KaggleChiSq.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "KaggleAkshat.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMc2spOpkz32gYkjmwCAQ0G",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mahaveer-rulaniya/competitions-and-hackathons/blob/main/KaggleChiSq.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dCUxMG4sN61_",
        "outputId": "62c570c3-cb68-40e5-ea00-e23474cb6e09"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "MBj2Z-uIN8Qs"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/train.csv')\n",
        "test_df = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/test.csv')"
      ],
      "metadata": {
        "id": "cKlPzLQROu0R"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "NJ8vNuNgO7j2",
        "outputId": "b731ebd4-4a1a-4260-f5d5-3236ada36d45"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0  type      time  dayofweek  models   params  queuelen  trials  \\\n",
              "0        1178   9.0  0.413889        3.0    21.0    535.0       0.0    40.0   \n",
              "1         877   9.0  0.643056        5.0     9.0   2682.0       0.0    40.0   \n",
              "2        4233  13.0  0.607639        4.0    32.0   3305.0       0.0    40.0   \n",
              "3        4134   8.0  0.643750        4.0     3.0  22023.0       0.0    40.0   \n",
              "4        3598  12.0  0.519444        3.0     8.0   3707.0       0.0    40.0   \n",
              "\n",
              "   duration  \n",
              "0       0.0  \n",
              "1       0.0  \n",
              "2       0.0  \n",
              "3       0.0  \n",
              "4       0.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a65722c6-260c-4e95-ae34-d824006bc233\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Unnamed: 0</th>\n",
              "      <th>type</th>\n",
              "      <th>time</th>\n",
              "      <th>dayofweek</th>\n",
              "      <th>models</th>\n",
              "      <th>params</th>\n",
              "      <th>queuelen</th>\n",
              "      <th>trials</th>\n",
              "      <th>duration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1178</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.413889</td>\n",
              "      <td>3.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>535.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>877</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.643056</td>\n",
              "      <td>5.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2682.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4233</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.607639</td>\n",
              "      <td>4.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>3305.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4134</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.643750</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>22023.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3598</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0.519444</td>\n",
              "      <td>3.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>3707.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a65722c6-260c-4e95-ae34-d824006bc233')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a65722c6-260c-4e95-ae34-d824006bc233 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a65722c6-260c-4e95-ae34-d824006bc233');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop('Unnamed: 0', axis = 1)\n"
      ],
      "metadata": {
        "id": "1nBysgDDO-nL"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "id = test_df['Unnamed: 0']"
      ],
      "metadata": {
        "id": "R2sHULnsQByo"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_df = test_df.drop('Unnamed: 0', axis = 1)"
      ],
      "metadata": {
        "id": "cBa5UGF6QH6-"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "YqYOWhujPnFs",
        "outputId": "ee11f036-0559-446a-993c-878b786993ba"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   type      time  dayofweek  models   params  queuelen  trials  duration\n",
              "0   9.0  0.413889        3.0    21.0    535.0       0.0    40.0       0.0\n",
              "1   9.0  0.643056        5.0     9.0   2682.0       0.0    40.0       0.0\n",
              "2  13.0  0.607639        4.0    32.0   3305.0       0.0    40.0       0.0\n",
              "3   8.0  0.643750        4.0     3.0  22023.0       0.0    40.0       0.0\n",
              "4  12.0  0.519444        3.0     8.0   3707.0       0.0    40.0       0.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d281008a-456f-406d-b671-b91a81203eec\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>type</th>\n",
              "      <th>time</th>\n",
              "      <th>dayofweek</th>\n",
              "      <th>models</th>\n",
              "      <th>params</th>\n",
              "      <th>queuelen</th>\n",
              "      <th>trials</th>\n",
              "      <th>duration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9.0</td>\n",
              "      <td>0.413889</td>\n",
