Retail_Discount_Prediction.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IQZegDJGMzyy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "abdb02f3-1cba-4950-f947-2f192028d8fd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting xgboost==1.6.1\n",
            "  Downloading xgboost-1.6.1-py3-none-manylinux2014_x86_64.whl (192.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 192.9 MB 52 kB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from xgboost==1.6.1) (1.21.6)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from xgboost==1.6.1) (1.7.3)\n",
            "Installing collected packages: xgboost\n",
            "  Attempting uninstall: xgboost\n",
            "    Found existing installation: xgboost 0.90\n",
            "    Uninstalling xgboost-0.90:\n",
            "      Successfully uninstalled xgboost-0.90\n",
            "Successfully installed xgboost-1.6.1\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "!pip3 install xgboost==1.6.1\n",
        "import xgboost as xgb"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "7cMuH2z4aWQJ",
        "outputId": "e24fa75e-b7e4-4e03-bfcb-958ae08ded60"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'1.6.1'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "xgb.__version__"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.upload()\n",
        "\n",
        "# Upload kaggle.json"
      ],
      "metadata": {
        "id": "4v87P3XYDMsn",
        "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
        },
        "outputId": "27619609-84e8-4bf8-deb2-d37676601440"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-2cf069e7-d231-4e6a-acf6-4e0fab4f9682\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-2cf069e7-d231-4e6a-acf6-4e0fab4f9682\">\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> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"ayazziaansari\",\"key\":\"6d5ea84e900752cd596b5f8384c53471\"}'}"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p0KRQU8nNA95",
        "outputId": "fac722af-93d8-4848-9184-881cf2925802"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading sa2022.zip to /content\n",
            "\r  0% 0.00/675k [00:00<?, ?B/s]\n",
            "\r100% 675k/675k [00:00<00:00, 113MB/s]\n",
            "Archive:  sa2022.zip\n",
            "  inflating: Sample__submission.csv  \n",
            "  inflating: test.csv                \n",
            "  inflating: train.csv               \n"
          ]
        }
      ],
      "source": [
        "!mkdir ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 /root/.kaggle/kaggle.json\n",
        "\n",
        "!kaggle competitions download -c sa2022\n",
        "!unzip sa2022.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BIiqAO-INB36"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv('train.csv', index_col='id')\n",
        "test_df = pd.read_csv('test.csv', index_col='id')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "He27qSULNzyR",
        "outputId": "4a8fd41b-8524-4cc0-a7ac-4bb6c83acab0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                   title  Rating maincateg  \\\n",
              "id                                                                           \n",
              "16695  Fashionable & Comfortable Bellies For Women  (...     3.9     Women   \n",
              "5120   Combo Pack of 4 Casual Shoes Sneakers For Men ...     3.8       Men   \n",
              "18391         Cilia Mode Leo Sneakers For Women  (White)     4.4     Women   \n",
              "495                              Men Black Sports Sandal     4.2       Men   \n",
              "16408                            Men Green Sports Sandal     3.9       Men   \n",
              "\n",
              "       platform  price1  actprice1 Offer %  norating1  noreviews1  star_5f  \\\n",
              "id                                                                           \n",
              "16695  Flipkart     698        999  30.13%       38.0         7.0     17.0   \n",
              "5120   Flipkart     999       1999  50.03%      531.0        69.0    264.0   \n",
              "18391  Flipkart    2749       4999  45.01%       17.0         4.0     11.0   \n",
              "495    Flipkart     518        724  15.85%    46413.0      6229.0   1045.0   \n",
              "16408  Flipkart    1379       2299  40.02%       77.0         3.0     35.0   \n",
              "\n",
              "       star_4f  star_3f  star_2f  star_1f  fulfilled1  \n",
              "id                                                     \n",
              "16695      9.0      6.0        3        3           0  \n",
              "5120      92.0     73.0       29       73           1  \n",
              "18391      3.0      2.0        1        0           1  \n",
              "495    12416.0   5352.0      701     4595           1  \n",
              "16408     21.0      7.0        7        7           1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c42a8dfd-ef3e-4ab9-87bb-dff7d86dfb11\">\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",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>Rating</th>\n",
              "      <th>maincateg</th>\n",
              "      <th>platform</th>\n",
              "      <th>price1</th>\n",
