</td>\n",
              "      <td>8404855ca02e48</td>\n",
              "      <td>e886a8ebb702cf</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>1684.0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>11</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1115.00</td>\n",
              "      <td>54</td>\n",
              "      <td>1500</td>\n",
              "      <td>0.5714</td>\n",
              "      <td>26</td>\n",
              "      <td>4851.00</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2981715</th>\n",
              "      <td>7f77051c7cef52</td>\n",
              "      <td>d6579a630399b5</td>\n",
              "      <td>4784f7d9a06b51</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>1025.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>87.96</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3998.00</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>93</td>\n",
              "      <td>4085.96</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3081503</th>\n",
              "      <td>f19a76e8d7ac52</td>\n",
              "      <td>624d65c529f87c</td>\n",
              "      <td>de19b70121c40f</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1038.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>32</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>270.00</td>\n",
              "      <td>28</td>\n",
              "      <td>1500</td>\n",
              "      <td>0.8571</td>\n",
              "      <td>57</td>\n",
              "      <td>270.00</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3255171</th>\n",
              "      <td>5524c154448425</td>\n",
              "      <td>674195558ad41b</td>\n",
              "      <td>db6f6d1f0d4904</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1355.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1039.00</td>\n",
              "      <td>37</td>\n",
              "      <td>0</td>\n",
              "      <td>0.5789</td>\n",
              "      <td>21</td>\n",
              "      <td>1039.00</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3304284</th>\n",
              "      <td>d0c286ce498e17</td>\n",
              "      <td>17fdd45e612bab</td>\n",
              "      <td>3eaaa2f7a360fe</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>2330.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>68.02</td>\n",
              "      <td>32</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>53</td>\n",
              "      <td>68.02</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3320960</th>\n",
              "      <td>0040e53dfe7b5d</td>\n",
              "      <td>650661c2351eb7</td>\n",
              "      <td>2daabf3a7852e6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>399.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>14</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1511.00</td>\n",
              "      <td>45</td>\n",
              "      <td>1500</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>15</td>\n",
              "      <td>5481.00</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3552532</th>\n",
              "      <td>db638834c62f6f</td>\n",
              "      <td>0614b611d6a935</td>\n",
              "      <td>ff80300f8262f5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>517.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1310.00</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>30</td>\n",
              "      <td>1344.88</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4332473</th>\n",
              "      <td>d8857d3d7e31b6</td>\n",
              "      <td>085de7a36897e6</td>\n",
              "      <td>42f997c16d8a0e</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>1685.0</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>18</td>\n",
              "      <td>8</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>523.30</td>\n",
              "      <td>35</td>\n",
              "      <td>1500</td>\n",
              "      <td>0.9091</td>\n",
              "      <td>26</td>\n",
              "      <td>523.30</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>20 rows × 32 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Id         groupId         matchId  assists  boosts  \\\n",
              "202281   88e2af7d78af5a  34ddeede52c042  4346bc63bc67fa        0       3   \n",
              "240005   41c2f5c0699807  9faecf87ab4275  634edab75860b3        5       0   \n",
              "324313   ef390c152bcc3d  30fd444be3bbc1  4f7f8d6cf558b4        2       0   \n",
              "656553   9948b058562163  c8cb8491112bf6  0104eeb664494d        6       0   \n",
              "803632   4e7e6c74e3c57d  94698690918933  da91b0c3d875f8        0       0   \n",
              "895411   1f5ba6e0cfb968  512ea24b831be3  5fb0d8b1fc16cf        4       0   \n",
              "1172437  303a93cfa1f46c  8795d39fd0df86  9c8962b58bb3e3        2       1   \n",
              "1209416  528659ff1c1aec  7d1ba83423551d  ea9386587d5888        0       6   \n",
              "1642712  91966848e08e2f  0ee4fbd27657c9  17dea22cefe62a        3       2   \n",
              "2015559  5ff0c1a9fab2ba  2d8119b1544f87  904cecf36217df        3       3   \n",
              "2122128  42df3102cb540b  7d9b2be15b355b  610d78f3affd2e        5       0   \n",
              "2152425  4b9f61bac5eb0a  bc717b964f3bbe  838cb9a3c94598        3       0   \n",
              "2592718  24e0fec84c18e9  8404855ca02e48  e886a8ebb702cf        7       0   \n",
              "2981715  7f77051c7cef52  d6579a630399b5  4784f7d9a06b51        3       5   \n",
              "3081503  f19a76e8d7ac52  624d65c529f87c  de19b70121c40f        3       0   \n",
              "3255171  5524c154448425  674195558ad41b  db6f6d1f0d4904        1       0   \n",
              "3304284  d0c286ce498e17  17fdd45e612bab  3eaaa2f7a360fe        7       0   \n",
              "3320960  0040e53dfe7b5d  650661c2351eb7  2daabf3a7852e6        0       0   \n",
              "3552532  db638834c62f6f  0614b611d6a935  ff80300f8262f5        2       0   \n",
              "4332473  d8857d3d7e31b6  085de7a36897e6  42f997c16d8a0e        5       0   \n",
              "\n",
              "         damageDealt  DBNOs  headshotKills  heals  killPlace  \\\n",
              "202281         783.9      5              1      1          5   \n",
              "240005        1284.0      8              5      7         18   \n",
              "324313        1028.0      0              0      0          9   \n",
              "656553        1410.0     17              5      0          3   \n",
              "803632         196.8      0              0      0         51   \n",
              "895411        1012.0     11              5      0          5   \n",
              "1172437        329.3      0              0      2         45   \n",
              "1209416       1640.0      0              7      0          1   \n",
              "1642712       2103.0      0              4     11         11   \n",
              "2015559       1302.0      0              6      5         15   \n",
              "2122128       2500.0      0              7      1          2   \n",
              "2152425        945.4      0              0      0         11   \n",
              "2592718       1684.0      0              4      7         11   \n",
              "2981715       1025.0      5              2      5          2   \n",
              "3081503       1038.0      0              0      0         32   \n",
              "3255171       1355.0      0              2      0          9   \n",
              "3304284       2330.0      0              2      0          2   \n",
              "3320960        399.0      2              0      6         14   \n",
              "3552532        517.0      0              0      0         10   \n",
              "4332473       1685.0     11              3     18          8   \n",
              "\n",
              "                 ...           swimDistance  teamKills  vehicleDestroys  \\\n",
              "202281           ...                   0.00          0                1   \n",
              "240005           ...                   0.00          0                0   \n",
              "324313           ...                   0.00          0                0   \n",
              "656553           ...                   0.00          0                0   \n",
              "803632           ...                   0.00          0                0   \n",
              "895411           ...                   0.00          0                0   \n",
              "1172437          ...                   0.00          0                0   \n",
              "1209416          ...                   0.00          0                0   \n",
              "1642712          ...                   0.00          0                0   \n",
              "2015559          ...                   0.00          0                0   \n",
              "2122128          ...                   0.00          2                0   \n",
              "2152425          ...                   0.00          0                0   \n",
              "2592718          ...                   0.00          0                0   \n",
              "2981715          ...                  87.96          0                0   \n",
              "3081503          ...                   0.00          0                0   \n",
              "3255171          ...                   0.00          1                0   \n",
              "3304284          ...                   0.00          1                0   \n",
              "3320960          ...                   0.00          0                0   \n",
              "3552532          ...                   0.00          2                0   \n",
              "4332473          ...                   0.00          0                0   \n",
              "\n",
              "         walkDistance  weaponsAcquired winPoints  winPlacePerc  player_played  \\\n",
              "202281        3052.00                9         0        0.9231             88   \n",
              "240005          48.87               38      1500        0.5385             29   \n",
              "324313        1264.00               26         0        1.0000             51   \n",
              "656553          29.21               30      1500        0.6000             41   \n",
              "803632        1074.00               22         0        0.0000             61   \n",
              "895411         569.50               18         0        0.9091             86   \n",
              "1172437        832.50                9      1500        0.2857             58   \n",
              "1209416       2848.00                4      1500        0.9412             52   \n",
              "1642712        235.30               41         0        0.5000             28   \n",
              "2015559        133.20               43         0        0.6000             42   \n",
              "2122128        464.50               52         0        0.0000             10   \n",
              "2152425        844.70               14         0        0.5714             60   \n",
              "2592718       1115.00               54      1500        0.5714             26   \n",
              "2981715       3998.00                7         0        1.0000             93   \n",
              "3081503        270.00               28      1500        0.8571             57   \n",
              "3255171       1039.00               37         0        0.5789             21   \n",
              "3304284         68.02               32         0        1.0000             53   \n",
              "3320960       1511.00               45      1500        0.0000             15   \n",
              "3552532       1310.00                6         0        0.0000             30   \n",
              "4332473        523.30               35      1500        0.9091             26   \n",
              "\n",
              "         total_distance  kills_without_moving  \n",
              "202281          3775.20                 False  \n",
              "240005            48.87                 False  \n",
              "324313          2981.00                 False  \n",
              "656553            29.21                 False  \n",
              "803632          3159.00                 False  \n",
              "895411           569.50                 False  \n",
              "1172437          832.50                 False  \n",
              "1209416         2848.00                 False  \n",
              "1642712          235.30                 False  \n",
              "2015559          133.20                 False  \n",
              "2122128          464.50                 False  \n",
              "2152425          844.70                 False  \n",
              "2592718         4851.00                 False  \n",
              "2981715         4085.96                 False  \n",
              "3081503          270.00                 False  \n",
              "3255171         1039.00                 False  \n",
              "3304284           68.02                 False  \n",
              "3320960         5481.00                 False  \n",
              "3552532         1344.88                 False  \n",
              "4332473          523.30                 False  \n",
              "\n",
              "[20 rows x 32 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "metadata": {
        "id": "TfJH3_KM5IYG",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "20 player who have longestKill more than 1000m."
      ]
    },
    {
      "metadata": {
        "id": "XDaI335X5FU1",
        "colab_type": "code",
        "outputId": "a794e116-15a0-4435-ccdf-6e718d0424c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 127
        }
      },
      "cell_type": "code",
      "source": [
        "# Let's take a look at the players who make these shots. \n",
        "test[test['longestKill']>=1000]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>groupId</th>\n",
              "      <th>matchId</th>\n",
              "      <th>assists</th>\n",
              "      <th>boosts</th>\n",
              "      <th>damageDealt</th>\n",
              "      <th>DBNOs</th>\n",
              "      <th>headshotKills</th>\n",
              "      <th>heals</th>\n",
              "      <th>killPlace</th>\n",
              "      <th>...</th>\n",
              "      <th>roadKills</th>\n",
              "      <th>swimDistance</th>\n",
              "      <th>teamKills</th>\n",
              "      <th>vehicleDestroys</th>\n",
              "      <th>walkDistance</th>\n",
              "      <th>weaponsAcquired</th>\n",
              "      <th>winPoints</th>\n",
              "      <th>player_played</th>\n",
              "      <th>total_distance</th>\n",
              "      <th>kills_without_moving</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>15085</th>\n",
              "      <td>b2d7b5499c7fdd</td>\n",
              "      <td>c57312c6ebd390</td>\n",
              "      <td>5fb683abbf1f33</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1119.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1135.0</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>48</td>\n",
              "      <td>1190.4</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 31 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                   Id         groupId         matchId  assists  boosts  \\\n",
              "15085  b2d7b5499c7fdd  c57312c6ebd390  5fb683abbf1f33        4       0   \n",
              "\n",
              "       damageDealt  DBNOs  headshotKills  heals  killPlace  \\\n",
              "15085       1119.0      0              2      3         11   \n",
              "\n",
              "               ...           roadKills  swimDistance  teamKills  \\\n",
              "15085          ...                   0           0.0          0   \n",
              "\n",
              "       vehicleDestroys  walkDistance weaponsAcquired  winPoints  \\\n",
              "15085                0        1135.0              19          0   \n",
              "\n",
              "       player_played  total_distance  kills_without_moving  \n",
              "15085             48          1190.4                 False  \n",
              "\n",
              "[1 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "metadata": {
        "id": "0T5HfJ8piDFQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# drop outliers.      \n",
        "\n",
        "\n",
        "#train\n",
        "train.drop(train[train['longestKill']>=1000].index,inplace=True)\n",
        "\n",
        "#test\n",
        "\n",
        "test.drop(test[test['longestKill']>=1000].index,inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2WcUkbHf52O9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "There is something fishy going on with these players. We are probably better off removing them from our dataset."