              "      <td>3.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>535.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9.0</td>\n",
              "      <td>0.643056</td>\n",
              "      <td>5.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2682.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13.0</td>\n",
              "      <td>0.607639</td>\n",
              "      <td>4.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>3305.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8.0</td>\n",
              "      <td>0.643750</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>22023.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12.0</td>\n",
              "      <td>0.519444</td>\n",
              "      <td>3.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>3707.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d281008a-456f-406d-b671-b91a81203eec')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d281008a-456f-406d-b671-b91a81203eec button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d281008a-456f-406d-b671-b91a81203eec');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5kHcAPdVPr2S",
        "outputId": "9066b934-5cbd-4d08-defb-e5457cabde58"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3464 entries, 0 to 3463\n",
            "Data columns (total 8 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   type       3104 non-null   float64\n",
            " 1   time       3464 non-null   float64\n",
            " 2   dayofweek  3464 non-null   float64\n",
            " 3   models     3464 non-null   float64\n",
            " 4   params     3191 non-null   float64\n",
            " 5   queuelen   3464 non-null   float64\n",
            " 6   trials     3464 non-null   float64\n",
            " 7   duration   3464 non-null   float64\n",
            "dtypes: float64(8)\n",
            "memory usage: 216.6 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "LA5nXm08P4ht",
        "outputId": "5e58e0f4-1bcb-4fde-8c4b-a3ca95269429"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              type         time    dayofweek        models         params  \\\n",
              "count  3104.000000  3464.000000  3464.000000   3464.000000    3191.000000   \n",
              "mean      9.253222     0.571791     3.246536     82.018764   10834.270762   \n",
              "std       3.642100     0.166895     1.571793   1901.719969   25712.079591   \n",
              "min       0.000000     0.000694     1.000000      1.000000      71.000000   \n",
              "25%       7.000000     0.455556     2.000000      5.000000     957.500000   \n",
              "50%       9.000000     0.584375     3.000000     13.000000    3026.000000   \n",
              "75%      13.000000     0.690972     5.000000     26.000000    6977.000000   \n",
              "max      14.000000     0.991667     7.000000  87293.000000  396701.000000   \n",
              "\n",
              "          queuelen       trials     duration  \n",
              "count  3464.000000  3464.000000  3464.000000  \n",
              "mean     54.402425    58.599018     0.731236  \n",
              "std     354.209141    68.524295     0.894489  \n",
              "min       0.000000     5.000000     0.000000  \n",
              "25%       0.000000    40.000000     0.000000  \n",
              "50%       0.000000    40.000000     0.000000  \n",
              "75%       0.000000    40.000000     1.000000  \n",
              "max    5547.000000   400.000000     3.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-298a8865-8bbf-44c3-b239-103b57683afd\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>type</th>\n",
              "      <th>time</th>\n",
              "      <th>dayofweek</th>\n",
              "      <th>models</th>\n",
              "      <th>params</th>\n",
              "      <th>queuelen</th>\n",
              "      <th>trials</th>\n",
              "      <th>duration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3104.000000</td>\n",
              "      <td>3464.000000</td>\n",
              "      <td>3464.000000</td>\n",
              "      <td>3464.000000</td>\n",
              "      <td>3191.000000</td>\n",
              "      <td>3464.000000</td>\n",
              "      <td>3464.000000</td>\n",
              "      <td>3464.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>9.253222</td>\n",
              "      <td>0.571791</td>\n",
              "      <td>3.246536</td>\n",
              "      <td>82.018764</td>\n",
              "      <td>10834.270762</td>\n",
              "      <td>54.402425</td>\n",
              "      <td>58.599018</td>\n",
              "      <td>0.731236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.642100</td>\n",
              "      <td>0.166895</td>\n",
              "      <td>1.571793</td>\n",
              "      <td>1901.719969</td>\n",
              "      <td>25712.079591</td>\n",
              "      <td>354.209141</td>\n",
              "      <td>68.524295</td>\n",