              "      <th>actprice1</th>\n",
              "      <th>Offer %</th>\n",
              "      <th>norating1</th>\n",
              "      <th>noreviews1</th>\n",
              "      <th>star_5f</th>\n",
              "      <th>star_4f</th>\n",
              "      <th>star_3f</th>\n",
              "      <th>star_2f</th>\n",
              "      <th>star_1f</th>\n",
              "      <th>fulfilled1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>16695</th>\n",
              "      <td>Fashionable &amp; Comfortable Bellies For Women  (...</td>\n",
              "      <td>3.9</td>\n",
              "      <td>Women</td>\n",
              "      <td>Flipkart</td>\n",
              "      <td>698</td>\n",
              "      <td>999</td>\n",
              "      <td>30.13%</td>\n",
              "      <td>38.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5120</th>\n",
              "      <td>Combo Pack of 4 Casual Shoes Sneakers For Men ...</td>\n",
              "      <td>3.8</td>\n",
              "      <td>Men</td>\n",
              "      <td>Flipkart</td>\n",
              "      <td>999</td>\n",
              "      <td>1999</td>\n",
              "      <td>50.03%</td>\n",
              "      <td>531.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>264.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>29</td>\n",
              "      <td>73</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18391</th>\n",
              "      <td>Cilia Mode Leo Sneakers For Women  (White)</td>\n",
              "      <td>4.4</td>\n",
              "      <td>Women</td>\n",
              "      <td>Flipkart</td>\n",
              "      <td>2749</td>\n",
              "      <td>4999</td>\n",
              "      <td>45.01%</td>\n",
              "      <td>17.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>495</th>\n",
              "      <td>Men Black Sports Sandal</td>\n",
              "      <td>4.2</td>\n",
              "      <td>Men</td>\n",
              "      <td>Flipkart</td>\n",
              "      <td>518</td>\n",
              "      <td>724</td>\n",
              "      <td>15.85%</td>\n",
              "      <td>46413.0</td>\n",
              "      <td>6229.0</td>\n",
              "      <td>1045.0</td>\n",
              "      <td>12416.0</td>\n",
              "      <td>5352.0</td>\n",
              "      <td>701</td>\n",
              "      <td>4595</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16408</th>\n",
              "      <td>Men Green Sports Sandal</td>\n",
              "      <td>3.9</td>\n",
              "      <td>Men</td>\n",
              "      <td>Flipkart</td>\n",
              "      <td>1379</td>\n",
              "      <td>2299</td>\n",
              "      <td>40.02%</td>\n",
              "      <td>77.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c42a8dfd-ef3e-4ab9-87bb-dff7d86dfb11')\"\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",
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              "  </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",
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              "      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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c42a8dfd-ef3e-4ab9-87bb-dff7d86dfb11 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-c42a8dfd-ef3e-4ab9-87bb-dff7d86dfb11');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_BzxyUBuPX8y",
        "outputId": "ffddc499-32df-4913-815b-ae44a08b309c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15730, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "vNnGqEikN1Mh",
        "outputId": "892e0c90-0459-49e2-8d50-b7bd60630645"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Rating        price1     actprice1      norating1    noreviews1  \\\n",
              "count  15730.000000  15730.000000  15730.000000   15052.000000  15152.000000   \n",
              "mean       4.012873    688.070693   1369.286777    3057.660776    423.976307   \n",
              "std        0.298440    649.409586   1240.900227   11846.965689   1768.230384   \n",
              "min        0.000000     69.000000     42.000000       1.000000      0.000000   \n",
              "25%        3.900000    349.000000    699.000000      63.000000      9.000000   \n",
              "50%        4.000000    474.000000    999.000000     308.000000     44.000000   \n",
              "75%        4.200000    699.000000   1299.000000    1526.000000    215.000000   \n",
              "max        5.000000   5998.000000  13499.000000  289973.000000  45448.000000   \n",
              "\n",
              "             star_5f       star_4f       star_3f       star_2f       star_1f  \\\n",
              "count   15142.000000  15191.000000  15499.000000  15730.000000  15730.000000   \n",
              "mean     1585.239466    655.923310    357.260662    155.085188    275.500572   \n",
              "std      6177.476241   2855.735531   1402.246610    558.650254    958.589075   \n",
              "min         0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "25%        30.000000     12.000000      7.000000      3.000000      6.000000   \n",
              "50%       150.000000     60.000000     34.000000     17.000000     30.000000   \n",