      ]
    },
    {
      "metadata": {
        "id": "_HpMPdelUXjR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Lets findout the Anomalies in travelling (rideDistance, walkDistance and swimDistance)\n",
        "\n",
        "Let's check out anomalies in Distance travelled.\n"
      ]
    },
    {
      "metadata": {
        "id": "-Ebn-kd7U68-",
        "colab_type": "code",
        "outputId": "42290df1-da93-40e3-b002-7ddc202c19ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "cell_type": "code",
      "source": [
        "train[['rideDistance','walkDistance','swimDistance','total_distance']].head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>rideDistance</th>\n",
              "      <th>walkDistance</th>\n",
              "      <th>swimDistance</th>\n",
              "      <th>total_distance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0000</td>\n",
              "      <td>244.80</td>\n",
              "      <td>0.00</td>\n",
              "      <td>244.8000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0045</td>\n",
              "      <td>1434.00</td>\n",
              "      <td>11.04</td>\n",
              "      <td>1445.0445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0000</td>\n",
              "      <td>161.80</td>\n",
              "      <td>0.00</td>\n",
              "      <td>161.8000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0000</td>\n",
              "      <td>202.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>202.7000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0000</td>\n",
              "      <td>49.75</td>\n",
              "      <td>0.00</td>\n",
              "      <td>49.7500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   rideDistance  walkDistance  swimDistance  total_distance\n",
              "0        0.0000        244.80          0.00        244.8000\n",
              "1        0.0045       1434.00         11.04       1445.0445\n",
              "2        0.0000        161.80          0.00        161.8000\n",
              "3        0.0000        202.70          0.00        202.7000\n",
              "4        0.0000         49.75          0.00         49.7500"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "metadata": {
        "id": "0llRAHglUYKb",
        "colab_type": "code",
        "outputId": "376f3e64-8302-4b27-a045-e46f282b5e44",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "cell_type": "code",
      "source": [
        "# plot walkDistance \n",
        "\n",
        "plt.figure(figsize=(12,4))\n",
        "sns.distplot(train['walkDistance'], bins=10)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAAEGCAYAAACuBLlKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzs3X18VPWd9//XmbvcMEnIhBlMCGCM\ngpgKErXWZLkrRCu92qVdq8APWn8XvXZVlvprqYSlXoV9LCAtwlWxdrfrpY/tasVcZdku8nCB9mrs\nKpkGFYqINwhyk0BMJiQkTMjN3JzfHyEDgYQEyGSSyfv5ePjAOed8z/me+WTy+Mw3n/P9GqZpmoiI\niIiIyHWzxLoDIiIiIiLxQsm1iIiIiEgfUXItIiIiItJHlFyLiIiIiPQRJdciIiIiIn3EFusO9CWf\n72ysuwBAenoy9fXnYt0N6UOKaXxRPOOPYhpfFM/4E28xdbtTut2nkesosNmsse6C9DHFNL4onvFH\nMY0vimf8GUoxVXItIiIiItJHlFyLiIiIiPQRJdciIiIiIn1EybWIiIiISB9Rci0iIiIi0keUXIuI\niIiI9BEl1yIiIiIifUTJdQw1twZj3QURERER6UNKrmPkUMUZFv+v/+KjY3Wx7oqIiIiI9JFeLX++\ndu1a9u/fj2EYrFixgokTJ0b2lZWVsXHjRqxWK1OnTmXx4sXdtqmqqmLZsmWEQiHcbjfr16/H4XBE\nzvWDH/wAh8PBunXrCAQCLF++nFOnTmG1Wnn66acZPXp0H99+7BytagSgosbPhBtdMe6NiIiIiPSF\nHkeu9+zZw/HjxykpKWHNmjWsWbOm0/7Vq1fz3HPPsXnzZnbv3s3hw4e7bbNp0ybmz5/Pq6++ytix\nY9myZUvkPLt37+bEiROR19u3byc1NZXNmzfz6KOPsmHDhr665wHhjL8VgHMqDRERERGJGz0m116v\nl1mzZgGQm5tLQ0MDfr8fgIqKCtLS0sjMzMRisTBt2jS8Xm+3bcrLy5k5cyYAM2bMwOv1AtDW1sY/\n/uM/8thjj3W6blFREQAFBQXs3bu3D2879hr8bQCca1FyLSIiIhIvekyua2trSU9Pj7x2uVz4fD4A\nfD4fLpfrsn3dtWlubo6UgWRkZETO88tf/pJ58+bhdDo7Xbfj3BaLBcMwaGtru557HVA6Rq71UKOI\niIhI/OhVzfXFTNO86ot01aZj27Fjx/jggw9YsmQJ5eXl13Xd9PRkbDbrVfcvGtzulCvuP9vcnlQH\nzZ6PlYFBcYovimf8UUzji+IZf4ZKTHtMrj0eD7W1tZHXNTU1uN3uLvdVV1fj8Xiw2+1dtklOTqal\npYXExMTIsW+++SanTp3ioYcewu/3U1dXxwsvvIDH48Hn83HrrbcSCAQwTbPTw49dqa8/d9VvQDS4\n3Sn4fGeveMzphmYAzjS29HisxF5vYiqDh+IZfxTT+KJ4xp94i+mVvij0WBZSWFjIzp07ATh48CAe\njydSvpGdnY3f76eyspJgMEhpaSmFhYXdtikoKIhs37VrF1OmTOGRRx7h9ddf5//8n//DypUrmT59\nOv/jf/wPCgsL2bFjBwClpaXcc8891/cuDCAtbUFa2kKAHmgUERERiSc9jlzn5+eTl5fH3LlzMQyD\nlStXsnXrVlJSUigqKmLVqlUsXboUgNmzZ5OTk0NOTs5lbQCWLFlCcXExJSUlZGVlMWfOnG6vO3v2\nbMrKypg3b15ker540fEwI+iBRhEREZF4YpjXUkQ9QA2UPzf09KePT07U85NX9wGQnGDj59+f2l9d\nk2sUb3/OGuoUz/ijmMYXxTP+xFtMr6ssRPremYtGrptbg4Tj5/uNiIiIyJCm5DoGGs5Pw2cYYAIt\nraHYdkhERERE+oSS6xjoGLn2DE8C4FxrIJbdEREREZE+ouQ6BjoWkMnMGAbooUYRERGReKHkOgYu\nJNfJgFZpFBEREYkXSq5j4Iy/DWeSnZTk9kVxNNe1iIiISHxQch0DDU2tDHc6SE5sn2ZcZSEiIiIi\n8UHJdT9rbQvR3BoizZlAcsL55Foj1yIiIiJxQcl1PzvT1F5vffHIdbNGrkVERETigpLrfnbmbEdy\nnXChLEQj1yIiIiJxQcl1P2toap/jergzgSSVhYiIiIjEFSXX/ezCyLUjUnOtshARERGR+KDkup+d\nOT9ynaaRaxEREZG4o+S6n3UsIDPc6cBmtZBgt2oqPhEREZE4oeS6nzX4z49cD0sAIDnRxrnWQCy7\nJCIiIiJ9RMl1Pzvjb8WZZMdua3/rkxJsGrkWERERiRNKrvvZGX8raU5H5HVygo3m1hCmacawVyIi\nIiLSF2y9OWjt2rXs378fwzBYsWIFEydOjOwrKytj48aNWK1Wpk6dyuLFi7ttU1VVxbJlywiFQrjd\nbtavX4/D4eDnP/85b731FqZpMn36dB5//HG2bt3Ks88+y5gxYwAoKCjgsccei8Jb0H86Vmcc7kyI\nbEtOtBE2TVoDIRIdvQqHiIiIiAxQPWZze/bs4fjx45SUlHDkyBFWrFhBSUlJZP/q1at58cUXGTly\nJAsWLOD++++nrq6uyzabNm1i/vz5PPDAA2zcuJEtW7YwdepUDh06RElJCaFQiAceeIC/+qu/AmD2\n7NkUFxdH7+77WWR1xmGdR64BzrUElVyLiIiIDHI9loV4vV5mzZoFQG5uLg0NDfj9fgAqKipIS0sj\nMzMTi8XCtGnT8Hq93bYpLy9n5syZAMyYMQOv10t2djabNm0CoKGhAcMwcDqdUbnZWOt4mHF4yoWR\n6ySt0igiIiISN3ocKq2trSUvLy/y2uVy4fP5cDqd+Hw+XC5Xp30VFRXU19d32aa5uRmHo33UNiMj\nA5/PFzlm9erVvPHGGxQXFzNs2DCgfdR80aJFBINBiouLue22267Y1/T0ZGw2ay9vPbrc7pTLtn1c\n2QjAqJGpkf0j0pMBcCQ6umwjA4fiE18Uz/ijmMYXxTP+DJWYXnUdwrU8eNdVm0u3PfXUUyxZsoSF\nCxeSn5/PpEmTcLlcTJ8+nX379lFcXMzrr79+xevU15+76r5Fg9udgs939rLtJ6oaALBhXtgfCgNQ\nVd2IJ8VxWRsZGLqLqQxOimf8UUzji+IZf+Itplf6otBjWYjH46G2tjbyuqamBrfb3eW+6upqPB5P\nt22Sk5NpaWnpdGxVVRUHDhwAIC0tjfz8fA4cOEBubi7Tp08HYPLkydTV1REKha7itgeeyAIyKgsR\nERERiUs9JteFhYXs3LkTgIMHD+LxeCI10dnZ2fj9fiorKwkGg5SWllJYWNhtm4KCgsj2Xbt2MWXK\nFOrq6li1ahXBYJBQKMTBgwfJycnhhRdeYPv27QAcOnQIl8uF1TowSj6uVYP/yg80ioiIiMjg1mNZ\nSH5+Pnl5ecydOxfDMFi5ciVbt24lJSWFoqIiVq1axdKlS4H22T1ycnLIycm5rA3AkiVLKC4upqSk\nhKysLObMmYPdbue+++5j3rx5kan4JkyYQHp6Ok8++SSvvfYawWCQNWvWRPed6AdnOlZnvGQqPtDI\ntYiIiEg8MMw4Wr1koNTydFdX9KMX/kRjUxvP/X9TI9uOnGpgzb++x1e+OIaHvnxzf3ZTrkK81YoN\ndYpn/FFM44viGX/iLabXVXMtfeeMv61TvTVcVBbSGohFl0RERESkDym57ietgRDNrcFO9dYAyYl2\nAM61Du6HNUVEREREyXW/iTzM6Lx05Lr9Ic3mFo1ci4iIiAx2Sq77SVcPMwLYbVbsNoseaBQRERGJ\nA1e9iIz0bIf3GGf9LZ22HatqX53R19DMm38+2Wmf1WJQ29By2fbBYvodo2LdBREREZEBQSPX/aRj\nZLrjAcaLOexWAsFwf3dJRERERPqYkut+0nz+gcWkrpJrm4W2QPialpYXERERkYFDyXU/aT4/cp2U\ncPkqkw67hbBpEgoruRYREREZzJRc95MrloXY2hPutoBKQ0REREQGMyXX/aS5JYjDbsFqvfwtt9va\nt7UFNde1iIiIyGCm5LqfNLcGuxy1hvYHGgECGrkWERERGdSUXPeDYChMWzDc5cOM0P5AI2jkWkRE\nRGSwU3LdDy48zNjdyHVHcq2RaxEREZHBTMl1PwiG2pPmjtrqS+mBRhEREZH4oOS6H4RC7VPsWS1G\nl/vtdpWFiIiIiMQDJdf9oGP+6u6Sa41ci4iIiMSHrouAL7F27Vr279+PYRisWLGCiRMnRvaVlZWx\nceNGrFYrU6dOZfHixd22qaqqYtmyZYRCIdxuN+vXr8fhcPDzn/+ct956C9M0mT59Oo8//jiBQIDl\ny5dz6tQprFYrTz/9NKNHj47OuxBlkeS6i2n44ELNdUAj1yIiIiKDWo8j13v27OH48eOUlJSwZs0a\n1qxZ02n/6tWree6559i8eTO7d+/m8OHD3bbZtGkT8+fP59VXX2Xs2LFs2bKFyspKDh06RElJCZs3\nb+a3v/0t1dXVbN++ndTUVDZv3syjjz7Khg0bovMO9IOO5NqikWsRERGRuNZjcu31epk1axYAubm5\nNDQ04Pf7AaioqCAtLY3MzEwsFgvTpk3D6/V226a8vJyZM2cCMGPGDLxeL9nZ2WzatAmAhoYGDMPA\n6XTi9XopKioCoKCggL179/b93feTcE9lIZotRERERCQu9FgWUltbS15eXuS1y+XC5/PhdDrx+Xy4\nXK5O+yoqKqivr++yTXNzMw6HA4CMjAx8Pl/kmNWrV/PGG29QXFzMsGHDqK2tjZzbYrFgGAZtbW2R\n9l1JT0/Gdn4UOKYOnybFmRh5abc3AzAsydFpewfTNLEY7SPcXe0f6NzulFh3oV8MlfscKhTP+KOY\nxhfFM/4MlZj2qub6YqZpXvVFumpz6bannnqKJUuWsHDhQvLz86/puvX15666b9Fy1t8S+f+mc60A\nBALBTtsvZrdZaWntfv9A5vOdjXUXos7tThkS9zlUKJ7xRzGNL4pn/Im3mF7pi0KPZSEej4fa2trI\n65qaGtxud5f7qqur8Xg83bZJTk6mpaWl07FVVVUcOHAAgLS0NPLz8zlw4AAejycysh0IBDBN84qj\n1gNZT7OFQHtpiKbiExERERncekyuCwsL2blzJwAHDx7E4/HgdDoByM7Oxu/3U1lZSTAYpLS0lMLC\nwm7bFBQURLbv2rWLKVOmUFdXx6pVqwgGg4RCIQ4ePEhOTg6FhYXs2LEDgNLSUu65556ovAH9oafZ\nQqD9oUY90CgiIiIyuPVYFpKfn09eXh5z587FMAxWrlzJ1q1bSUlJoaioiFWrVrF06VIAZs+eTU5O\nDjk5OZe1AViyZAnFxcWUlJSQlZXFnDlzsNvt3HfffcybNy8yFd+ECRMYN24cZWVlzJs3D4fDwbp1\n66L7TkRRb0euQ2GTUDiM1aLpx0VEREQGI8O8liLqAWqg1PK8d/h0p9rpfZ/WcuDIae7/4mhGupK7\nbPPmvpOcqPbzrRm5JCVcdSl8TE2/Y1SsuxB18VYrNtQpnvFHMY0vimf8ibeYXlfNtVy/cLi93KO7\nea4BHPb2WU4Cmo5PREREZNBSct0PQqFelIXYzs91HdBDjSIiIiKDlZLrfnCh5voKDzSeH7nWQjIi\nIiIig5eS637QqwcabVqlUURERGSwU3LdDy5Mxdd9cm1XWYiIiIjIoKfkuh/0bio+lYWIiIiIDHZK\nrvtBKNSeMPemLCSgkWsRERGRQUvJdT8Inx+57s1UfBq5FhERERm8lFz3g1DYxGIYGIam4hMRERGJ\nZ0qu+0EobF7xYUYAu12zhYiIiIgMdkqu+0EobF6x3hrAbrVgAG0BJdciIiIig5WS634QDptXrLcG\nMAwDu91CIKiyEBEREZHBSsl1PwiFwz2OXAM4bFaNXIuIiIgMYkqu+0Eo1HNZCIDDbqFNI9ciIiIi\ng5aS637QXnPd81ttt1kIhszI1H0iIiIiMrgouY4y0zR7NVsItJeFgGYMERERERmslFxHWccgdG/L\nQgA91CgiIiIySNl6c9DatWvZv38/hmGwYsUKJk6cGNlXVlbGxo0bsVqtTJ06lcWLF3fbpqqqimXL\nlhEKhXC73axfvx6Hw8Ebb7zBSy+9hMVi4d577+X73/8+W7du5dlnn2XMmDEAFBQU8Nhjj0XhLYiu\nULh9FLqn2UIAkhzt4fA3B0hJdkS1XyIiIiLS93pMrvfs2cPx48cpKSnhyJEjrFixgpKSksj+1atX\n8+KLLzJy5EgWLFjA/fffT11dXZdtNm3axPz583nggQfYuHEjW7Zs4Rvf+AbPPPMM27ZtY9iwYTz0\n0EN87WtfA2D27NkUFxdH7+77QUf9dG9GrkcMTwTAd6aFzIxhUe2XiIiIiPS9HstCvF4vs2bNAiA3\nN5eGhgb8fj8AFRUVpKWlkZmZicViYdq0aXi93m7blJeXM3PmTABmzJiB1+slKSmJbdu24XQ6MQyD\n4cOHc+bMmWjdb78LhXqfXLuHJwHgq2+Oap9EREREJDp6HLmura0lLy8v8trlcuHz+XA6nfh8Plwu\nV6d9FRUV1NfXd9mmubkZh6O93CEjIwOfzweA0+kE4JNPPuHkyZNMmjSJEydOsGfPHhYtWkQwGKS4\