              "      <td>0.894489</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000694</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>7.000000</td>\n",
              "      <td>0.455556</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>957.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>9.000000</td>\n",
              "      <td>0.584375</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>3026.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>13.000000</td>\n",
              "      <td>0.690972</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>6977.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>14.000000</td>\n",
              "      <td>0.991667</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>87293.000000</td>\n",
              "      <td>396701.000000</td>\n",
              "      <td>5547.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-298a8865-8bbf-44c3-b239-103b57683afd')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-298a8865-8bbf-44c3-b239-103b57683afd button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-298a8865-8bbf-44c3-b239-103b57683afd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vKlN0wssP_iI",
        "outputId": "39151c80-5911-4f6a-dfc1-6ee9ece0db2d"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "type         360\n",
              "time           0\n",
              "dayofweek      0\n",
              "models         0\n",
              "params       273\n",
              "queuelen       0\n",
              "trials         0\n",
              "duration       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df.corr() , annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "id": "ecTeVyLkQHfF",
        "outputId": "226edc5e-71ba-42e9-fdb7-cbe36bbf4292"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff9eb292d50>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.duration.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oht1EGG7Qi-m",
        "outputId": "0afe02e5-bc77-49f2-cb2c-8a25f1c61400"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 1., 3., 2.])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x = df['duration'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "LbbVV4lMRBxu",
        "outputId": "420fdc98-674d-4e6c-a53b-a406aefb91f3"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff9e8964d50>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(x = df.duration , y = df.trials)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "WPkGi03ARRi3",
        "outputId": "752a0d23-22bd-41ae-b6cd-899cc2db1e44"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7ff9e83ee250>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#sns.pairplot(df)"
      ],
      "metadata": {
        "id": "aSGvLvz6SB7B"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(df['type'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "MxPGffvVSMeD",
        "outputId": "e2ea6924-0a53-4d30-934d-f1eee45cc9d5"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff9e839ba90>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df['params'],30)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "id": "j_9PLgkJTSNa",
        "outputId": "35ab5573-7124-4f26-a547-ca18c27bc34e"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([2.632e+03, 2.210e+02, 1.070e+02, 6.700e+01, 5.000e+01, 3.000e+01,\n",
              "        2.200e+01, 1.800e+01, 1.100e+01, 8.000e+00, 2.000e+00, 6.000e+00,\n",
              "        5.000e+00, 0.000e+00, 3.000e+00, 1.000e+00, 0.000e+00, 4.000e+00,\n",
              "        0.000e+00, 1.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n",
              "        1.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 2.000e+00]),\n",
              " array([7.10000e+01, 1.32920e+04, 2.65130e+04, 3.97340e+04, 5.29550e+04,\n",
              "        6.61760e+04, 7.93970e+04, 9.26180e+04, 1.05839e+05, 1.19060e+05,\n",
              "        1.32281e+05, 1.45502e+05, 1.58723e+05, 1.71944e+05, 1.85165e+05,\n",
              "        1.98386e+05, 2.11607e+05, 2.24828e+05, 2.38049e+05, 2.51270e+05,\n",
              "        2.64491e+05, 2.77712e+05, 2.90933e+05, 3.04154e+05, 3.17375e+05,\n",
              "        3.30596e+05, 3.43817e+05, 3.57038e+05, 3.70259e+05, 3.83480e+05,\n",
              "        3.96701e+05]),\n",
              " <a list of 30 Patch objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gRk6MgESPE-g",
        "outputId": "8287c2a5-9a51-40b2-91f7-ffa7876cc4cf"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "type         66\n",
              "time          0\n",
              "dayofweek     0\n",
              "models        0\n",
              "params       69\n",
              "queuelen      0\n",
              "trials        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['type']= df['type'].fillna(round(df['type'].median()))\n",
        "test_df['type']=test_df['type'].fillna(round(test_df['type'].median()))"
      ],
      "metadata": {
        "id": "uygN6Q9uTmgT"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['params'] = df['params'].fillna(df['params'].median())\n",
        "test_df['params'] = test_df['params'].fillna(test_df['params'].median())"
      ],