              "75%       788.000000    300.000000    172.000000     77.000000    140.000000   \n",
              "max    151193.000000  74037.000000  34978.000000  11705.000000  18060.000000   \n",
              "\n",
              "         fulfilled1  \n",
              "count  15730.000000  \n",
              "mean       0.601526  \n",
              "std        0.489600  \n",
              "min        0.000000  \n",
              "25%        0.000000  \n",
              "50%        1.000000  \n",
              "75%        1.000000  \n",
              "max        1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2874fe34-0263-4439-99be-5a989747e339\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Rating</th>\n",
              "      <th>price1</th>\n",
              "      <th>actprice1</th>\n",
              "      <th>norating1</th>\n",
              "      <th>noreviews1</th>\n",
              "      <th>star_5f</th>\n",
              "      <th>star_4f</th>\n",
              "      <th>star_3f</th>\n",
              "      <th>star_2f</th>\n",
              "      <th>star_1f</th>\n",
              "      <th>fulfilled1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>15730.000000</td>\n",
              "      <td>15730.000000</td>\n",
              "      <td>15730.000000</td>\n",
              "      <td>15052.000000</td>\n",
              "      <td>15152.000000</td>\n",
              "      <td>15142.000000</td>\n",
              "      <td>15191.000000</td>\n",
              "      <td>15499.000000</td>\n",
              "      <td>15730.000000</td>\n",
              "      <td>15730.000000</td>\n",
              "      <td>15730.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.012873</td>\n",
              "      <td>688.070693</td>\n",
              "      <td>1369.286777</td>\n",
              "      <td>3057.660776</td>\n",
              "      <td>423.976307</td>\n",
              "      <td>1585.239466</td>\n",
              "      <td>655.923310</td>\n",
              "      <td>357.260662</td>\n",
              "      <td>155.085188</td>\n",
              "      <td>275.500572</td>\n",
              "      <td>0.601526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.298440</td>\n",
              "      <td>649.409586</td>\n",
              "      <td>1240.900227</td>\n",
              "      <td>11846.965689</td>\n",
              "      <td>1768.230384</td>\n",
              "      <td>6177.476241</td>\n",
              "      <td>2855.735531</td>\n",
              "      <td>1402.246610</td>\n",
              "      <td>558.650254</td>\n",
              "      <td>958.589075</td>\n",
              "      <td>0.489600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.900000</td>\n",
              "      <td>349.000000</td>\n",
              "      <td>699.000000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>474.000000</td>\n",
              "      <td>999.000000</td>\n",
              "      <td>308.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.200000</td>\n",
              "      <td>699.000000</td>\n",
              "      <td>1299.000000</td>\n",
              "      <td>1526.000000</td>\n",
              "      <td>215.000000</td>\n",
              "      <td>788.000000</td>\n",
              "      <td>300.000000</td>\n",
              "      <td>172.000000</td>\n",
              "      <td>77.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>5998.000000</td>\n",
              "      <td>13499.000000</td>\n",
              "      <td>289973.000000</td>\n",
              "      <td>45448.000000</td>\n",
              "      <td>151193.000000</td>\n",
              "      <td>74037.000000</td>\n",
              "      <td>34978.000000</td>\n",
              "      <td>11705.000000</td>\n",
              "      <td>18060.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2874fe34-0263-4439-99be-5a989747e339 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-2874fe34-0263-4439-99be-5a989747e339');\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
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4RFXLk0OTN5D",
        "outputId": "5a6a6784-005a-46bd-a6d1-2ee6a4169957"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "title         False\n",
              "Rating        False\n",
              "maincateg      True\n",
              "platform      False\n",
              "price1        False\n",
              "actprice1     False\n",
              "Offer %       False\n",
              "norating1      True\n",
              "noreviews1     True\n",
              "star_5f        True\n",
              "star_4f        True\n",
              "star_3f        True\n",
              "star_2f       False\n",
              "star_1f       False\n",
              "fulfilled1    False\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "df.isnull().any()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mfQDKte6sPUL",
        "outputId": "380e9f9c-6ef8-4723-934f-063f03ab3419"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "title         False\n",
              "Rating         True\n",
              "maincateg      True\n",
              "platform      False\n",
              "actprice1     False\n",
              "norating1     False\n",