nuJjbbrvtin1NT0/Gdv6hwJg6fJoUZ/sodMhsT6oTE+yRbd1JcUKa00FtQwvOYQkYRs8J+UDgdqfE\nugv9Yqjc51CheMYfxTS+KJ7xZ6jEtFc11xczzaufJq6rNpduO3bsGD/84Q/ZsGEDdrudSZMm4XK5\nmD59Ovv27aO4uJjXX3/9iteprz931X2LlrP+FgAa/a0AhMPhyLYryUhN5LNTjVR+3sjwlISo9rGv\n+HxnY92FqHO7U4bEfQ4Vimf8UUzji+IZf+Itplf6otBjcu3xeKitrY28rqmpwe12d7mvuroaj8eD\n3W7vsk1ycjItLS0kJiZGjgX4/PPPWbx4MT/96U+ZMGEC0F5OkpubC8DkyZOpq6sjFAphtQ6Akemr\nELqKmmsAz/AkPjvVSM2Z5kGTXIuIiIhIux5rrgsLC9m5cycABw8exOPxRMo4srOz8fv9VFZWEgwG\nKS0tpbCwsNs2BQUFke27du1iypQpAPzoRz9i1apVnUpJXnjhBbZv3w7AoUOHcLlcgy6xhguzhfQ2\nuXanq+5aREREZLDqceQ6Pz+fvLw85s6di2EYrFy5kq1bt5KSkkJRURGrVq1i6dKlQPvsHjk5OeTk\n5FzWBmDJkiUUFxdTUlJCVlYWc+bM4ejRo7z77rts2rQpcs1HHnmEr33tazz55JO89tprBINB1qxZ\nE6W3ILo6HmjszVR8AMOdDuw2CzVnlFyLiIiIDDaGeS1F1APUQKnlee/w6Uh9dWWNnz/sPUn+uBF8\n4aaMXrX//buVnKpt4lszcklKuOqy+H43/Y5Rse5C1MVbrdhQp3jGH8U0viie8SfeYnqlmmut0Bhl\nF2que/9WeyLzXWv0WkRERGQwUXIdZZHk2tr7afUidddnep5dREREREQGDiXXUXa1s4UAjEhLwgBq\n9FCjiIiIyKCi5DrKOmYL6e0DjQB2m4X01ARON7ZE2ouIiIjIwKfkOsrCV7H8+cXcw5MIh03qGlqj\n0S0RERERiQIl11F2LQ80QvtiMoCm5BMREREZRJRcR9m11FzDxQ81KrkWERERGSyUXEfZtcwWAjAs\n0UZygo2a+mbiaCpyERERkbim5DrKrnb58w6GYeBOT6KlLYS/ORCNromIiIhIH1NyHWXh8NUtf36x\nSN21puQTERERGRSUXEdZ6BpnCwHVXYuIiIgMNkquo+xaZwsBcKUkYLUYGrkWERERGSSUXEfZtc4W\nAu2lJJ70JM7426iuP9fXXRMRERGRPqbkOsqudbaQDpNuHgHAux/7NGuIiIiIyACn5DrKrmX584t5\n0pO48YYUTje0cLSqsS+7JiIiIiJ9TMl1lIVCJhYDLMa1JdcA+ePdWCwGew/VEgyF+7B3IiIiItKX\nbL05aO3atezfvx/DMFixYgUTJ06M7CsrK2Pjxo1YrVamTp3K4sWLu21TVVXFsmXLCIVCuN1u1q9f\nj8Ph4I033uCll17CYrFw77338v3vf59AIMDy5cs5deoUVquVp59+mtGjR0fnXYiisGle86h1B2eS\nndtuTOeDz+o4eLQuUioiIiIiIgNLjyPXe/bs4fjx45SUlLBmzRrWrFnTaf/q1at57rnn2Lx5M7t3\n7+bw4cPdttm0aRPz58/n1VdfZezYsWzZsoXm5maeeeYZ/uVf/oWSkhLKyso4fPgw27dvJzU1lc2b\nN/Poo4+yYcOG6LwDURYKmdc0U8ilbr8pg0SHlYNH6zjXokVlRERERAaiHrM+r9fLrFmzAMjNzaWh\noQG/3w9ARUUFaWlpZGZmYrFYmDZtGl6vt9s25eXlzJw5E4AZM2bg9XpJSkpi27ZtOJ1ODMNg+PDh\nnDlzBq/XS1FREQAFBQXs3bs3Km9AtIXC5jU/zHgxu83C5FtGEAyZ7DtU2wc9ExEREZG+1mNZSG1t\nLXl5eZHXLpcLn8+H0+nE5/Phcrk67auoqKC+vr7LNs3NzTgcDgAyMjLw+XwAOJ1OAD755BNOnjzJ\npEmT+MUvfhE5t8ViwTAM2traIu27kp6ejM1mvZr7j47Dp0lxJgLtZSE2qyXy+nrccetIDlU2cORU\nI/kTRuJJT77uc/YFtzsl1l3oF0PlPocKxTP+KKbxRfGMP0Mlpr2qub7YtUwH11WbS7cdO3aMH/7w\nh2zYsAG73X5N160fQHNBn/W3ABAMhXHYLJHX1yt/3Ah+904lb+w+yt0TPIz2tI/4x5LPdzam1+8P\nbnfKkLjPoULxjD+KaXxRPONPvMX0Sl8UeiwL8Xg81NZeKEOoqanB7XZ3ua+6uhqPx9Ntm+TkZFpa\nWjodC/D555+zePFi1q1bx4QJEyLn7hjZDgQCmKZ5xVHrgSocNq9pAZnuZGYMY9LNGZxrDfLmvlP8\n/t1KGvytfXZ+EREREbl2PSbXhYWF7Ny5E4CDBw/i8XgiZRzZ2dn4/X4qKysJBoOUlpZSWFjYbZuC\ngoLI9l27djFlyhQAfvSjH7Fq1apOpSSFhYXs2LEDgNLSUu65554+vO3+EwqZWPrggcaLTbp5BF8v\nvJHMjGSqTp9j2+5jvPtxDS1toT69joiIiIhcnR7LQvLz88nLy2Pu3LkYhsHKlSvZunUrKSkpFBUV\nsWrVKpYuXQrA7NmzycnJIScn57I2AEuWLKG4uJiSkhKysrKYM2cOR48e5d1332XTpk2Raz7yyCPM\nnj2bsrIy5s2bh8PhYN26dVF6C6InHDYxubalz3uS5kxg1l3ZVNT4efdjHx8eq+fj4/Vke5zclJXK\nKLczKtcVERERke4ZZhytqT1QanneO3yas/4WAsEwm3//KaPcw5h5Z3bUrhcMhTl04gyHTzZwxt8G\nQILdSk5mCrfluHAmXV7D3pem3zEqqucfCOKtVmyoUzzjj2IaXxTP+BNvMb1SzfVVP9Aovdex9Hm0\nR5BtVgu35biYcGM6dWdb+exkI0erGvn4xBkOVTRw69jhfOH8PNkiIiIiEj1KrqMoFG7/o8D1rtDY\nW4ZhkJGaSEZqIneOd3O0qpE/f1rLh8fq+bSygS+cT8BtVq16LyIiIhINSq6jKHw+uY5F7bPFYpA7\nKo0bM1P45MQZDhypY9+ntXx84gyTbs7g5lFp/Zb0i4iIiAwVGsKMolCoI7mO3dtstVi47UYX35ia\nw+03uWgLhPjTwWq27T7Gieqz1zRvuYiIiIh0TSPXURSK4cj1pRx2K5PHuRk/Jp39h2s5fLKBN/ed\nYkRaewnJSNfAWO1RREREZDDTyHUUDaTkukNyoo17v3ADXy+8kTEjndQ2tLBzTwV/eK+S+rNajEZE\nRETkemjkOoois4VYB05y3SHNmcD0yaPwnWnmvU98VPqaOOlr4qZRqdxx8wiGRXn6PhEREZF4pOQ6\nigbiyPWl3MOTuP+Loznpa2LvIR9HTjZytOos40cPJy/HRXKifkREREREekuZUxR1PNA40GflMAyD\nbI+TLPcwjp5qZN+ntXx0vJ5PKs5wS3YaX8hxaSRbREREpBeUXEfRhan4Bkdpu8W4MH3fkZONfPBZ\nHZ+cOMOnFWfIHZXGHbeMIClBPzIiIiIi3VGmFEWDoSykK1aLhXGjh3PzqDSOVjXy/pHTfFrZwKna\nJmbdlU2aMyHWXRQREREZkAbHkOogNZAfaOyNjoVo/nJKDnfcnEFTS5D/LD9Bdf25WHdNREREZEBS\nch1Fg3Xk+lIWw2DizSMovP0GAsEwv3unkuOfn411t0REREQGHCXXURQvyXWH3FFpzLwzG4sBf/zz\nKT46Vh/rLomIiIgMKEquo2ggLH/e17JGDOP+e8aQlGDlnY9r+OSEEmwRERGRDvGT9Q1A8TZy3SEj\nNZGv3DOGRIeVPR/VcKjiTKy7JCIiIjIg9Cq5Xrt2LQ8//DBz587l/fff77SvrKyMBx98kIcffpjn\nn3/+im2qqqpYuHAh8+fP54knnqCtrQ2AhoYGFi1axPe+971I+61btzJt2jQWLlzIwoUL+cd//Mfr\nvtn+1jEV30Cf5/papCQ7mDopC4Bf/PYD6hpbYtwjERERkdjrMbnes2cPx48fp6SkhDVr1rBmzZpO\n+1evXs1zzz3H5s2b2b17N4cPH+62zaZNm5g/fz6vvvoqY8eOZcuWLQCsXLmSO++887Jrz549m5df\nfpmXX36Zxx57rC/ut18N9tlCenJDRjJ3jffQ2NTG8//+AYFgKNZdEhEREYmpHpNrr9fLrFmzAMjN\nzaWhoQG/3w9ARUUFaWlpZGZmYrFYmDZtGl6vt9s25eXlzJw5E4AZM2bg9XqB9gS9q+R6sIvXspCL\n3Tp2OAVfuIGjVY28vOsQpmnGuksiIiIiMdNjcl1bW0t6enrktcvlwufzAeDz+XC5XJft665Nc3Mz\nDocDgIyMjMh5nE5nl9fes2cPixYt4jvf+Q4ffvjhNdxebA2F5NowDL59/3jG3pDC2+9X8ea+k7Hu\nkoiIiEjMXPUKjdcyMtlVm57OM2nSJFwuF9OnT2ffvn0UFxfz+uuvX7FNenoyNpv1qvvX5w6fJsWZ\niMVo/+6SlpJEYhwvGz4qazhZ0OdCAAAgAElEQVQ//u6X+MHP/sirv/+UgsnZZHtSYt2tPud2x989\nDWWKZ/xRTOOL4hl/hkpMe8z4PB4PtbW1kdc1NTW43e4u91VXV+PxeLDb7V22SU5OpqWlhcTExMix\n3cnNzSU3NxeAyZMnU1dXRygUwmrtPnmuH0ArB571t9DaFgTgXHMbgUAwxj2KHp/vLAawoGgcz//7\nB/zvfz/A4m/eHutu9Sm3OwWfTwvnxAvFM/4opvFF8Yw/8RbTK31R6LEspLCwkJ07dwJw8OBBPB5P\npIwjOzsbv99PZWUlwWCQ0tJSCgsLu21TUFAQ2b5r1y6mTJnS7XVfeOEFtm/fDsChQ4dwuVxXTKwH\noqFQFnKx/HFuckel8t4hH4dPNsS6OyIiIiL9rseR6/z8fPLy8pg7dy6GYbBy5Uq2bt1KSkoKRUVF\nrFq1iqVLlwLts3vk5OSQk5NzWRuAJUuWUFxcTElJCVlZWcyZM4dQKMQjjzxCY2Mj1dXVLFy4kMcf\nf5yvfe1rPPnkk7z22msEg8HLZikZDELhMIYRn1PxdcUwDL41/WbW/XovW0oPU/z/5GMYQ+PeRURE\nRAAMM46mdxgof2547/Bpzvpb2F52jMamNuYXjYt1l6Jq+h2jOr3etOV9/ny4lu/91UTuuGVEjHrV\nt+Ltz1lDneIZfxTT+KJ4xp94i+l1lYXItQuFzbha+ry3/mp6LoYBW/54JDLXt4iIiMhQMPQyv34U\nDptDpiTkYqNGDOMvbs/kVG0TZQc+j3V3RERERPqNkusoCoXMIfMw46XmTLkJu83Cb98+SmtAKzeK\niIjI0KDkOopCYTNulz7vSXpKAkV3jab+bCv/973KWHdHREREpF8ouY6iUDg8ZEeuAWZ/aQzDEm28\n4T1Oc2v8zvMtIiIi0kHJdRS1P9A4dJPr5EQ7RXeP5lxrkP/afyrW3RERERGJOiXXURIOm5gmQ3K2\nkIt9OT8bh93CrncqCIY0c4iIiIjEt6Gd+UXRUFudsTvOJDtTJ2VRf7aV8g+rY90dERERkahSch0l\n4fPJ9VCciu9S9989BqvF4D/LTxCOnzWLRERERC6j5DpKNHJ9QUZaIl+cMJJTtU28f/h0rLsjIiIi\nEjVKrqOkY2XCoToV36UeuGcMAP9ZfjzGPRERERGJHiXXUaKR686yPU4m5mbwaWUDhysbYt0dERER\nkahQch0lF5JrvcUdOkav3/iTRq9FREQkPinzi5JwSA80Xmrc6OHkZqXy58O1nKptinV3RERERPqc\nkusoUVnI5QzD4Cv3jAVgx54TMe6NiIiISN9Tch0lSq67NnncCDzDkyj/sJqmlkCsuyMiIiLSp5Rc\nR4lmC+maxTCYNjmLQDDM7gOfx7o7IiIiIn2qV8n12rVrefjhh5k7dy7vv/9+p31lZWU8+OCDPPzw\nwzz//PNXbFNVVcXChQuZP38+TzzxBG1tbQA0NDSwaNEivve970XaBwIBli5dyrx581iwYAEVFRXX\nfbP9SSPX3fuL2zOxWS28ue8kphaVERERkTjSY3K9Z88ejh8/TklJCWvWrGHNmjWd9q9evZrnnnuO\nzZs3s3v3bg4fPtxtm02bNjF//nxeffVVxo4dy5YtWwBYuXIld955Z6fzbt++ndTUVDZv3syjjz7K\nhg0b+uqe+0UopNlCupOS7ODuW918XneOj4/Xx7o7IiIiIn2mx8zP6/Uya9YsAHJzc2loaMDv9wNQ\nUVFBWloamZmZWCwWpk2bhtfr7bZNeXk5M2fOBGDGjBl4vV6gPUG/NLn2er0UFRUBUFBQwN69e/vo\nlvuHlj+/shmTswEo3Xcyxj0RERER6Tu2ng6ora0lLy8v8trlcuHz+XA6nfh8PlwuV6d9FRUV1NfX\nd9mmubkZh8MBQEZGBj6fDwCn09nldTvObbFYMAyDtra2SPuupKcnY7NZe7ql6Dt8Gpu9vR/OYQ5S\nnIkx7lB0ud0pV91mxAgnN/7fT9n3aS3WBDuu1IH/Hl3LfcrApXjGH8U0viie8WeoxLTH5PpS11Ij\n21Wbqz1Pb46vrz93VeeMpnPN7fXkbW1BzvpbYtyb6PL5zl5TuykTM3l5ZyP//odDfL0wp4971bfc\n7pRrvk8ZeBTP+KOYxhfFM/7EW0yv9EWhx7IQj8dDbW1t5HVNTQ1ut7vLfdXV1Xg8nm7bJCcn09LS\n0unYK123Y2Q7EAhgmuYVR60HGj3Q2LMv3TaSBIeVP/75VGR2FREREZHBrMfkurCwkJ07dwJw8OBB\nPB5PpIwjOzsbv99PZWUlwWCQ0tJSCgsLu21TUFAQ2b5r1y6mTJlyxevu2LEDgNLSUu65557ru9N+\npuS6Z0kJNu7Nu4H6s628f/h0rLsjIiIict16LAvJz88nLy+PuXPnYhgGK1euZOvWraSkpFBUVMSq\nVatYunQpALNnzyYnJ4ecnJzL2gAsWbKE4uJiSkpKyMrKYs6cOYRCIR555BEaGxuprq5m4cKFPP74\n48yePZuysjLmzZuHw+Fg3bp10X0n+phmC+mdGZNH8ea+k5TuO8nkce5Yd0dERETkuhhmHE00PFBq\ned47fJrflR/nUMUZvl54I8NTEmLdpaiafseo62q/9uX3OHyygXV/8yU86cl91Ku+FW+1YkOd4hl/\nFNP4onjGn3iL6XXVXMu10VR8vTdjcntyrmn5REREZLBTch0lWv689+661UPqMAf/tb+KlrZgrLsj\nIiIics2UXEeJHmjsPbvNwpcnj6K5NcjuA5/HujsiIiIi10zJdZRcSK71FvfG9MmjsFkNfvduBeH4\neQxAREREhhhlflGikeurkzrMwZduu4Ga+mbeP6Jp+URERGRwUnIdJaGQiQEYyq17bdZd2QD87p2K\nGPdERERE5NoouY6ScDiM1WpgKLvutTEjU5gwNp2PjtdTWeOPdXdERERErpqS6ygJhU1Nw3cNiu4a\nDcDv3tXotYiIiAw+Sq6jJBQ2VW99DSbenIFneBLeg9U0nmuLdXdEREREroqS6yhpT6719l4ti2Ew\n665sgqEwf9SiMiIiIjLIKPuLkrBGrq9Z4e2ZJCVY+cPekwSC4Vh3R0RERKTXlFxHSSikmutrlZRg\nY8rELBqa2vAe1KIyIiIiMngouY6SUDiskevrcP8Xx2CzWnh99zGCIY1ei4iIyOCg5DoKTNMkbILV\nquT6WqWnJDBj8ihON7bw1vtVse6OiIiISK8ouY4Crc7YN2Z/aQwOm4XtZccIBEOx7o6IiIhIj5Rc\nR0FHGYNmC7k+ac4EvpyfTf3ZVv5rv0avRUREZOCz9eagtWvXsn//fgzDYMWKFUycODGyr6ysjI0b\nN2K1Wpk6dSqLFy/utk1VVRXLli0jFArhdrtZv349DoeDbdu28atf/QqLxcJDDz3Et771LbZu3cqz\nzz7LmDFjACgoKOCxxx6LwlvQ90KhoTVy/eafozdlXqrTjs1qsPW/jmBiYrPG5gtLijORs/4WAKbf\nMSomfRAREZGBr8fkes+ePRw/fpySkhKOHDnCihUrKCkpiexfvXo1L774IiNHjmTBggXcf//91NXV\nddlm06ZNzJ8/nwceeICNGzeyZcsW5syZw/PPP8+WLVuw2+08+OCDFBUVATB79myKi4ujd/dR0lEW\notlCrl+iw8atY9P54LM6DlWc4bYbXbHukoiIiEi3ehwG9Hq9zJo1C4Dc3FwaGhrw+/0AVFRUkJaW\nRmZmJhaLhWnTpuH1erttU15ezsyZMwGYMWMGXq+X/fv3c/vtt5OSkkJiYiL5+fns3bs3WvfbL0KR\nshAl133hthtd2K0WPvisTvNei4iIyIDW48h1bW0teXl5kdculwufz4fT6cTn8+FyuTrtq6iooL6+\nvss2zc3NOBwOADIyMvD5fNTW1l52Dp/Ph91uZ8+ePSxatIhgMEhxcTG33XbbFfuanp6MzWbt/d1H\nSfD8yHVSop0UZ2KMezP4pQCTxrl596NqjlX7yR/viU0/zsfS7U6JyfWlbymO8UcxjS+KZ/wZKjHt\nVc31xUzTvOqLdNWmu/N0bJ80aRIul4vp06ezb98+iouLef311694nfr6c1fdt2joGLkOhcKROl25\nPjdnpbD/Ux97P65htDuZRMdV/+hel4trrn2+s/16bel7bneK4hhnFNP4onjGn3iL6ZW+KPRYFuLx\neKitrY28rqmpwe12d7mvuroaj8fTbZvk5GRaWlp6PNbj8ZCbm8v06dMBmDx5MnV1dYRCg2M6Nk3F\n1/ccdisTczNoDYTYfeDza/qSJyIiIhJtPSbXhYWF7Ny5E4CDBw/i8XhwOp0AZGdn4/f7qaysJBgM\nUlpaSmFhYbdtCgoKItt37drFlClTmDRpEgcOHKCxsZGmpib27t3LXXfdxQsvvMD27dsBOHToEC6X\nC6s19iUfvaGa6+i47cZ0MjOSOelr4qPj9bHujoiIiMhlevzben5+Pnl5ecydOxfDMFi5ciVbt24l\nJSWFoqIiVq1axdKlS4H22T1ycnLIycm5rA3AkiVLKC4upqSkhKysLObMmYPdbmfp0qUsWrQIwzBY\nvHgxKSkpfO1rX+PJJ5/ktddeIxgMsmbNmui+E30oqJHrqDAMg7+YmMnru4+x9xMfI9OTyUhTTbuI\niIgMHIYZR39fHyi1PK+VHmFX+XG+OMHDrWPTY92duHOqtonfv1tJSrKd/1ZwI3Zb9Oe+1jzX8SXe\nav9EMY03imf8ibeYXlfNtVy9UPh8WYhVI9fRkDViGHk5Ls6eC1D+YXWsuyMiIiISoeQ6Ci6s0Ki3\nN1om3zKCEWmJfHaqkSMnG2LdHRERERFAyXVUBPVAY9RZLAZTJmVit1nwHqymum5gTMMoIiIiQ5uS\n6yjQVHz9IyXZwbQ7ssA0+cPek9Q1ak5xERERiS0l11HQURZiUXIddVkjhlE4MZNAMMzv363k7Lm2\nWHdJREREhjAl11GgBxr7V05mKl+c4KGlLcTv3qnkXEsw1l0SERGRIUrJdRTogcb+d+vYdCbmZuBv\nDvB/36ukLTA4VvMUERGR+KLsLwoiI9cqC+lXk27OYPyY4dSfbeUN73Fq6ptj3SUREREZYpRcR0Ew\npAcaY8EwDO6e4OG2G9NpPBdgR/kJ3vmohkAwHOuuiYiIyBCh5Po6+c40c/iSeZY1ch07FsPgrls9\nfOWe0aQm2/noeD2v7z7G56c1VZ+IiIhEn5Lr6/RvfzzCulf2UnW6KbJNs4XEnic9mf9WeCN5OS6a\nmgPseqeCt9+voqklEOuuiYiISBxTcn2d7r7VQ9g02bb7WGRbZJ5rzRYSUzarhTvHu3ng3jGkpyTw\n2alGfvtfR/nzp7UqFREREZGoUHJ9nfLHuRkz0smeD6up9PmBi1do1Ns7EIxIS+KrBWO59ws34LBb\neP/Iaf7jraMcOdkQKeERERER6QvK/q6TYRh8Y8pNmMB/vHUUuDByraqQgcNiGNySncacKTdx+00u\nWgIhdh/4nN+UHsH7wedU153DNM1Yd1NEREQGOVusOxAPJuZmkJuVynuHfBz//CyhUBirxcAwlF0P\nNHabhcnj3NwyejgfH6/naFUjn1Y28GllA8MSbdyYmcrYkU4y0hIVPxEREblqSq77gGEYzJl6Exte\n+zP//tZnhMKmZgoZ4JxJdu661UP+eDefnz7H0apGTnzu5+DROg4erSM5wcbokU5Ge5yMdCXHursi\nIiJDViAYYsubn+FvDjBv1i04k+yx7tIVKbnuI7eNTWfc6OG8f+Q0VouBw66Km8HAYhhkjRhG1ohh\n3HNbmFO1TZyo9lPp8/PJiTN8cuIMhgGpwxykJNlJHebAAEa5nWS7h5Ho0EdIREQkWmrPNPP8bz/g\n+OdnAfi08gyLv3E7Y29IiXHPuterzGDt2rXs378fwzBYsWIFEydOjOwrKytj48aNWK1Wpk6dyuLF\ni7ttU1VVxbJlywiFQrjdbtavX4/D4WDbtm386le/wmKx8NBDD/Gtb32LQCDA8uXLOXXqFFarlaef\nfprRo0dH513oA+211zn85NV9hMImFpUUDDo2q4UxI1MYMzKFcNikuv4cJ6r91DW2cPZcgAZ/G/ia\n+PBYPQAG4E5PYrTbySj3MFKSHSQn2EhKsJGUYCUpwUaaM4GUZLt+HkREZEgJhsKcqPbjsFvIGjHs\nsv2hcJg/f3qa0n2VfHS8nrwcF1+enM3E3IzIVMYffHaaX247SFNLkL+4PZPhKQlsLzvG2lfeY+F9\n4/mLiZn9fVu90mNyvWfPHo4fP05JSQlHjhxhxYoVlJSURPavXr2aF198kZEjR7JgwQLuv/9+6urq\numyzadMm5s+fzwMPPMDGjRvZsmULc+bM4fnnn2fLli3Y7XYefPBBioqKKC0tJTU1lQ0bNvD222+z\nYcMGfvazn0X1zbhe48ekk3djOgeP1WO1auR6MLNYDDIzhpGZ0f4LIcWZiK+uicamNm5wJXPS10RF\nzVkqavy8d8jHe4d83Z/LMEgdZifNmcCwRBuRNPt8wu2wWUhOtJGcYCc5sT05N4z2+dLDpkkoFCZs\nth/nsFtJdFhJsFuxWS2EwmGCIZNgKBx5kNZht5Bgs+KwW3HYLRgYkeNC4TChkInFYmCzWbBF/rW0\nXyt8/nphE5P2GvX2/6zYbRYsQDDcfr32/0wMA+xWCzarBZvNgt1qIRw2CXZcMxQmbJpYLRZsVqP9\nWJsFi8Ug1NGnsEko1H5Nm8XAajGwWi1YLQYmEA5fOC58/sur1WJgOX+sYTEwwyZhs/0XdjhsYppE\n9lvO/8f5c2GzUXummZBpYhgGFqN9dh+LAUZHSZcJJoDZ3q+O5107Hnw1TTDPH2Ry/thLGN38PwYY\nGFz8naujxt8w6PQzYkSOj3TrQt+66N/FfeSS7d1dL9K/jutddM0L17nwfnT0o6trXvpccOT8F128\nu3vs+KfjHi/cb/f32BEH02rldEPz+XMYV77uFa7Z2+t1Cnhv4nlJLK90vZ5+xixG++8Vi8Vo/+Ju\ngBlu//yGzfbPQcdxnX6uTSKfofbj2j9LkXNZ2u8hcq6wSai9E5d/li46V8dn02IxsBqXfy5DYZPg\n+WNN88JxHZ9xaD9Xx++xUChMc8iksbG5/XeBxYLV2n6uYKj9d0sodP53gcXAZjWwnT+XYRjtv5vC\nJsFgODJ7l81qiRxns7b/vgsGwwRCYQLBcKSc8+LfY1ZL+7nagu3HtAVDmOGLfy9acNgsmEBbIERb\nIExrsP1fq8Ug4fzvYMf535+BYJiWQIjWthCtbUECoTAOm5UEx4Xf6xaLQXNrkObW0Pl/gwAkJdpI\nclwYuGkLhmlqDtDUEsDfHKClNUSiw8qwJDvDEu0MS7LhsFk5e66NhqY2Gpva/w2FwqQOc5A6zEHa\nsARSh9kJBMOcbmihtrGF0w0t1DW2kpRgJSMtkRFpSWSkJpKabKfmTDMna5s46WviZK2fs00BbshI\nZtSIYYxyD2PUCCdh0+Tj4/V8dKKeTysaaA2EgPZSzEnj3Nw00smNmakc+Ow0f/zzKerPtgIwIi2R\nDz6r44PP6shITWT65CyCIZNtbx/FajX4zlfGM3VSFoZhcFNWKv/79Q956Y2POHKqgfmzxmG3Dayc\nq8fk2uv1MmvWLAByc3NpaGjA7/fjdDqpqKggLS2NzMz2bw7Tpk3D6/VSV1fXZZvy8nL+/u//HoAZ\nM2bw0ksvkZOTw+23305KSvvwfn5+Pnv37sXr9TJnzhwACgoKWLFiRd/ffRTMmXoTB4+9p5rrOJTo\nsJLoSCJsmmSOSCZzRDJ3T/DQ3BrkjL+N1kCIwPlfwoFgmNbAhV+Oza0hKmr87YmdiIjIIGYxDJIT\nbbx/5DTvHznd5TGZGcmMH5NOa1uQj0+cYff+U+y+aH+Cw8qM/FHMmDyKbLeTE9VneXPfSbwHq/m3\nP34GgCs1gcXfuJ2czNRIuztuHsGP/9+7eX7rAf7451NYLAYL7xsfzdu9aj0m17W1teTl5UVeu1wu\nfD4fTqcTn8+Hy+XqtK+iooL6+vou2zQ3N+NwOADIyMjA5/NRW1t72Tku3W6xWDAMg7a2tkj7rrjd\nsa+/cbtTeH1Ddqy7ISIiIjJouN0p3PmFrF4f+4vimVHu0bW76nH0a5kLuKs23Z3nareLiIiIiAwU\nPSbXHo+H2trayOuamhrcbneX+6qrq/F4PN22SU5OpqWlpcdjO7b7fO11rIFAANM0rzhqLSIiIiIS\naz0m14WFhezcuROAgwcP4vF4cDqdAGRnZ+P3+6msrCQYDFJaWkphYWG3bQoKCiLbd+3axZQpU5g0\naRIHDhygsbGRpqYm9u7dy1133UVhYSE7duwAoLS0lHvuuScqb4CIiIiISF8xzF7UWzzzzDO8++67\nGIbBypUr+fDDD0lJSaGoqIh33nmHZ555BoD77ruPRYsWddnm1ltvpaamhuLiYlpbW8nKyuLpp5/G\nbrezY8cOXnzxRQzDYMGCBXz9618nFArx1FNPcezYMRwOB+vWrYs8OCkiIiIiMhD1KrkWEREREZGe\nDayJAUVEREREBjEl1yIiIiIifaRXy59L711pqXgZWMrLy3niiSe45ZZbABg3bhzf/e53WbZsGaFQ\nCLfbzfr163E4HGzbto1f/epXWCwWHnroIb71rW8RCARYvnw5p06dwmq18vTTTzN69OgY39XQdOjQ\nIR5//HEeeeQRFixYQFVV1XXH8eOPP2bVqlUAjB8/PrIAlkTfpfFcvnw5Bw8eZPjw4QAsWrSI6dOn\nK56DyE9/+lPee+89gsEgf/M3f8Ptt9+uz+ggdmk8//CHP+gzejFT+kx5ebn513/916Zpmubhw4fN\nhx56KMY9kiv505/+ZC5ZsqTTtuXLl5tvvPGGaZqmuWHDBvPXv/612dTUZN53331mY2Oj2dzcbH71\nq1816+vrza1bt5qrVq0yTdM033rrLfOJJ57o93sQ02xqajIXLFhgPvXUU+bLL79smmbfxHHBggXm\n/v37TdM0zR/84Afmm2++GYO7G3q6imdxcbH5hz/84bLjFM/Bwev1mt/97ndN0zTNuro6c9q0afqM\nDmJdxVOf0c5UFtKHulsqXgaP8vJyZs5sX/VpxowZeL1e9u/fz+23305KSgqJiYnk5+ezd+9evF4v\nRUVFABQUFLB3795Ydn3IcjgcvPDCC3g8nsi2641jW1sbJ0+ejPzlqeMcEn1dxbMriufgcffdd/Ps\ns88CkJqaSnNzsz6jg1hX8QyFQpcdN5TjqeS6D9XW1pKenh553bGUuwxchw8f5tFHH2XevHns3r2b\n5ubmyGJFGRkZ+Hw+amtrcblckTYdcb14u8ViwTAM2traYnIfQ5nNZiMxMbHTtuuNY21tLampqZFj\nO84h0ddVPAFeeeUVvv3tb/P973+furo6xXMQsVqtJCcnA7BlyxamTp2qz+gg1lU8rVarPqMXUc11\nFJma5XBAu/HGG/nbv/1bHnjgASoqKvj2t7/d6dt3d/G72u0SW30RR8U2tv7yL/+S4cOHM2HCBP75\nn/+Zn//850yePLnTMYrnwPf73/+eLVu28NJLL3HfffdFtuszOjhdHM8PPvhAn9GLaOS6D11pqXgZ\neEaOHMns2bMxDIMxY8YwYsQIGhoaaGlpAaC6uhqPx9NlXDu2d3yzDgQCmKYZGYmR2EpOTr6uOLrd\nbs6cORM5tuMcEhv33nsvEyZMAODLX/4yhw4dUjwHmbfeeot/+qd/4oUXXiAlJUWf0UHu0njqM9qZ\nkus+dKWl4mXg2bZtGy+++CIAPp+P06dP881vfjMSw127djFlyhQmTZrEgQMHaGxspKmpib1793LX\nXXdRWFjIjh07ACgtLeWee+6J2b1IZwUFBdcVR7vdzk033cS7777b6RwSG0uWLKGiogJor6e/5ZZb\nFM9B5OzZs/z0pz/ll7/8ZWQ2CX1GB6+u4qnPaGdaobGPdbXsuwxMfr+fH/7whzQ2NhIIBPjbv/1b\nJkyYQHFxMa2trWRlZfH0009jt9vZsWMHL774IoZhsGDBAr7+9a8TCoV46qmnOHbsGA6Hg3Xr1pGZ\nmRnr2xpyPvjgA37yk59w8uRJbDYbI0eO5JlnnmH58uXXFcfDhw/z4x//mHA4zKRJk/i7v/u7WN/q\nkNBVPBcsWMA///M/k5SURHJyMk8//TQZGRmK5yBRUlLCc889R05OTmTbunXreOqpp/QZHYS6iuc3\nv/lNXnnlFX1Gz1NyLSIiIiLSR1QWIiIiIiLSR5Rci4iIiIj0ESXXIiIiIiJ9RMm1iIiIiEgfUXIt\nIiIiItJHlFyLiAxClZWVTJ06FYDly5fzm9/85rJjxo8fz4IFC1i4cCFz587lxz/+MfX19QB89NFH\n/MM//MMVr/Ef//Effd9xEZE4p+RaRCSO/cu//Asvv/wyv/71r8nKymLRokWEQiEmTJjA//yf/7Pb\ndqFQiF/84hf92FMRkfhgi3UHRESGui9/+cv89re/JTU1lSeeeCKyCIPP5+M73/kOd955J5999hlt\nbW1MmjSJp556qttzPffcc1RVVbF27dpO261WK48++ihvvvkmb7/9NomJifzsZz9j8+bN/OpXv2Lb\ntm0kJSWRmJjI+vXrWbduHSdPnuS///f/zksvvcSzzz6L1+sF4IYbbmD9+vXY7XbuvPNOHn30Ud56\n6y18Ph8/+9nPGD9+PPv372ft2rXY7XbS0tL4yU9+gtPpZOPGjezdu5eWlhbuvvtuli1bhmEYUX1/\nRUT6k0auRURi7N577+W9997DNE1Onz7daRnhSZMmMX78eH7961/zm9/8hrfffptDhw51eZ5/+7d/\n4+OPP75iucfkyZP55JNPOm3btGkTv/zlL3nllVf4zne+Q01NDUuWLMHlcvHSSy8RDAZJSkri1Vdf\n5bXXXuPs2bO8/fbbQPtKp+PGjeNf//Vf+epXvxopT3nyySf5h3/4B1555RXuvvtu/vjHP/Kf//mf\nVFdX88orr7BlyxZOnPlFj4UAAAMhSURBVDhBaWlpX7yFIiIDhkauRURirLCwkHfeeYfMzExuuukm\nGhsbqaqqory8nNmzZ/OnP/2Jhx9+GIfDgc/no76+nuTk5E7nKCsrY9++fezcuROr1drttc6ePcuI\nESM6bXvwwQf57ne/y/33389XvvIVcnJyqKysjOy32WxYLBbmz5+PzWbjs88+i9RuA3zpS18CICsr\ni+PHj1NXV0djYyPjxo0D4JFHHgH+//bt3qWRIA7j+De6WMR3lA0iIiJpxCqaiP+BhYUEFV8bLRRE\nGwsRLCzUZgsLI2ITUEhhkcZG0EpLQRyMtYiQRkixhU0IzHU5c0lxxy3oHc+nnNn9zexUD7u/hd3d\nXYwxLC4ulvfyeR0Rkf+BwrWIyBcbHR3l/PycSCRCPB7H933u7+8xxhCPx8nlcmQyGRzHIZlM1qzx\n/v5Ob28vl5eXTE1N1bzGWosxhvHx8Yrx7e1t8vk8t7e3rK2tsbW1RX9/f3n+4eGBbDZLNpslHA6z\nsbFRcf/nMG+tJRQKYa2tWr+hoYHp6WmWl5d/+2xERP41agsREfli7e3tWGu5u7sjkUgwPDzM1dUV\nrutSKBTo6+vDcRyen595e3ujWCxW1ZiYmMDzPE5OTnh5eamat9aSSqVoampiZGSkPO77PkdHR3R1\ndTE3N8f8/Dy5XI66ujpKpRIAhUKB7u5uwuEw+XweY0zNPXx+nra2Np6engBIp9NkMhmGhoa4ubkp\n102lUry+vv7N0YmIfDt6cy0i8g0kEgmur6+JRCK4rsvj4yMrKyuMjY2xurrKwsICsViMpaUl9vb2\nODw8rKrhui47Oztsbm5ycXEB/GzJ8H2fwcFBTk9PK34gbG1t5ePjg8nJSVpaWnAch/39fTo6Oujs\n7CSZTHJ2dkY6nWZ2dpZoNMr6+jrHx8cVIf1XnudxcHCA4zg0NzfjeR6NjY0YY5iZmaG+vp6BgQF6\nenqCPUgRkS8WsrW+3YmIiIiIyB9TW4iIiIiISEAUrkVEREREAqJwLSIiIiISEIVrEREREZGAKFyL\niIiIiARE4VpEREREJCAK1yIiIiIiAfkBOHcdc1sSqzYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd354f67438>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "r4X9CCHhUmu_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Remove the outliers\n",
        "\n",
        "#train\n",
        "train.drop(train[train['walkDistance']>=10000].index,inplace=True)\n",
        "\n",
        "\n",
        "#test\n",
        "test.drop(test[test['walkDistance']>=10000].index,inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "C34XenrJUnA9",
        "colab_type": "code",
        "outputId": "f6ae58ff-720b-4bfb-858a-97fb0971b78a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "cell_type": "code",
      "source": [
        "# plot rideDistance\n",
        "\n",
        "plt.subplots(figsize=(12,4))\n",
        "sns.distplot(train.rideDistance,bins=10)\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAAEGCAYAAACuBLlKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n9sZO+gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd35510c630>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "XJVVJhKHUnFm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Remove the outliers.\n",
        "\n",
        "#test\n",
        "\n",
        "train.drop(train[train.rideDistance >=15000].index, inplace=True)\n",
        "\n",
        "#test\n",
        "\n",
        "test.drop(test[test.rideDistance >=15000].index, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gYAeG3jgUnPo",
        "colab_type": "code",
        "outputId": "5cb6b4f4-4225-452d-b16f-e7068faa4842",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "cell_type": "code",
      "source": [
        "# swimDistance\n",
        "plt.subplots(figsize=(12, 4))\n",