      "metadata": {
        "id": "Ruevo3gI1vkE"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "igQ6kwYI1wNq",
        "outputId": "1f48fc74-0a53-4792-ee74-9e7ae89256a7"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   type      time  dayofweek  models   params  queuelen  trials  duration\n",
              "0   9.0  0.413889        3.0    21.0    535.0       0.0    40.0       0.0\n",
              "1   9.0  0.643056        5.0     9.0   2682.0       0.0    40.0       0.0\n",
              "2  13.0  0.607639        4.0    32.0   3305.0       0.0    40.0       0.0\n",
              "3   8.0  0.643750        4.0     3.0  22023.0       0.0    40.0       0.0\n",
              "4  12.0  0.519444        3.0     8.0   3707.0       0.0    40.0       0.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b76080f3-9b70-42fe-8760-998aa5321927\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>type</th>\n",
              "      <th>time</th>\n",
              "      <th>dayofweek</th>\n",
              "      <th>models</th>\n",
              "      <th>params</th>\n",
              "      <th>queuelen</th>\n",
              "      <th>trials</th>\n",
              "      <th>duration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9.0</td>\n",
              "      <td>0.413889</td>\n",
              "      <td>3.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>535.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9.0</td>\n",
              "      <td>0.643056</td>\n",
              "      <td>5.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2682.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13.0</td>\n",
              "      <td>0.607639</td>\n",
              "      <td>4.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>3305.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8.0</td>\n",
              "      <td>0.643750</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>22023.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12.0</td>\n",
              "      <td>0.519444</td>\n",
              "      <td>3.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>3707.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b76080f3-9b70-42fe-8760-998aa5321927')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b76080f3-9b70-42fe-8760-998aa5321927 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b76080f3-9b70-42fe-8760-998aa5321927');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train = df['duration']\n",
        "X_train = df.drop('duration', axis = 1)\n",
        "X_test = test_df"
      ],
      "metadata": {
        "id": "LoXrB9wQ2Otk"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "MBFLOR1QPorG",
        "outputId": "0029186e-17fb-49cc-c74e-44c52aa5be4d"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   type      time  dayofweek  models   params  queuelen  trials\n",
              "0   9.0  0.590972        2.0    13.0   4615.0       0.0    40.0\n",
              "1   9.0  0.545833        7.0    33.0    552.0       0.0    40.0\n",
              "2  14.0  0.510417        3.0     2.0   2890.0       0.0    40.0\n",
              "3   9.0  0.518056        3.0     9.0   1126.0       0.0    40.0\n",
              "4  11.0  0.376389        2.0    23.0  47164.0       0.0    40.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6f399668-da55-4bc0-828f-1fe9f01ae769\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>type</th>\n",
              "      <th>time</th>\n",
              "      <th>dayofweek</th>\n",
              "      <th>models</th>\n",
              "      <th>params</th>\n",
              "      <th>queuelen</th>\n",
              "      <th>trials</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9.0</td>\n",
              "      <td>0.590972</td>\n",
              "      <td>2.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>4615.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9.0</td>\n",
              "      <td>0.545833</td>\n",
              "      <td>7.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>552.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>14.0</td>\n",
              "      <td>0.510417</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2890.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9.0</td>\n",
              "      <td>0.518056</td>\n",
              "      <td>3.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1126.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11.0</td>\n",
              "      <td>0.376389</td>\n",
              "      <td>2.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>47164.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6f399668-da55-4bc0-828f-1fe9f01ae769')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6f399668-da55-4bc0-828f-1fe9f01ae769 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6f399668-da55-4bc0-828f-1fe9f01ae769');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler=StandardScaler()\n",
        "X_train_scaled=scaler.fit_transform(X_train)\n",
        "X_test_scaled=scaler.transform(X_test)\n"
      ],
      "metadata": {