              "noreviews1    False\n",
              "star_5f        True\n",
              "star_4f       False\n",
              "star_3f       False\n",
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              "star_1f        True\n",
              "fulfilled1    False\n",
              "dtype: bool"
            ]
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          "metadata": {},
          "execution_count": 10
        }
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      "source": [
        "test_df.isnull().any()"
      ]
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "tg2bYxbjRrqi",
        "outputId": "94ebba28-b187-469e-b280-8e07ad618b71"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 15730 entries, 16695 to 13682\n",
            "Data columns (total 15 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   title       15730 non-null  object \n",
            " 1   Rating      15730 non-null  float64\n",
            " 2   maincateg   15204 non-null  object \n",
            " 3   platform    15730 non-null  object \n",
            " 4   price1      15730 non-null  int64  \n",
            " 5   actprice1   15730 non-null  int64  \n",
            " 6   Offer %     15730 non-null  object \n",
            " 7   norating1   15052 non-null  float64\n",
            " 8   noreviews1  15152 non-null  float64\n",
            " 9   star_5f     15142 non-null  float64\n",
            " 10  star_4f     15191 non-null  float64\n",
            " 11  star_3f     15499 non-null  float64\n",
            " 12  star_2f     15730 non-null  int64  \n",
            " 13  star_1f     15730 non-null  int64  \n",
            " 14  fulfilled1  15730 non-null  int64  \n",
            "dtypes: float64(6), int64(5), object(4)\n",
            "memory usage: 1.9+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lngx6UcIvyUU",
        "outputId": "d2f0d21a-8ae4-48c1-8cb8-00de662ec942"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 5244 entries, 2242 to 13574\n",
            "Data columns (total 13 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   title       5244 non-null   object \n",
            " 1   Rating      5041 non-null   float64\n",
            " 2   maincateg   5177 non-null   object \n",
            " 3   platform    5244 non-null   object \n",
            " 4   actprice1   5244 non-null   int64  \n",
            " 5   norating1   5244 non-null   int64  \n",
            " 6   noreviews1  5244 non-null   int64  \n",
            " 7   star_5f     5176 non-null   float64\n",
            " 8   star_4f     5244 non-null   int64  \n",
            " 9   star_3f     5244 non-null   int64  \n",
            " 10  star_2f     5244 non-null   int64  \n",
            " 11  star_1f     5058 non-null   float64\n",
            " 12  fulfilled1  5244 non-null   int64  \n",
            "dtypes: float64(3), int64(7), object(3)\n",
            "memory usage: 573.6+ KB\n"
          ]
        }
      ],
      "source": [
        "test_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uC-CPhxuRjkN"
      },
      "outputs": [],
      "source": [
        "df['norating1']=df['norating1'].fillna(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0SAv3H91UECV"
      },
      "outputs": [],
      "source": [
        "df['noreviews1']=df['noreviews1'].fillna(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TLQIZMj4UuyL"
      },
      "outputs": [],
      "source": [
        "df['star_5f'] = df['star_5f'].fillna(0)\n",
        "df['star_4f'] = df['star_4f'].fillna(0)\n",
        "df['star_3f'] = df['star_3f'].fillna(0)\n",
        "\n",
        "test_df['star_5f'] = test_df['star_5f'].fillna(0)\n",
        "test_df['star_1f'] = test_df['star_1f'].fillna(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3tz10h9SUXga"
      },
      "outputs": [],
      "source": [
        "df['star_5f'] = df['star_5f'].astype('int64')\n",
        "df['star_4f'] = df['star_4f'].astype('int64')\n",
        "df['star_3f'] = df['star_3f'].astype('int64')\n",
        "df['norating1'] = df['norating1'].astype('int64')\n",
        "df['noreviews1'] = df['noreviews1'].astype('int64')\n",
        "\n",
        "test_df['star_5f'] = test_df['star_5f'].astype('int64')\n",
        "test_df['star_1f'] = test_df['star_1f'].astype('int64')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IIU550WGU4TF"
      },
      "outputs": [],
      "source": [
        "df['fulfilled1'] = df['fulfilled1'].astype('uint8')\n",
        "test_df['fulfilled1'] = test_df['fulfilled1'].astype('uint8')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        "id": "pBnyJr84V3rG",
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              "                                            title  Rating maincateg  platform  \\\n",
              "id                                                                              \n",
              "12336                    Women Beige Heels Sandal     4.0       NaN  Flipkart   \n",
              "20804             Men Brown, Orange Sports Sandal     4.1       NaN  Flipkart   \n",
              "5575                    Women Copper Flats Sandal     4.1       NaN  Flipkart   \n",
              "4262                                     Slippers     3.6       NaN  Flipkart   \n",
              "2767   Ace Slip-On Running Shoes For Men  (Black)     4.3       NaN  Flipkart   \n",
              "\n",
              "       price1  actprice1 Offer %  norating1  noreviews1  star_5f  star_4f  \\\n",
              "id                                                                          \n",
              "12336     499        999  50.05%          0           0       28        9   \n",
              "20804     819        999  18.02%      26640        3667    14148        0   \n",
              "5575      349        999  65.07%          0          27        0        0   \n",
              "4262      213        249  14.46%          0           0        0        0   \n",
              "2767     2999       4999  40.01%          0           0       46       27   \n",
              "\n",
              "       star_3f  star_2f  star_1f  fulfilled1  \n",
              "id                                            \n",
              "12336       10        2        5           0  \n",
              "20804        0     1113     2094           1  \n",
              "5575        22        5       15           1  \n",
              "4262       466      281      476           1  \n",
              "2767         5        1        5           1  "
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "df[df['maincateg'].isnull()].head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "lY-xF4F1Vewe",
        "outputId": "757b91dc-5904-4d83-df90-53bb6e6069c3"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Flip Flops                                  495\n",
              "Slides                                      433\n",
              "Slippers                                    356\n",
              "Women Black Heels Sandal                    341\n",
              "Women Black Wedges Sandal                   252\n",
              "                                           ... \n",
              "Ebernon Low Casuals For Men  (White)          1\n",
              "Casuals For Women  (Brown)                    1\n",
              "CLASSIC PARTYWEAR Slip On For Men  (Tan)      1\n",
              "KELDO Casuals For Men  (Black)                1\n",
              "Blink Duo IDP Flip Flops                      1\n",
              "Name: title, Length: 4782, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
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      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Vc7kxMZFV7iz"
      },
      "outputs": [],
      "source": [
        "temp = df[df['maincateg'].isnull()].copy()\n",
        "women = [\"Women\", \"Women's\", \"WOMEN\", \"Womens\", \"WOMENS\"]\n",
        "men = [\"Men\", \"Men's\", \"Boy\", \"men\"]\n",
        "\n",
        "category = []\n",
        "for title in temp['title'].values:\n",
        "  title = title.replace(u'\\xa0', u' ')\n",
        "  if any(x in title.split(\" \") for x in women):\n",
        "    category.append(\"Women\")\n",
        "  elif any(x in title.split(\" \") for x in men):\n",
        "    category.append(\"Men\")\n",
        "  else:\n",
        "    category.append(np.nan)\n",
        "\n",
        "temp['maincateg'] = category"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RhoPToGeBdgU"
      },
      "outputs": [],
      "source": [
        "df.dropna(axis=0, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i-oCkARbCbxi"
      },
      "outputs": [],
      "source": [
        "df = pd.concat([df, temp])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PO5TDEkHNClL",
        "outputId": "88c409b9-70d2-42f6-d657-70545fad9bf8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Slides                                                                                                              16\n",
              "Flip Flops                                                                                                          16\n",
              "Slippers                                                                                                            15\n",
              "Ketava Graphic v2 IDP Slippers                                                                                       1\n",
              "Pink Fluffy Fur Bow Winter Slides                                                                                    1\n",
              "crocs Unisex's Classic Clogs                                                                                         1\n",
              "Gliders (from Liberty)                                                                                               1\n",
              "Stamp IDP Flip Flops                                                                                                 1\n",
              "NIKE CHROMA THONG 5 Slippers                                                                                         1\n",
              "ASIAN Captain-12 Latest Casual Sneakers,Lace up Lightweight Shoes for Running Walking, Gym,Sports,Hiking & Party     1\n",
              "WF_HF_06 Flip Flops                                                                                                  1\n",
              "SFG 48 Beige Slippers                                                                                                1\n",