        "sns.distplot(train.swimDistance,bins=10)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd354e88e80>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtoAAAEGCAYAAABb+jL6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzt3X10VPWB//HPnYckhhkgwZkQQDFS\nrGzKg1lqgVSikcCKXeoi0OBGjz1ItxWkpyc2QFYJPQehFJd1tVZXTxTkYTcrsjYcQewq/clKCkpc\nxLRbFlQMGGHyQJJJyMNM7u+PyJRAQpKZ3AzI+3WOkpk73/vw4ZL5ZPKduYZpmqYAAAAA9ClbtHcA\nAAAA+DqiaAMAAAAWoGgDAAAAFqBoAwAAABagaAMAAAAWcER7B8Lh89VHbdsJCfGqqWmM2vavZGQX\nGfILH9lFhvwiQ37hI7vIkF/4zs/O43GHvR5e0e4lh8Me7V24YpFdZMgvfGQXGfKLDPmFj+wiQ37h\n66vsKNoAAACABSjaAAAAgAUo2gAAAIAFKNoAAACABSjaAAAAgAUo2gAAAIAFKNoAAACABSjaAAAA\ngAUo2gAAAIAFrshLsEfTmyWfqd7fFO3d6De3Txge7V0AAAC4IvGKNgAAAGABijYAAABgAYo2AAAA\nYAGKNgAAAGCBHr0ZcvXq1Tp06JAMw1B+fr7GjRsXWrZv3z6tX79edrtdU6dO1aJFi7ocU1FRoeXL\nlysQCMjhcGjdunXyeDxKTU1VWlpaaJ0bNmyQ3W7v40MFAAAA+k+3RfvAgQM6fvy4ioqKdOzYMeXn\n56uoqCi0fNWqVSosLFRSUpJycnI0Y8YMVVdXdzrmqaee0rx58zRz5kxt2bJFL7/8svLy8uRyubRp\n0yZLDxQAAADoT90W7ZKSEk2bNk2SNGrUKNXW1srv98vlcqm8vFyDBg1ScnKyJCkjI0MlJSWqrq7u\ndExBQYFiY2MlSQkJCSorK7PquAAAAICo6naOdmVlpRISEkK3ExMT5fP5JEk+n0+JiYkXLetqTHx8\nvOx2u4LBoLZu3aq//du/lSS1tLQoNzdX2dnZevnll/vs4AAAAIBo6fUFa0zT7PVGzh8TDAaVl5en\nSZMmafLkyZKkvLw8zZo1S4ZhKCcnRxMnTtTYsWO7XF9CQrwcjijN4T5aJbcrLjrbjgKPx31Zr+9q\nQ37hI7vIkF9kyC98ZBcZ8gtfX2TXbdH2er2qrKwM3T59+rQ8Hk+ny06dOiWv1yun09nlmOXLl2vk\nyJFavHhxaPn8+fNDX0+aNElHjhy5ZNGuqWnsybFZ5mq6MqTPV99n6/J43H26vqsN+YWP7CJDfpEh\nv/CRXWTIL3znZxdJ4e526kh6erp2794tSSorK5PX65XL5ZIkjRgxQn6/XydOnFAgENCePXuUnp7e\n5Zji4mI5nU4tWbIktP5PPvlEubm5Mk1TgUBApaWlGj16dNgHBAAAAFwOun1FOy0tTampqcrOzpZh\nGCooKND27dvldruVlZWllStXKjc3V5I0c+ZMpaSkKCUl5aIxkrR161Y1Nzfr/vvvl9T+RsmVK1dq\n6NChmjNnjmw2mzIzMzt8fCAAAABwJTLMcCZdR1k0fw1y8GjVVTV15PYJw/tsXfwKKzLkFz6yiwz5\nRYb8wkd2kSG/8PXb1BEAAAAAvUfRBgAAACxA0QYAAAAsQNEGAAAALEDRBgAAACxA0QYAAAAsQNEG\nAAAALEDRBgAAACxA0QYAAAAsQNEGAAAALEDRBgAAACxA0QYAAAAsQNEGAAAALEDRBgAAACxA0QYA\nAAAsQNEGAAAALEDRBgAAACxA0QYAAAAsQNEGAAAALEDRBgAAACxA0QYAAAAsQNEGAAAALEDRBgAA\nACxA0QYAAAAsQNEGAAAALEDRBgAAACxA0QYAAAAsQNEGAAAALEDRBgAAACxA0QYAAAAsQNEGAAAA\nLODoyYNWr16tQ4cOyTAM5efna9y4caFl+/bt0/r162W32zV16lQtWrSoyzEVFRVavny5AoGAHA6H\n1q1bJ4/Ho+LiYm3cuFE2m03z5s3T3LlzrTlaAAAAoJ90+4r2gQMHdPz4cRUVFemJJ57QE0880WH5\nqlWr9Mwzz+jf/u3f9N577+no0aNdjnnqqac0b948bd68WVlZWXr55ZfV2NioZ599Vhs2bNCmTZu0\nceNGnTlzxpqjBQAAAPpJt69ol5SUaNq0aZKkUaNGqba2Vn6/Xy6XS+Xl5Ro0aJCSk5MlSRkZGSop\nKVF1dXWnYwoKChQbGytJSkhIUFlZmQ4dOqSxY8fK7XZLktLS0lRaWqrMzExLDhgAAADoD90W7crK\nSqWmpoZuJyYmyufzyeVyyefzKTExscOy8vJy1dTUdDomJSVFkhQMBrV161YtWrRIlZWVF63D5/Nd\ncp8SEuLlcNh7fpR96WiV3K646Gw7Cjwe92W9vqsN+YWP7CJDfpEhv/CRXWTIL3x9kV2P5mifzzTN\nXm/k/DHBYFB5eXmaNGmSJk+erB07dvR6/TU1jb3eh75U72+K6vb7k89X32fr8njcfbq+qw35hY/s\nIkN+kSG/8JFdZMgvfOdnF0nh7naOttfrVWVlZej26dOn5fF4Ol126tQpeb3eS45Zvny5Ro4cqcWL\nF3e5fq/XG/YBAQAAAJeDbot2enq6du/eLUkqKyuT1+uVy+WSJI0YMUJ+v18nTpxQIBDQnj17lJ6e\n3uWY4uJiOZ1OLVmyJLT+8ePH6/Dhw6qrq1NDQ4NKS0s1ceJEK44VAAAA6DfdTh1JS0tTamqqsrOz\nZRiGCgoKtH37drndbmVlZWnlypXKzc2VJM2cOVMpKSlKSUm5aIwkbd26Vc3Nzbr//vsltb9R8tz4\nBQsWyDAMLVq0KPTGSAAAAOBKZZjhTLqOsmjONzp4tOqqmqN9+4ThfbYu5opFhvzCR3aRIb/IkF/4\nyC4y5Be+fpujDQAAAKD3KNoAAACABSjaAAAAgAUo2gAAAIAFKNoAAACABSjaAAAAgAUo2gAAAIAF\nKNoAAACABSjaAAAAgAUo2gAAAIAFKNoAAACABSjaAAAAgAUo2gAAAIAFKNoAAACABSjaAAAAgAUo\n2gAAAIAFKNoAAACABSjaAAAAgAUo2gAAAIAFKNoAAACABSjaAAAAgAUo2gAAAIAFKNoAAACABSja\nAAAAgAUo2gAAAIAFKNoAAACABSjaAAAAgAUo2gAAAIAFKNoAAACABSjaAAAAgAUo2gAAAIAFHD15\n0OrVq3Xo0CEZhqH8/HyNGzcutGzfvn1av3697Ha7pk6dqkWLFl1yzCuvvKK1a9fqwIEDGjBggCQp\nNTVVaWlpoXVu2LBBdru9zw4SAAAA6G/dFu0DBw7o+PHjKioq0rFjx5Sfn6+ioqLQ8lWrVqmwsFBJ\nSUnKycnRjBkzVF1d3emY119/XVVVVfJ6vR224XK5tGnTpr4/OgAAACBKui3aJSUlmjZtmiRp1KhR\nqq2tld/vl8vlUnl5uQYNGqTk5GRJUkZGhkpKSlRdXd3pmGnTpsnlcmnHjh0WHhIAAAAQfd0W7crK\nSqWmpoZuJyYmyufzyeVyyefzKTExscOy8vJy1dTUdDomJSWl0220tLQoNzdXJ0+e1IwZM/TDH/7w\nkvuUkBAvhyNKU0uOVsntiovOtqPA43Ff1uu72pBf+MguMuQXGfILH9lFhvzC1xfZ9WiO9vlM0+z1\nRrobk5eXp1mzZskwDOXk5GjixIkaO3Zsl4+vqWns9T70pXp/U1S33598vvo+W5fH4+7T9V1tyC98\nZBcZ8osM+YWP7CJDfuE7P7tICne3nzri9XpVWVkZun369Gl5PJ5Ol506dUper/eSYzozf/58DRgw\nQPHx8Zo0aZKOHDkS1sEAAAAAl4tui3Z6erp2794tSSorK5PX65XL5ZIkjRgxQn6/XydOnFAgENCe\nPXuUnp5+yTEX+uSTT5SbmyvTNBUIBFRaWqrRo0f31fEBAAAAUdHt1JG0tDSlpqYqOztbhmGooKBA\n27dvl9vtVlZWllauXKnc3FxJ0syZM5WSkqKUlJSLxkjSc889p3379snn82nhwoWaMGGC8vLyNHTo\nUM2ZM0c2m02ZmZkdPj4QAAAAuBIZZjiTrqMsmvONDh6tuqrmaN8+YXifrYu5YpEhv/CRXWTILzLk\nFz6yiwz5ha/f5mgDAAAA6D2KNgAAAGABijYAAABgAYo2AAAAYAGKNgAAAGABijYAAABgAYo2AAAA\nYAGKNgAAAGABijYAAABgAYo2AAAAYAGKNgAAAGABijYAAABgAYo2AAAAYAGKNgAAAGABijYAAABg\nAYo2AAAAYAGKNgAAAGABijYAAABgAYo2AAAAYAGKNgAAAGABijYAAABgAYo2AAAAYAGKNgAAAGAB\nijYAAABgAYo2AAAAYAGKNgAAAGABijYAAABgAYo2AAAAYAGKNgAAAGABijYAAABggR4V7dWrV+sH\nP/iBsrOz9dFHH3VYtm/fPs2ZM0c/+MEP9Oyzz3Y75pVXXlFqaqoaGhpC9xUXF+vee+/V3Llz9eqr\nr0Z6TAAAAEDUObp7wIEDB3T8+HEVFRXp2LFjys/PV1FRUWj5qlWrVFhYqKSkJOXk5GjGjBmqrq7u\ndMzrr7+uqqoqeb3e0PjGxkY9++yz2rZtm5xOp+bMmaOsrCwNHjzYmiMGAAAA+kG3r2iXlJRo2rRp\nkqRRo0aptrZWfr9fklReXq5BgwYpOTlZNptNGRkZKikp6XLMtGnT9LOf/UyGYYTWf+jQIY0dO1Zu\nt1txcXFKS0tTaWmpFccKAAAA9Jtui3ZlZaUSEhJCtxMTE+Xz+SRJPp9PiYmJFy3raozL5ep0/Z2t\nAwAAALiSdTt15EKmafZ6I70Z05PHJiTEy+Gw93o/+sTRKrldcdHZdhR4PO7Len1XG/ILH9lFhvwi\nQ37hI7vIkF/4+iK7bou21+tVZWVl6Pbp06fl8Xg6XXbq1Cl5vV45nc4ux/Rk/RMmTLjkPtXUNHa3\n25aq9zdFdfv9yeer77N1eTzuPl3f1Yb8wkd2kSG/yJBf+MguMuQXvvOzi6Rwdzt1JD09Xbt375Yk\nlZWVyev1hqaAjBgxQn6/XydOnFAgENCePXuUnp5+yTEXGj9+vA4fPqy6ujo1NDSotLRUEydODPuA\nAAAAgMtBt69op6WlKTU1VdnZ2TIMQwUFBdq+fbvcbreysrK0cuVK5ebmSpJmzpyplJQUpaSkXDRG\nkp577jnt27dPPp9PCxcu1IQJE5SXl6fc3FwtWLBAhmFo0aJFcrv5NQcAAACubIYZzqTrKIvmr0EO\nHq26qqaO3D5heJ+ti19hRYb8wkd2kSG/yJBf+MguMuQXvn6bOgIAAACg9yjaAAAAgAUo2gAAAIAF\nKNoAAACABSjaAAAAgAUo2gAAAIAFKNoAAACABSjaAAAAgAUo2gAAAIAFKNoAAACABSjaAAAAgAUo\n2gAAAIAFKNoAAACABSjaAAAAgAUo2gAAAIAFKNoAAACABSjaAAAAgAUo2gAAAIAFKNoAAACABSja\nAAAAgAUo2gAAAIAFKNoAAACABSjaAAAAgAUo2gAAAIAFKNoAAACABSjaAAAAgAUo2gAAAIAFKNoA\nAACABSjaAAAAgAUo2gAAAIAFKNoAAACABRw9edDq1at16NAhGYah/Px8jRs3LrRs3759Wr9+vex2\nu6ZOnapFixZ1OaaiokJ5eXkKBoPyeDxat26dYmJilJqaqrS0tNA6N2zYILvd3seHCgAAAPSfbov2\ngQMHdPz4cRUVFenYsWPKz89XUVFRaPmqVatUWFiopKQk5eTkaMaMGaquru50zNNPP6377rtPd911\nl9avX69t27bpvvvuk8vl0qZNmyw9UAAAAKA/dTt1pKSkRNOmTZMkjRo1SrW1tfL7/ZKk8vJyDRo0\nSMnJybLZbMrIyFBJSUmXY/bv368777xTknTHHXeopKTEquMCAAAAoqrbV7QrKyuVmpoaup2YmCif\nzyeXyyWfz6fExMQOy8rLy1VTU9PpmLNnzyomJkaSNGTIEPl8PklSS0uLcnNzdfLkSc2YMUM//OEP\nL7lPCQnxcjiiNLXkaJXcrrjobDsKPB73Zb2+qw35hY/sIkN+kSG/8JFdZMgvfH2RXY/maJ/PNM1e\nb6SzMeffl5eXp1mzZskwDOXk5GjixIkaO3Zsl+urqWns9T70pXp/U1S33598vvo+W5fH4+7T9V1t\nyC98ZBcZ8osM+YWP7CJDfuE7P7tICne3U0e8Xq8qKytDt0+fPi2Px9PpslOnTsnr9XY5Jj4+Xk1N\nTR0eK0nz58/XgAEDFB8fr0mTJunIkSNhHxAAAABwOei2aKenp2v37t2SpLKyMnm9XrlcLknSiBEj\n5Pf7deLECQUCAe3Zs0fp6eldjpkyZUro/rfeeku33XabPvnkE+Xm5so0TQUCAZWWlmr06NFWHS8A\nAADQL7qdOpKWlqbU1FRlZ2fLMAwVFBRo+/btcrvdysrK0sqVK5WbmytJmjlzplJSUpSSknLRGEl6\n5JFHtHTpUhUVFWnYsGG655575HQ6NXToUM2ZM0c2m02ZmZkdPj4QAAAAuBIZZjiTrqMsmvONDh6t\nuqrmaN8+YXifrYu5YpEhv/CRXWTILzLkFz6yiwz5ha/f5mgDAAAA6D2KNgAAAGABijYAAABgAYo2\nAAAAYAGKNgAAAGABijYAAABgAYo2AAAAYAGKNgAAAGABijYAAABgAYo2AAAAYAGKNgAAAGABijYA\nAABgAYo2AAAAYAGKNgAAAGABijYAAABgAYo2AAAAYAGKNgAAAGABR7R34OvMNE2d9DXow/+rVII7\nVlPGDpXNMKK9WwAAAOgHFG2LnPE36/0/nVZFVaMkqaa+WU6HTbeO8cqgbAMAAHztUbT7WHNLUP9z\ntFJHys/INKXkIfEa/41r9YeyL/Xnz8/IdY1TqSmJ0d5NAAAAWIyi3cf+3/98oS+rG+WOd2rizV6N\n8AyQYRi6869HaNcfPtfBP/sUH+tQyrCB0d5VAAAAWIg3Q/ahuoYWfVndqKSEazTruym6zusKTRMZ\ncI1Td04cIafDpvcOV+jLr6aUAAAA4OuJot2HPvmiTpI0+rpBstsunoed4I7V7bcMkyTt+fCkauqb\n+3X/AAAA0H8o2n3ENE198kWdHHZD13ndXT4uecgATRmbrNZAm94+eEINTa39uJcAAADoLxTtPnL6\nzFn5z7bq+iS3nI5Lx3rjsIFKu+laNTYF9PYHJ9TSGuynvQQAAEB/oWj3kU9Otk8bubGHb3JMTUnU\nN68frDP+Fv3+wy8UbDOt3D0AAAD0M4p2HwgG2/TZl/WKj3Vo6JD4Ho0xDEPfHuPVdV6Xvqxu1L7D\nFTJNyjYAAMDXBUW7D5zwNag10KaUYQN7deVHm2HotvHJunZQnD6tqFfpkUo1tQTUGmijdAMAAFzh\n+BztPnDsZK2knk8bOZ/DblPmXw/Xrj98rrJPq1X2aXVomc0wFB/n0M3XD9bo6wZ3O/cbAAAAlw+K\ndoSaWgI6WdmgxIGxSnDHhrWOuBiHsiZep8OfVKm5Nahg0FSgrU3BoKkz/mZ98GefPv60WmNuSNDN\n1ydQuAEAAK4AFO0IfVpRL9MM79Xs87ninZr8raEX3d/UEtT/Hq/Rn47X6MMjlSr7tFojk9yKi7Er\nxmlXjMOmGKddAwfEaJArpldTVwAAAGCdHhXt1atX69ChQzIMQ/n5+Ro3blxo2b59+7R+/XrZ7XZN\nnTpVixYt6nJMRUWF8vLyFAwG5fF4tG7dOsXExKi4uFgbN26UzWbTvHnzNHfuXGuONgKBYJs++PNp\n1TYGFGNX6IqPn3xRJ8OQUpKtuaR6XIxdE0Zfq7+6IUH/+/kZ/fGzav3fidpOH+t02HTtoDh5E67R\ntYOukdNhk2maajNNtbW1P8bhMBTjsMvpsCnGYZPD/pdXx8/NCrcZfzk+AAAAhKfbon3gwAEdP35c\nRUVFOnbsmPLz81VUVBRavmrVKhUWFiopKUk5OTmaMWOGqqurOx3z9NNP67777tNdd92l9evXa9u2\nbbrnnnv07LPPatu2bXI6nZozZ46ysrI0ePBgSw+8t/78+Rm9UPxHSdI1sXYNu3aAEgfGqaq2ScOv\nHaBrYq395UCM065xo4bor25IUH1jq1pag2oJtKmlNajm1qBq6pvlqzmriqpGVUR4eXfDaC/4cTEO\nHfyzTwPjY2S3GwoG2xQImgq2mQoG2+Sw2+R02uS020Ll3fiqpLf/2T7PvL3U25WQEK/msy2y24z2\nUm9K5lf13m5rL/1Oh01OuyHHV9Njzr0n9NybQ+12m+w2o/0/u002Q2oz25ebptTWZsowJLvNkO2r\nx9mM9u21/8DR/p9pSvpqX89dxNP21X7LMGTT+cfR8c9z2wn9EHPe+1aNC/4894Xx1Rfn//xy/g8z\nhvHVQ7+6z+gwtl2Mv1n1jS266G2y5iVv/iXErpZ34qIfsy74wevi5d2M76FevQW4Fw92xjWrrqGl\nt7vTuT76GfRy+VG2JzHG+JtV19hNfpfb+7f7IOC++js692+3Oz2OsBdZ9/VfS48y6eZBvcm1w7l3\nqe91l/g+d6nPFjAu+D7b4XvweV+cu22G/nfuazO0rQufrzrbdmh7Pfj+b3y1DbOt/Xn3/Ocbm9H+\nvGkzJMNmhJ6Xgm1t7c9zZvtjTLtdZ+qbZbO1P38Fg+3P38E2U4Fgm0y1v1fMbjNCf5qmqdZAm1qD\nbWoNtD/WZrQ/LzvthpwOu+w2Qy2BoFpa29TcGlRLa1CBNlOxDrtinDbFOtt/626aphqbAmpoalVj\nc0CNTQHZbYbi45yKj3NoQJxDcTEO1Te2qLquWVV1Taqua1J9Y6sGuWKUODBOQwbGKnFgnDyDr1Gs\n0971X+Zlqtt2WFJSomnTpkmSRo0apdraWvn9frlcLpWXl2vQoEFKTk6WJGVkZKikpETV1dWdjtm/\nf79+8YtfSJLuuOMOvfTSS0pJSdHYsWPldrdfTTEtLU2lpaXKzMy05IDD9Vc3JOjH30/VOx9+oc+/\nrNOxk+3/SZFPG+kNh912ybngTS0B+c40qaq2qb102tqL5LkpJef+4bS0BtUaaC/O5/5BnxMItqmp\nJaj6xhaVfcpl4gEAQHS545168uEpcjqurLLdbdGurKxUampq6HZiYqJ8Pp9cLpd8Pp8SExM7LCsv\nL1dNTU2nY86ePauYmBhJ0pAhQ+Tz+VRZWXnROnw+3yX3yePp+hLnVrrbO1B3T/1GVLYNAACA/tMX\nfbPXH18Rzuc7dzamq/Xw+dEAAAD4Oui2aHu9XlVWVoZunz59Wh6Pp9Nlp06dktfr7XJMfHy8mpqa\nun2s1+uN/MgAAACAKOq2aKenp2v37t2SpLKyMnm9XrlcLknSiBEj5Pf7deLECQUCAe3Zs0fp6eld\njpkyZUro/rfeeku33Xabxo8fr8OHD6uurk4NDQ0qLS3VxIkTrTpeAAAAoF8YZg/majz55JP64IMP\nZBiGCgoK9Mc//lFut1tZWVl6//339eSTT0qSpk+frgULFnQ65uabb9bp06e1dOlSNTc3a9iwYVqz\nZo2cTqfefPNNFRYWyjAM5eTkaNasWdYeNQAAAGCxHhVtAAAAAL3DtbwBAAAAC1C0AQAAAAtYeznD\nr5FLXYYe7fbv36+f/vSnGj16tCTppptu0kMPPaS8vDwFg0F5PB6tW7dOMTExKi4u1saNG2Wz2TRv\n3jzNnTs3ynsfPUeOHNHDDz+sBx98UDk5OaqoqOhxZq2trVq2bJm++OIL2e12rVmzRtddd120D6lf\nXZjfsmXLVFZWFrq67IIFC3T77beTXyd+9atf6eDBgwoEAvqHf/gHjR07lnOvFy7M75133uHc64Gz\nZ89q2bJlqqqqUnNzsx5++GHdfPPNnHs91Fl+u3fv5tzrhaamJn3ve9/Tww8/rMmTJ1t77pno1v79\n+80f/ehHpmma5tGjR8158+ZFeY8uT3/4wx/MRx55pMN9y5YtM3fu3Gmapmn+0z/9k7llyxazoaHB\nnD59ullXV2eePXvWvPvuu82amppo7HLUNTQ0mDk5OeZjjz1mbtq0yTTN3mW2fft2c+XKlaZpmube\nvXvNn/70p1E7lmjoLL+lS5ea77zzzkWPI7+OSkpKzIceesg0TdOsrq42MzIyOPd6obP8OPd65o03\n3jBfeOEF0zRN88SJE+b06dM593qhs/w493pn/fr15uzZs83XXnvN8nOPqSM90NVl6NG9/fv36847\n75Qk3XHHHSopKdGhQ4c0duxYud1uxcXFKS0tTaWlpVHe0+iIiYnRiy++2OGz43uTWUlJibKysiRJ\nU6ZMuepy7Cy/zpDfxb797W/rX/7lXyRJAwcO1NmzZzn3eqGz/ILB4EWPI7+LzZw5UwsXLpQkVVRU\nKCkpiXOvFzrLrzPk17ljx47p6NGjuv322yVZ/5xL0e6ByspKJSQkhG735DLxV6ujR4/qxz/+sebP\nn6/33ntPZ8+eVUxMjCRpyJAh8vl8qqysVGJiYmjM1Zynw+FQXFxch/t6k9n599tsNhmGoZaWlv47\ngCjrLD9J2rx5sx544AH97Gc/U3V1Nfl1wm63Kz4+XpK0bds2TZ06lXOvFzrLz263c+71QnZ2th59\n9FHl5+dz7oXh/Pwkvu/11Nq1a7Vs2bLQbavPPeZoh8HkExE7dcMNN2jx4sW66667VF5ergceeKDD\nKzxd5UaeXettZmQpff/739fgwYM1ZswYvfDCC/r1r3+tW265pcNjyO8v/uu//kvbtm3TSy+9pOnT\np4fu59zrmfPz+/jjjzn3euHf//3f9ac//Uk///nPOxw/517PnJ9ffn4+514PvP7665owYUKX86qt\nOPd4RbsHLnUZevxFUlKSZs6cKcMwdP311+vaa69VbW2tmpqaJEmnTp2S1+vtNM/ufvV/NYmPj+9x\nZl6vN/TbgNbWVpmmGfrJ/Go1efJkjRkzRpKUmZmpI0eOkF8X9u7dq+eff14vvvii3G43514vXZgf\n517PfPzxx6qoqJAkjRkzRsFgUAMGDODc66HO8rvppps493rg97//vd5++23NmzdPr776qn7zm99Y\n/n2Pot0Dl7oMPf6iuLhYhYXjtefLAAAGzklEQVSFkiSfz6eqqirNnj07lN1bb72l2267TePHj9fh\nw4dVV1enhoYGlZaWauLEidHc9cvKlClTepxZenq63nzzTUnSnj179J3vfCeau35ZeOSRR1ReXi6p\nfe7d6NGjya8T9fX1+tWvfqV//dd/DX1SAedez3WWH+dez3zwwQd66aWXJLVPzWxsbOTc64XO8lux\nYgXnXg889dRTeu211/Qf//Efmjt3rh5++GHLzz2uDNlDnV1SHh35/X49+uijqqurU2trqxYvXqwx\nY8Zo6dKlam5u1rBhw7RmzRo5nU69+eabKiwslGEYysnJ0axZs6K9+1Hx8ccfa+3atTp58qQcDoeS\nkpL05JNPatmyZT3KLBgM6rHHHtNnn32mmJgY/fKXv1RycnK0D6vfdJZfTk6OXnjhBV1zzTWKj4/X\nmjVrNGTIEPK7QFFRkZ555hmlpKSE7vvlL3+pxx57jHOvBzrLb/bs2dq8eTPnXjeampr0j//4j6qo\nqFBTU5MWL16sb33rWz1+rrias5M6zy8+Pl7r1q3j3OuFZ555RsOHD9d3v/tdS889ijYAAABgAaaO\nAAAAABagaAMAAAAWoGgDAAAAFqBoAwAAABagaAMAAAAWoGgDwGXoiSee0Mcff9zjx2dmZmru3Lm6\n//77NX/+fOXm5urkyZOS2j/XfsmSJZccv2PHDrW1tUW0zwCAjvh4PwD4GsjMzNTLL7+skSNHSmq/\n1PDTTz+t4uLiHl1ga/r06dq5c6ccDofVuwoAVw2+owKAxU6dOqVHH31UUvvFJubNm6ejR49q+fLl\nOnLkiGbNmqW3335bw4cP14oVKzRlyhRt2bJFP/nJT2S32/X8889r6NChOnz4sMaPH69vfvOb+t3v\nfqczZ87oxRdf1NChQy/a5j333KO9e/fqt7/9rTIyMnTffffp3Xff1c6dO1VYWKj4+HiZpqk1a9bo\nP//zP3X8+HE9+OCD+vWvf62dO3fqt7/9rZxOp2JjY/XP//zPGjhwoDIzM/XAAw/o3Xff1YkTJ/SL\nX/xCkydP1meffabHH39cbW1tio2N1Zo1a5SUlKRNmzZp165dCgaDuvHGG1VQUKC4uLj+jh8Aooap\nIwBgsV27dunGG2/Upk2btHnzZvl8Pn3wwQeS2i+XnJ6ervfff19S++WVp0yZ0mH8Rx99pKVLl+q1\n117Tjh07NHDgQG3atEmpqamhywF35pZbbtGRI0c63Pf8889rxYoV2rRpk37+85/r1KlToWklGzZs\n0ODBg9Xc3KzCwkJt3rxZw4cPV3FxcWh8bGysXnrpJf3kJz/RK6+8IkkqKCjQggULtGXLFt17773a\ntWuXPvroI/3ud7/Tli1bVFRUJLfbrVdffTXyMAHgCsIr2gBgsdtuu01bt27VsmXLlJGRoYULF2rX\nrl3y+/3av3+/HnroIe3YsUOTJ0/WwIEDNXDgwA7jR40apcGDB0uSBg8erFtuuUWSlJSUJL/f3+V2\n6+vrZbfbO9w3e/ZsLVu2TNOnT9f06dM1fvz4i8YNHjxYP/rRj2Sz2XTy5El5PJ7QsltvvVWSNGzY\nMNXW1kpq/0Hg3P133323JOnFF1/U559/rgceeECS1NjYyLQUAFcdvusBgMVGjRqlN954Q++//77e\nfPNNbdy4Ud/5znd08OBB+Xw+TZ48WU899VTo1e0LXViWz799qbfZlJaW6m/+5m863Pfggw/qe9/7\nnvbu3asVK1Zo7ty5ys7ODi3/8ssvtXbtWr3xxhsaMmSI1q5d22H8+WX5/G1f+EbKmJgYZWZmasWK\nFV3uHwB83TF1BAAstmPHDh0+fFhTpkxRQUGBKioqlJ6erq1bt+qmm26SJHm9Xu3cuVPf/e53+2Sb\n27Zt02effRZ6hVmSgsGgnnzySbndbv3d3/2dHnnkER06dEiSZBiGAoGAqqqqlJCQoCFDhujMmTP6\n7//+b7W0tFxyW2lpadq7d68kaefOnVq/fr3S0tL07rvvqqGhQZK0ZcsWffjhh31ybABwpeAVbQCw\n2De+8Q0VFBQoJiZGpmlq4cKFuvXWW7VkyRI98cQTkqSJEyfqN7/5jcaNGxf2dh599FHFxcXpzJkz\nGjlypDZu3NjhzYd2u10JCQnKzs4OTU957LHHJLVPb7n33nv13HPPaeTIkZozZ46uv/56LVmyRCtX\nrlRGRkaX23388cf1+OOPa+vWrXI4HFq9erWSk5P193//97r//vsVGxsrr9er2bNnh31sAHAl4uP9\nAAAAAAswdQQAAACwAEUbAAAAsABFGwAAALAARRsAAACwAEUbAAAAsABFGwAAALAARRsAAACwwP8H\nnltB4646jQIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd354eeb9b0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "SKe8SYAl7VsF",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Very few people prefer to swim."
      ]
    },
    {
      "metadata": {
        "id": "QG1nOUnY7Uey",
        "colab_type": "code",
        "outputId": "bbade0de-7988-4025-9aeb-60a0671c1a85",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "train[train['swimDistance']>=1000].shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(138, 32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "metadata": {
        "id": "RpNIVKUdUnVQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Remove the ouliers.\n",
        "\n",
        "#train\n",
        "train.drop(train[train.swimDistance>=1000].index,inplace=True)\n",
        "\n",
        "#test\n",
        "test.drop(test[test.swimDistance>=1000].index,inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "yboknRMlUna4",
        "colab_type": "code",
        "outputId": "90ca6474-16df-4f00-fea0-4d58e177313f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "cell_type": "code",
      "source": [
        "# total_distance\n",
        "plt.subplots(figsize=(12,4))\n",
        "sns.distplot(train.total_distance,bins=10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd354ea4668>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtEAAAEGCAYAAACjGskNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrD+HIqG2dgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd354ebc550>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "u89Hu1Oi78o0",
        "colab_type": "code",
        "outputId": "8edf7394-46d1-4e86-84bd-4117972e2c69",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "train[train.total_distance>=15000].shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(519, 32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "metadata": {
        "id": "I8EsLpxJUnZk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Remove the outliers.\n",
        "\n",
        "#train\n",
        "train.drop(train[train.total_distance>=15000].index,inplace=True)\n",
        "\n",
        "#test\n",
        "test.drop(test[test.total_distance>=15000].index,inplace=True)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "49ndbDRhVhbh",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Anomalies in weaponsAcquired"
      ]
    },
    {
      "metadata": {
        "id": "6sTsVM6BUnNr",
        "colab_type": "code",
        "outputId": "577ced49-3448-4de1-e3b0-25df413af200",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "cell_type": "code",
      "source": [
        "#weaponsAcquired\n",
        "#distplot\n",
        "\n",
        "plt.figure(figsize=(12,4))\n",
        "sns.distplot(train['weaponsAcquired'], bins=100)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAEGCAYAAABmaFOcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nX2oAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd354f2b2e8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "vn4fFsJo8Rrj",
        "colab_type": "code",
        "outputId": "e2bfbf8c-b7b9-461d-d4d7-9b8eade304ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "train[train.weaponsAcquired>=50].shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(135, 32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "metadata": {
        "id": "3RNM5fcD8S_7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We should probably remove these outliers from our model. Do you agree?\n",
        "\n",
        "Note that player '3f2bcf53b108c4' acquired 236 weapons in one game!"
      ]
    },
    {
      "metadata": {
        "id": "BaSjNAXQ8nSG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "20IfvzwrUnK5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# remove the outliers.\n",
        "\n",
        "#train\n",
        "train.drop(train[train.weaponsAcquired>=50].index,inplace=True)\n",
        "\n",
        "#test\n",
        "test.drop(test[test.weaponsAcquired>=50].index,inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4cUpWHC_V9y3",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Anomalies in Heals\n",
        "heals - Number of healing items used."
      ]
    },
    {
      "metadata": {
        "id": "J6H3AcufUnEM",
        "colab_type": "code",
        "outputId": "0df5edd3-8155-4e79-86f9-e11aa4ffa31c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "cell_type": "code",
      "source": [
        "#heals \n",
        "#distplot\n",
        "\n",
        "plt.subplots(figsize=(12,4))\n",
        "sns.distplot(train.heals, bins=20)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd3550fec50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAEGCAYAAABmaFOcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd354f82cc0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "U5H2isFD85qM",