        "id": "UHZYA4-6PogV"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier"
      ],
      "metadata": {
        "id": "vyNtq1hpPoct"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xgb = GradientBoostingClassifier(n_estimators=500, max_depth= 6).fit(X_train_scaled, y_train)"
      ],
      "metadata": {
        "id": "M253kBRePoZR"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = xgb.predict(X_test_scaled)"
      ],
      "metadata": {
        "id": "IPgKFGeLPoRl"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_output = pd.Series(y_pred).astype(int)"
      ],
      "metadata": {
        "id": "C9hZ6n4aRPoG"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = {'ID': id , 'duration': y_output}"
      ],
      "metadata": {
        "id": "BBag05fMRv9C"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output = pd.concat(data, axis = 1)"
      ],
      "metadata": {
        "id": "QCZ65YYXRPjp"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "x3w6A7-IRPf9",
        "outputId": "0b93966e-1481-4829-d72e-f8a07ab6a923"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     ID  duration\n",
              "0  2439         1\n",
              "1  1213         1\n",
              "2  2659         1\n",
              "3    17         0\n",
              "4  3800         0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6cdac6a5-454a-4289-866c-f408b1dee701\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>duration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2439</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1213</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2659</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3800</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6cdac6a5-454a-4289-866c-f408b1dee701')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6cdac6a5-454a-4289-866c-f408b1dee701 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6cdac6a5-454a-4289-866c-f408b1dee701');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output.to_csv('submission.csv', index = False)"
      ],
      "metadata": {
        "id": "T7B0LlsZRPcf"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6596QKe3RPYH",
        "outputId": "8b60418e-03e4-4ec0-9222-4d9a38ec51b2"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(867, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "gAwnZddeRPUu"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# y = df['duration']\n",
        "# X = df.drop('duration' , axis = 1)"
      ],
      "metadata": {
        "id": "dbW5-PXj2REc"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.preprocessing import StandardScaler\n",
        "# from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "jsFXxLMv2v_J"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# X_train , X_test , y_train, y_test = train_test_split(X, y , random_state=43)"
      ],
      "metadata": {
        "id": "6zggNtv623Cn"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# X_train.head()"
      ],
      "metadata": {
        "id": "6Phn0Cgv3F8L"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# scaler=StandardScaler()"
      ],
      "metadata": {
        "id": "LsBrbeVC3JLI"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# X_train_scaled=scaler.fit_transform(X_train)\n",
        "# X_test_scaled=scaler.transform(X_test)\n"
      ],
      "metadata": {
        "id": "H_CyjuU03bPG"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.ensemble import GradientBoostingClassifier"
      ],
      "metadata": {
        "id": "t7X51l5U303b"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# xgb = GradientBoostingClassifier(n_estimators=500, max_depth= 5).fit(X_train_scaled, y_train)"
      ],
      "metadata": {
        "id": "M_gDK2Pn4pg-"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# y_pred = xgb.predict(X_test_scaled)"
      ],
      "metadata": {
        "id": "rYxQOe258ksm"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.metrics import f1_score\n",
        "# print(f1_score(y_test, y_pred , average='weighted'))"
      ],
      "metadata": {
        "id": "c8oB395c9SFJ"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.metrics import classification_report\n",
        "# print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "id": "CpCBNkPA9bHP"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "sepHxLcn_JLL"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.ensemble import RandomForestClassifier"
      ],
      "metadata": {
        "id": "pqaeaaueA6Wa"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# rm = RandomForestClassifier().fit(X_train_scaled, y_train)"
      ],
      "metadata": {
        "id": "CBH_t-fpBBg3"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# rm_pred = rm.predict(X_test_scaled)"
      ],
      "metadata": {
        "id": "XRQ2GtFWBI0L"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.metrics import classification_report\n",
        "# print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "id": "_IXa3mGIBNGA"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "QUnknqPGBQfa"
      },
      "execution_count": 49,
      "outputs": []
    }
  ]
}