              "Name: title, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "df[df['maincateg'].isnull()]['title'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bwJvQpc9uRwa"
      },
      "outputs": [],
      "source": [
        "temp = test_df[test_df['Rating'].isnull()].copy()\n",
        "rating = []\n",
        "\n",
        "for i in range(len(temp)):\n",
        "  rating.append(round((temp.iloc[i]['star_1f'] + 2*temp.iloc[i]['star_2f'] + 3*temp.iloc[i]['star_3f'] + 4*temp.iloc[i]['star_4f'] + 5*temp.iloc[i]['star_5f'])/temp.iloc[i]['norating1'], 1))\n",
        "\n",
        "temp['Rating'] = rating"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5JRHvX-c4JSx"
      },
      "outputs": [],
      "source": [
        "test_df = test_df[test_df['Rating'].notna()]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0HssBq700I2O"
      },
      "outputs": [],
      "source": [
        "test_df = pd.concat([test_df, temp])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c9YvKAvFsw-S"
      },
      "outputs": [],
      "source": [
        "temp = test_df[test_df['maincateg'].isnull()].copy()\n",
        "women = [\"Women\", \"Women's\", \"WOMEN\", \"Womens\", \"WOMENS\"]\n",
        "men = [\"Men\", \"Men's\", \"Boy\", \"men\"]\n",
        "\n",
        "category = []\n",
        "for title in temp['title'].values:\n",
        "  title = title.replace(u'\\xa0', u' ')\n",
        "  if any(x in title.split(\" \") for x in women):\n",
        "    category.append(\"Women\")\n",
        "  elif any(x in title.split(\" \") for x in men):\n",
        "    category.append(\"Men\")\n",
        "  else:\n",
        "    category.append(np.nan)\n",
        "\n",
        "temp['maincateg'] = category"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hNWL4jCwtKI4"
      },
      "outputs": [],
      "source": [
        "test_df.dropna(axis=0, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2dBX5No0tM1d"
      },
      "outputs": [],
      "source": [
        "test_df = pd.concat([test_df, temp])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wm4YUXEktR5c",
        "outputId": "b4ed5dee-88e1-4417-c869-86fe6bfe5603"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Flip Flops                     3\n",
              "Slides                         2\n",
              "Slippers                       1\n",
              "Wink Duo GU2 IDP Flip Flops    1\n",
              "Name: title, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "test_df[test_df['maincateg'].isnull()]['title'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hZhzrbGXFUax",
        "outputId": "ba3cde3d-8814-4f40-fef5-60b0dab3d76d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Flipkart    15389\n",
              "Amazon        341\n",
              "Name: platform, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "df['platform'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nihAiwqr4WfU",
        "outputId": "c467b57a-4c94-4c82-eb58-a633dcfe4a39"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Flipkart    5140\n",
              "Amazon       104\n",
              "Name: platform, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "test_df['platform'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WzeRVwVLn86G"
      },
      "outputs": [],
      "source": [
        "df = pd.get_dummies(df, columns=['maincateg'])\n",
        "test_df = pd.get_dummies(test_df, columns=['maincateg'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m0Aama_44-RL"
      },
      "outputs": [],
      "source": [
        "le = LabelEncoder()\n",
        "df['platform'] = le.fit_transform(df['platform']).astype('uint8')\n",
        "test_df['platform'] = le.transform(test_df['platform']).astype('uint8')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6r_AcpHLSBaC"
      },
      "outputs": [],
      "source": [
        "df.drop(['title', 'Offer %'], axis=1, inplace=True)\n",
        "test_df.drop(['title'], axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Converting categorical object type to sparse matrix\n",
        "corr_matrix = df.corr()\n",
        "# Create a mask\n",
        "mask = np.tril(np.ones_like(corr_matrix, dtype=bool))\n",
        "\n",
        "fig=plt.figure(figsize=(16, 8))\n",
        "fig.suptitle('Plotting Correlation Matrix\\n_________________', y=0.94, horizontalalignment='center', verticalalignment='center',fontsize=18, fontweight='bold',\n",
        "              fontfamily='serif', color=\"black\")\n",
        "background_color = \"#ffe6e6\"\n",
        "fig.patch.set_facecolor(background_color)\n",
        "\n",
        "sns.heatmap(corr_matrix, mask=mask, annot=True, cmap='coolwarm')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        },
        "id": "3yS9QGvonF4p",
        "outputId": "767ef8ff-d415-42ed-c654-878a6fbbe701"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig=plt.figure(figsize=(24, 8))\n",
        "\n",
        "fig.patch.set_facecolor(background_color)\n",
        "fig.suptitle('Rating vs price1\\n_________________', y=0.94, horizontalalignment='center', verticalalignment='center',fontsize=18, fontweight='bold',\n",
        "              fontfamily='serif', color=\"black\")\n",
        "\n",
        "sns.stripplot(data=df, x='Rating', y='price1')\n",