        "colab_type": "code",
        "outputId": "924ba297-f2f7-4d22-c902-f525006085ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "train[train.heals>=40].shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(134, 32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "metadata": {
        "id": "2678-m-TWNlf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# remove the outliers.\n",
        "\n",
        "#train\n",
        "train.drop(train[train.heals>=40].index,inplace=True)\n",
        "\n",
        "#test\n",
        "\n",
        "test.drop(test[test.heals>=40].index,inplace=True)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9Ti1w2fFWVzM",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##  Final outcome after Removing all outliers"
      ]
    },
    {
      "metadata": {
        "id": "WCEG2N3yWNuc",
        "colab_type": "code",
        "outputId": "201b3283-e4fb-4f8e-df7b-0570b26a7feb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "# Lets find the shape of training data\n",
        "train.shape\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4443698, 32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "metadata": {
        "id": "HWvge2HMWj_u",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "So the initial shape is (4446966, 29)   \n",
        "And After removeing the outliers the new shape is  (4443698, -)"
      ]
    },
    {
      "metadata": {
        "id": "LfJRDbR6WsrY",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Something around 3000 rows we removed . Which is nothing compare to the number of rows we have."
      ]
    },
    {
      "metadata": {
        "id": "jUlNB3lJW44G",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Categorical Variables"
      ]
    },
    {
      "metadata": {
        "id": "VBIOxzZDWN33",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Create the dummy variable for categorical variable present in our data set.\n",
        "#matchType\n",
        "#train\n",
        "\n",
        "train=pd.get_dummies(train,columns=['matchType'])\n",
        "\n",
        "#test\n",
        "\n",
        "test=pd.get_dummies(test,columns=['matchType'])\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9EjSaTDM904O",
        "colab_type": "code",
        "outputId": "af7a5723-b553-4ed6-e613-87a9809137ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "train.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4443698, 47)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "metadata": {
        "id": "FoPxXpDR92HK",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Now If you guys planning to create dummy variable of matchId and groupId ..\n",
        "\n",
        "For me it's like getting into a sucidal trap."
      ]
    },
    {
      "metadata": {
        "id": "b2zThxeW-CBP",
        "colab_type": "code",
        "outputId": "1be62d6b-f308-48ec-b86c-087de37c5306",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 963
        }
      },
      "cell_type": "code",
      "source": [
        "# Lets again check the data structure of both train and test data\n",
        "train.info()\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 4443698 entries, 0 to 4446965\n",
            "Data columns (total 47 columns):\n",
            "Id                            object\n",
            "groupId                       object\n",
            "matchId                       object\n",
            "assists                       int64\n",
            "boosts                        int64\n",
            "damageDealt                   float64\n",
            "DBNOs                         int64\n",
            "headshotKills                 int64\n",
            "heals                         int64\n",
            "killPlace                     int64\n",
            "killPoints                    int64\n",
            "kills                         int64\n",
            "killStreaks                   int64\n",
            "longestKill                   float64\n",
            "matchDuration                 int64\n",
            "maxPlace                      int64\n",
            "numGroups                     int64\n",
            "rankPoints                    int64\n",
            "revives                       int64\n",
            "rideDistance                  float64\n",
            "roadKills                     int64\n",
            "swimDistance                  float64\n",
            "teamKills                     int64\n",
            "vehicleDestroys               int64\n",
            "walkDistance                  float64\n",
            "weaponsAcquired               int64\n",
            "winPoints                     int64\n",
            "winPlacePerc                  float64\n",
            "player_played                 int64\n",
            "total_distance                float64\n",
            "kills_without_moving          bool\n",
            "matchType_crashfpp            uint8\n",
            "matchType_crashtpp            uint8\n",
            "matchType_duo                 uint8\n",
            "matchType_duo-fpp             uint8\n",
            "matchType_flarefpp            uint8\n",
            "matchType_flaretpp            uint8\n",
            "matchType_normal-duo          uint8\n",
            "matchType_normal-duo-fpp      uint8\n",
            "matchType_normal-solo         uint8\n",
            "matchType_normal-solo-fpp     uint8\n",
            "matchType_normal-squad        uint8\n",
            "matchType_normal-squad-fpp    uint8\n",
            "matchType_solo                uint8\n",
            "matchType_solo-fpp            uint8\n",
            "matchType_squad               uint8\n",
            "matchType_squad-fpp           uint8\n",
            "dtypes: bool(1), float64(7), int64(20), object(3), uint8(16)\n",
            "memory usage: 1.1+ GB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "FBtqgQez-IJg",
        "colab_type": "code",
        "outputId": "5ffd46cb-6066-4f87-a523-94e221358448",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 945
        }
      },
      "cell_type": "code",
      "source": [
        "test.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1933174 entries, 0 to 1934173\n",
            "Data columns (total 46 columns):\n",
            "Id                            object\n",
            "groupId                       object\n",
            "matchId                       object\n",
            "assists                       int64\n",
            "boosts                        int64\n",
            "damageDealt                   float64\n",
            "DBNOs                         int64\n",
            "headshotKills                 int64\n",
            "heals                         int64\n",
            "killPlace                     int64\n",
            "killPoints                    int64\n",
            "kills                         int64\n",
            "killStreaks                   int64\n",
            "longestKill                   float64\n",
            "matchDuration                 int64\n",
            "maxPlace                      int64\n",
            "numGroups                     int64\n",
            "rankPoints                    int64\n",
            "revives                       int64\n",
            "rideDistance                  float64\n",
            "roadKills                     int64\n",
            "swimDistance                  float64\n",
            "teamKills                     int64\n",
            "vehicleDestroys               int64\n",
            "walkDistance                  float64\n",
            "weaponsAcquired               int64\n",
            "winPoints                     int64\n",
            "player_played                 int64\n",
            "total_distance                float64\n",
            "kills_without_moving          bool\n",
            "matchType_crashfpp            uint8\n",
            "matchType_crashtpp            uint8\n",
            "matchType_duo                 uint8\n",
            "matchType_duo-fpp             uint8\n",
            "matchType_flarefpp            uint8\n",
            "matchType_flaretpp            uint8\n",
            "matchType_normal-duo          uint8\n",
            "matchType_normal-duo-fpp      uint8\n",
            "matchType_normal-solo         uint8\n",
            "matchType_normal-solo-fpp     uint8\n",
            "matchType_normal-squad        uint8\n",
            "matchType_normal-squad-fpp    uint8\n",
            "matchType_solo                uint8\n",
            "matchType_solo-fpp            uint8\n",
            "matchType_squad               uint8\n",
            "matchType_squad-fpp           uint8\n",
            "dtypes: bool(1), float64(6), int64(20), object(3), uint8(16)\n",
            "memory usage: 473.8+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "pQ97MOHt0xLQ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Feature Selection\n",
        "\n",
        "Feature selection methods can give you useful information on the relative importance or relevance of features for a given problem. You can use this information to create filtered versions of your dataset and increase the accuracy of your models."