        "plt.grid(color='black', linestyle=':', axis='y', zorder=0,  dashes=(1,5))\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "Ke9CPuu2EzK_",
        "outputId": "d4c11806-6aab-4335-b2aa-9bdd4328f873"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1728x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.lmplot(data=df, x='actprice1', y='price1', height=8, aspect=3)\n",
        "plt.grid(color='black', linestyle=':', axis='y', zorder=0,  dashes=(1,5))\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "H3Tc4u65Na9O",
        "outputId": "8c9f14d6-7221-4b02-c212-322c5debb974"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1728x576 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig=plt.figure(figsize=(24, 8))\n",
        "gs=fig.add_gridspec(1,3)\n",
        "gs.update(wspace=0.3, hspace=0.2)\n",
        "ax1=fig.add_subplot(gs[0,0])\n",
        "ax2=fig.add_subplot(gs[0,1])\n",
        "ax3=fig.add_subplot(gs[0,2])\n",
        "\n",
        "fig.patch.set_facecolor(background_color)\n",
        "fig.suptitle('fulfilled1 vs price1\\n_________________', y=0.94, horizontalalignment='center', verticalalignment='center',fontsize=18, fontweight='bold',\n",
        "              fontfamily='serif', color=\"black\")\n",
        "\n",
        "sns.stripplot(data=df, x='fulfilled1', y='price1', ax=ax1)\n",
        "ax1.grid(color='black', linestyle=':', axis='y', zorder=0,  dashes=(1,5))\n",
        "\n",
        "sns.violinplot(data=df, x='fulfilled1', y='price1', ax=ax2)\n",
        "ax2.grid(color='black', linestyle=':', axis='y', zorder=0,  dashes=(1,5))\n",
        "\n",
        "sns.boxenplot(data=df, x='fulfilled1', y='price1', ax=ax3)\n",
        "ax3.grid(color='black', linestyle=':', axis='y', zorder=0,  dashes=(1,5))\n",
        "\n",
        "for ax in [ax1, ax2, ax3]:\n",
        "  ax.set_facecolor(background_color)\n",
        "  ax.set_ylabel(None)\n",
        "  for borders in [\"top\",\"right\",\"left\"]:\n",
        "    ax.spines[borders].set_visible(False)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "jdPqHBY_IEPp",
        "outputId": "56715489-2ad8-4352-ee71-30880f5e7c32"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1728x576 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "target = df.pop('price1')"
      ],
      "metadata": {
        "id": "xDDte0jqn7Ic"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OFNNbdigj_Ya"
      },
      "outputs": [],
      "source": [
        "con_cols = ['Rating', 'actprice1', 'norating1', 'noreviews1', 'star_5f', 'star_4f', 'star_3f', 'star_2f', 'star_1f']\n",
        "cat_cols = ['platform', 'fulfilled1', 'maincateg_Men', 'maincateg_Women']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2K3PNyI2RVUI"
      },
      "outputs": [],
      "source": [
        "X_train, X_val, y_train, y_val = train_test_split(df, target, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Tz5lNKQMj2P6"
      },
      "outputs": [],
      "source": [
        "scaler = StandardScaler()\n",
        "X_train[con_cols] = scaler.fit_transform(X_train[con_cols])\n",
        "X_val[con_cols] = scaler.transform(X_val[con_cols])\n",
        "test_df[con_cols] = scaler.transform(test_df[con_cols])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QMON8wChwzUe"
      },
      "source": [
        "# XGB"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yZa7U1jLxE7l"
      },
      "outputs": [],
      "source": [
        "dtrain = xgb.DMatrix(X_train, label=y_train)\n",
        "dval = xgb.DMatrix(X_val, label=y_val)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = df.copy()\n",
        "df1[con_cols] = scaler.transform(df1[con_cols])"
      ],
      "metadata": {
        "id": "MLpG_SXykBgM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dcv = xgb.DMatrix(df1, label=target)"
      ],
      "metadata": {
        "id": "n8TRiExdj0aF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "28UPo-HCxcqN"
      },
      "outputs": [],
      "source": [
        "params = {\n",
        "    'max_depth':8,\n",
        "    'min_child_weight': 1,\n",
        "    'eta':0.1,\n",
        "    'subsample': 1,\n",
        "    'colsample_bytree': 1,\n",
        "    'eval_metric': 'rmse',\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Acd1SWJCfNFr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bcd159ea-bd6c-4960-d0ab-c3e5797d0add"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\tVal-rmse:858.342\n",
            "Will train until Val-rmse hasn't improved in 20 rounds.\n",
            "[1]\tVal-rmse:782.533\n",
            "[2]\tVal-rmse:714.581\n",
            "[3]\tVal-rmse:654.342\n",
            "[4]\tVal-rmse:600.715\n",
            "[5]\tVal-rmse:552.632\n",
            "[6]\tVal-rmse:510.522\n",
            "[7]\tVal-rmse:473.467\n",
            "[8]\tVal-rmse:440.879\n",
            "[9]\tVal-rmse:412.547\n",
            "[10]\tVal-rmse:387.82\n",
            "[11]\tVal-rmse:365.264\n",
            "[12]\tVal-rmse:346.504\n",
            "[13]\tVal-rmse:330.523\n",
            "[14]\tVal-rmse:316.095\n",
            "[15]\tVal-rmse:303.365\n",
            "[16]\tVal-rmse:292.705\n",
            "[17]\tVal-rmse:282.715\n",
            "[18]\tVal-rmse:274.557\n",
            "[19]\tVal-rmse:267.982\n",
            "[20]\tVal-rmse:261.847\n",