      ]
    },
    {
      "metadata": {
        "id": "xjbd2-YJ9GPW",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Why to perform feature selection?\n",
        "\n",
        "- It enables the machine learning algorithm to train faster.\n",
        "- It reduces the complexity of a model and makes it easier to interpret.\n",
        "- It improves the accuracy of a model if the right subset is chosen.\n",
        "- It reduces overfitting.\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "3U8-QgPU-VhA",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Checking correlation is the fastest way to estimate the impact but it doesn't capture the actual contribution of the score."
      ]
    },
    {
      "metadata": {
        "id": "BIY4NvnY-cv4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "But, correlation is naive, nearly primitive, method of feature selection. Consequently it will sometimes work (since data sometimes does follow the linear combination redundancy) and will fail miserably in many more complex tasks. There is no \"golden\" answer here. Correlation based feature selection is like logistic regression for classification - the easiest thing to try, but should not expect to solve any problem out there."
      ]
    },
    {
      "metadata": {
        "id": "tfYJZbeg-xHz",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## One more step to make data more feasible for training."
      ]
    },
    {
      "metadata": {
        "id": "e6G_8Jg9-kg7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#drop the all unnecessary columns\n",
        "\n",
        "#train\n",
        "train.drop(['kills_without_moving'],axis=1,inplace=True)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zi3tAkrQ-syo",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#test\n",
        "\n",
        "test.drop(['kills_without_moving'],axis=1,inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "majvWfSa8orV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Featrue selection  using algorithm"
      ]
    },
    {
      "metadata": {
        "id": "pg-OaCVoWN-O",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# feature selectionm using algorithm itself.\n",
        "from sklearn.ensemble import RandomForestRegressor\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "EUHbQV84WOI2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#before that first drop some unnecessary columns.\n",
        "\n",
        "#For train\n",
        "#Id #groupId  # matchId\n",
        "\n",
        "train.drop(['Id','groupId','matchId'],axis=1,inplace=True)\n",
        "\n",
        "# For test\n",
        "# Save the test Id \n",
        "test_id=test['Id']\n",
        "\n",
        "test.drop(['Id','groupId','matchId'],axis=1,inplace=True)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "5EaLi2EAR4wy",
        "colab_type": "code",
        "outputId": "474c9a63-d620-4ac7-9070-92a25e48615a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "train.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4443698, 43)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "metadata": {
        "id": "x_rTLHCmR8dP",
        "colab_type": "code",
        "outputId": "fa0db5bc-78fb-4122-b1ba-442233da8354",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "test.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1933174, 42)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "metadata": {
        "id": "ZCy4CO349u2O",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Sample data for training"
      ]
    },
    {
      "metadata": {
        "id": "OYbAQgZFWOS6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Take sample for debugging and exploration\n",
        "sample = 500000\n",
        "df_sample = train.sample(sample)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "5Xo5hTarWONW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Metric used for the PUBG competition (Mean Absolute Error (MAE))\n",
        "from sklearn.metrics import mean_absolute_error"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "TMQRfnUQWOED",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Design model function. \n",
        "\n",
        "def score(m : RandomForestRegressor):\n",
        "    res = ['mae train: ', mean_absolute_error(m.predict(X_train), y_train), \n",
        "           'mae val: ', mean_absolute_error(m.predict(X_valid), y_valid)]\n",
        "    if hasattr(m, 'oob_score_'): res.append(m.oob_score_)\n",
        "    print(res)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xV9E5Y-Y-VE1",
        "colab_type": "code",
        "outputId": "621cb643-63fe-46df-dcf2-f92ebeecc4b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 890
        }
      },
      "cell_type": "code",
      "source": [
        "df_sample.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 500000 entries, 4013047 to 1919861\n",
            "Data columns (total 43 columns):\n",
            "assists                       500000 non-null int64\n",
            "boosts                        500000 non-null int64\n",
            "damageDealt                   500000 non-null float64\n",
            "DBNOs                         500000 non-null int64\n",
            "headshotKills                 500000 non-null int64\n",
            "heals                         500000 non-null int64\n",
            "killPlace                     500000 non-null int64\n",
            "killPoints                    500000 non-null int64\n",
            "kills                         500000 non-null int64\n",
            "killStreaks                   500000 non-null int64\n",
            "longestKill                   500000 non-null float64\n",
            "matchDuration                 500000 non-null int64\n",
            "maxPlace                      500000 non-null int64\n",
            "numGroups                     500000 non-null int64\n",
            "rankPoints                    500000 non-null int64\n",
            "revives                       500000 non-null int64\n",
            "rideDistance                  500000 non-null float64\n",
            "roadKills                     500000 non-null int64\n",
            "swimDistance                  500000 non-null float64\n",
            "teamKills                     500000 non-null int64\n",
            "vehicleDestroys               500000 non-null int64\n",
            "walkDistance                  500000 non-null float64\n",
            "weaponsAcquired               500000 non-null int64\n",
            "winPoints                     500000 non-null int64\n",
            "winPlacePerc                  500000 non-null float64\n",
            "player_played                 500000 non-null int64\n",
            "total_distance                500000 non-null float64\n",
            "matchType_crashfpp            500000 non-null uint8\n",
            "matchType_crashtpp            500000 non-null uint8\n",
            "matchType_duo                 500000 non-null uint8\n",
            "matchType_duo-fpp             500000 non-null uint8\n",
            "matchType_flarefpp            500000 non-null uint8\n",
            "matchType_flaretpp            500000 non-null uint8\n",
            "matchType_normal-duo          500000 non-null uint8\n",
            "matchType_normal-duo-fpp      500000 non-null uint8\n",
            "matchType_normal-solo         500000 non-null uint8\n",
            "matchType_normal-solo-fpp     500000 non-null uint8\n",
            "matchType_normal-squad        500000 non-null uint8\n",
            "matchType_normal-squad-fpp    500000 non-null uint8\n",
            "matchType_solo                500000 non-null uint8\n",
            "matchType_solo-fpp            500000 non-null uint8\n",
            "matchType_squad               500000 non-null uint8\n",
            "matchType_squad-fpp           500000 non-null uint8\n",
            "dtypes: float64(7), int64(20), uint8(16)\n",
            "memory usage: 114.4 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Do1e8MGjSSFb",
        "colab_type": "code",
        "outputId": "e86671ee-b6b3-499c-847d-a38d9d907c2e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "y=df_sample['winPlacePerc']\n",
        "df = df_sample.drop(columns = ['winPlacePerc'])\n",
        "df.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(500000, 42)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "metadata": {
        "id": "wIp0wZSRWN1m",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#train test split.\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train,X_valid,y_train,y_valid=train_test_split(df,y,test_size=0.3,random_state=40)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pIWUMTRyrPCa",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "## Before training lets talk bit about randome forest regression algorithm.\n",
        "Random Forest is a supervised learning algorithm , very flexible and easy to use .And it produce better result even without hyper-parameter tuning.\n",
        "![alt text](https://cdn-images-1.medium.com/max/592/1*i0o8mjFfCn-uD79-F1Cqkw.png)\n",
        "\n",
        "Random Forest doc:\n",
        "[RandomForest Doc ](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)"
      ]
    },
    {
      "metadata": {
        "id": "v2WgEfavWN0Z",
        "colab_type": "code",
        "outputId": "ba5d5682-0b4f-4951-96bb-33def74d6d2e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "#model with certaion parameter. Ramdon forest algorithm with default parameters.\n",
        "m1 = RandomForestRegressor(n_estimators=40, min_samples_leaf=3, max_features='sqrt',\n",
        "                          n_jobs=-1)\n",
        "\n",
        "m1.fit(X_train, y_train)\n",
        "score(m1)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['mae train: ', 0.0428168158465911, 'mae val: ', 0.06292893715434004]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "alhUa0N3WNsf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Find the important feature using random Forest algorithm.\n",
        "\n",
        "importance=m1.feature_importances_"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bSLqbjgI-6qe",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bAMfXSJaWNq0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Create a new Dataframe for the given feature with their importance.\n",
        "data=pd.DataFrame(sorted(zip(m1.feature_importances_, df.columns)), columns=['Value','Feature'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mLVnNZWOWNo5",
        "colab_type": "code",
        "outputId": "71bed138-6d02-46ce-c9e0-340db2bbe5d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 681
        }
      },
      "cell_type": "code",
      "source": [
        "# Lets plot all the feature.\n",
        "\n",
        "plt.figure(figsize=(20, 10))\n",
        "sns.barplot(x=\"Value\", y=\"Feature\", data=data.sort_values(by=\"Value\", ascending=False))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1428: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
            "  stat_data = remove_na(group_data)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd351bcd080>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABQQAAAJNCAYAAABnbnixAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8jdeIytPoo4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd351bcd438>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "QofKEqxRAzwK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# select top 25 features and re-train the model.\n",
        "\n",
        "\n",
        "new_data=data.sort_values(by='Value',ascending=False)[:25]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UoKmvHea_QBs",
        "colab_type": "code",
        "outputId": "d06ce5ca-09d1-4f02-acf3-df4ad6bd8e2c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "cell_type": "code",
      "source": [
        "new_data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Value</th>\n",
              "      <th>Feature</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>0.206070</td>\n",
              "      <td>walkDistance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>0.198567</td>\n",
              "      <td>total_distance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>0.183394</td>\n",
              "      <td>killPlace</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>0.118222</td>\n",
              "      <td>boosts</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>0.075067</td>\n",
              "      <td>weaponsAcquired</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Value          Feature\n",
              "41  0.206070     walkDistance\n",
              "40  0.198567   total_distance\n",
              "39  0.183394        killPlace\n",
              "38  0.118222           boosts\n",
              "37  0.075067  weaponsAcquired"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "metadata": {
        "id": "eirIa1SEAz6O",
        "colab_type": "code",
        "outputId": "ab641730-874c-47f7-bcd8-00ad27735b4d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 572
        }
      },
      "cell_type": "code",
      "source": [
        "# Plot all the variables.\n",
        "plt.subplots(figsize=(15,8))\n",
        "sns.barplot(x='Value',y='Feature',data=new_data)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1428: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
            "  stat_data = remove_na(group_data)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd35129e1d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA8MAAAHgCAYAAACB0r+HAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n7LwCG9s2kv9iWRAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd351292da0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ejddtcg-_ZtD",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "cols=new_data.Feature.values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "PmUjO5lr_d0g",
        "colab_type": "code",
        "outputId": "5649aab8-f266-4a46-fc39-ed4f5bbc6622",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        }
      },
      "cell_type": "code",
      "source": [
        "cols\n",
        "# so these are the top 25 set of feature"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['walkDistance', 'total_distance', 'killPlace', 'boosts',\n",
              "       'weaponsAcquired', 'damageDealt', 'longestKill', 'heals', 'kills',\n",
              "       'rideDistance', 'matchDuration', 'numGroups', 'player_played',\n",
              "       'maxPlace', 'headshotKills', 'killStreaks', 'rankPoints',\n",
              "       'swimDistance', 'assists', 'DBNOs', 'winPoints', 'revives',\n",
              "       'killPoints', 'matchType_squad', 'matchType_solo-fpp'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "metadata": {
        "id": "RauiFZ9A_fRf",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "So these are the top 25 features and we need to create new model based on these features."
      ]
    },
    {
      "metadata": {
        "id": "g2iOVtwBAHaa",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Recreate the model using sample data."
      ]
    },
    {
      "metadata": {
        "id": "-OYgJHf0_qQz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# train and validation data\n",
        "X_train,X_valid,y_train,y_valid=train_test_split(df[cols],y,test_size=0.3,random_state=40)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9LE81mgPAZuq",
        "colab_type": "code",
        "outputId": "6d87c254-9dc4-4fb7-8d6a-5cd7ef73613b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "X_train.shape\n",
        "print(X_valid.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(150000, 25)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "QbkE1mpSAtDB",
        "colab_type": "code",
        "outputId": "c3f91a8e-a811-42b5-dde3-c770927b39c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "m1 = RandomForestRegressor(n_estimators=40, min_samples_leaf=3, max_features='sqrt',\n",
        "                          n_jobs=-1)\n",
        "m1.fit(X_train, y_train)\n",
        "score(m1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['mae train: ', 0.03804379443764784, 'mae val: ', 0.06045273441256874]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ddfFrFKHCydp",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Now What"
      ]
    },
    {
      "metadata": {
        "id": "DxgffJf5C7GH",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "You guys must be thinking all seem to be perfectly working . Let's train the model for the complete data.\n",
        "\n",
        "Here the problem came  ,"
      ]
    },
    {
      "metadata": {
        "id": "X0YVD5-eDeNt",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Google colab can handle that huge amoung of data for training. Because of tons of calculation is happeining in the backend. Due to which it ran out of memory."
      ]
    },
    {
      "metadata": {
        "id": "8PZLctzVEPqH",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We gonna use cloud services . I prefer AWS(amazon web services ) you can choose of your choice.\n"
      ]
    },
    {
      "metadata": {
        "id": "lhhc2pXJTU-R",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "To learn more about how do we train model in cloud. check out \n",
        "\n",
        "**First: **\n",
        "\n",
        "Check out amazon sagemaker blog [Amazon Sagemaker ](http://ai-venture.com/amazon-sagemaker/) \n",
        "\n",
        "**Second:**\n",
        "\n",
        "Notebook for Aws training [ Final PUBG model using AWS ](https://github.com/AIVenture0/PubG-Win-prediction-using-ML/blob/master/Final%20PUBG%20model%20using%20AWS.ipynb) \n",
        "\n"
      ]
    }
  ]
}