            "[21]\tVal-rmse:256.864\n",
            "[22]\tVal-rmse:253.061\n",
            "[23]\tVal-rmse:249.561\n",
            "[24]\tVal-rmse:245.968\n",
            "[25]\tVal-rmse:243.359\n",
            "[26]\tVal-rmse:241.101\n",
            "[27]\tVal-rmse:238.843\n",
            "[28]\tVal-rmse:237.092\n",
            "[29]\tVal-rmse:235.112\n",
            "[30]\tVal-rmse:233.34\n",
            "[31]\tVal-rmse:232.27\n",
            "[32]\tVal-rmse:231.372\n",
            "[33]\tVal-rmse:230.413\n",
            "[34]\tVal-rmse:229.704\n",
            "[35]\tVal-rmse:228.404\n",
            "[36]\tVal-rmse:227.437\n",
            "[37]\tVal-rmse:226.707\n",
            "[38]\tVal-rmse:225.829\n",
            "[39]\tVal-rmse:225.247\n",
            "[40]\tVal-rmse:224.802\n",
            "[41]\tVal-rmse:223.966\n",
            "[42]\tVal-rmse:223.368\n",
            "[43]\tVal-rmse:222.7\n",
            "[44]\tVal-rmse:222.252\n",
            "[45]\tVal-rmse:221.74\n",
            "[46]\tVal-rmse:221.396\n",
            "[47]\tVal-rmse:221.062\n",
            "[48]\tVal-rmse:220.543\n",
            "[49]\tVal-rmse:220.387\n",
            "[50]\tVal-rmse:219.979\n",
            "[51]\tVal-rmse:219.812\n",
            "[52]\tVal-rmse:219.529\n",
            "[53]\tVal-rmse:219.348\n",
            "[54]\tVal-rmse:218.989\n",
            "[55]\tVal-rmse:218.811\n",
            "[56]\tVal-rmse:218.959\n",
            "[57]\tVal-rmse:218.281\n",
            "[58]\tVal-rmse:217.958\n",
            "[59]\tVal-rmse:217.755\n",
            "[60]\tVal-rmse:217.49\n",
            "[61]\tVal-rmse:217.264\n",
            "[62]\tVal-rmse:216.943\n",
            "[63]\tVal-rmse:216.671\n",
            "[64]\tVal-rmse:216.565\n",
            "[65]\tVal-rmse:216.261\n",
            "[66]\tVal-rmse:216.122\n",
            "[67]\tVal-rmse:215.8\n",
            "[68]\tVal-rmse:215.684\n",
            "[69]\tVal-rmse:215.589\n",
            "[70]\tVal-rmse:215.303\n",
            "[71]\tVal-rmse:215.021\n",
            "[72]\tVal-rmse:214.982\n",
            "[73]\tVal-rmse:214.885\n",
            "[74]\tVal-rmse:214.762\n",
            "[75]\tVal-rmse:214.447\n",
            "[76]\tVal-rmse:214.276\n",
            "[77]\tVal-rmse:214.084\n",
            "[78]\tVal-rmse:214.048\n",
            "[79]\tVal-rmse:213.926\n",
            "[80]\tVal-rmse:213.608\n",
            "[81]\tVal-rmse:213.462\n",
            "[82]\tVal-rmse:213.301\n",
            "[83]\tVal-rmse:213.145\n",
            "[84]\tVal-rmse:212.999\n",
            "[85]\tVal-rmse:212.91\n",
            "[86]\tVal-rmse:212.762\n",
            "[87]\tVal-rmse:212.653\n",
            "[88]\tVal-rmse:212.669\n",
            "[89]\tVal-rmse:212.644\n",
            "[90]\tVal-rmse:212.6\n",
            "[91]\tVal-rmse:212.031\n",
            "[92]\tVal-rmse:211.623\n",
            "[93]\tVal-rmse:211.346\n",
            "[94]\tVal-rmse:211.337\n",
            "[95]\tVal-rmse:210.974\n",
            "[96]\tVal-rmse:210.693\n",
            "[97]\tVal-rmse:210.573\n",
            "[98]\tVal-rmse:210.362\n",
            "[99]\tVal-rmse:209.894\n",
            "[100]\tVal-rmse:209.765\n",
            "[101]\tVal-rmse:209.507\n",
            "[102]\tVal-rmse:209.254\n",
            "[103]\tVal-rmse:209.103\n",
            "[104]\tVal-rmse:208.93\n",
            "[105]\tVal-rmse:208.895\n",
            "[106]\tVal-rmse:208.856\n",
            "[107]\tVal-rmse:208.792\n",
            "[108]\tVal-rmse:208.563\n",
            "[109]\tVal-rmse:208.581\n",
            "[110]\tVal-rmse:208.573\n",
            "[111]\tVal-rmse:208.528\n",
            "[112]\tVal-rmse:208.252\n",
            "[113]\tVal-rmse:208.223\n",
            "[114]\tVal-rmse:208.117\n",
            "[115]\tVal-rmse:207.955\n",
            "[116]\tVal-rmse:207.897\n",
            "[117]\tVal-rmse:207.88\n",
            "[118]\tVal-rmse:207.832\n",
            "[119]\tVal-rmse:207.681\n",
            "[120]\tVal-rmse:207.644\n",
            "[121]\tVal-rmse:207.63\n",
            "[122]\tVal-rmse:207.521\n",
            "[123]\tVal-rmse:207.504\n",
            "[124]\tVal-rmse:207.434\n",
            "[125]\tVal-rmse:207.239\n",
            "[126]\tVal-rmse:207.075\n",
            "[127]\tVal-rmse:207.033\n",
            "[128]\tVal-rmse:206.924\n",
            "[129]\tVal-rmse:206.85\n",
            "[130]\tVal-rmse:206.838\n",
            "[131]\tVal-rmse:206.726\n",
            "[132]\tVal-rmse:206.642\n",
            "[133]\tVal-rmse:206.531\n",
            "[134]\tVal-rmse:206.394\n",
            "[135]\tVal-rmse:206.367\n",
            "[136]\tVal-rmse:206.326\n",
            "[137]\tVal-rmse:206.139\n",
            "[138]\tVal-rmse:206.087\n",
            "[139]\tVal-rmse:205.957\n",
            "[140]\tVal-rmse:205.927\n",
            "[141]\tVal-rmse:205.821\n",
            "[142]\tVal-rmse:205.727\n",
            "[143]\tVal-rmse:205.579\n",
            "[144]\tVal-rmse:205.556\n",
            "[145]\tVal-rmse:205.441\n",
            "[146]\tVal-rmse:205.208\n",
            "[147]\tVal-rmse:205.182\n",
            "[148]\tVal-rmse:205.093\n",
            "[149]\tVal-rmse:204.98\n",
            "[150]\tVal-rmse:204.952\n",
            "[151]\tVal-rmse:205.091\n",
            "[152]\tVal-rmse:205.043\n",
            "[153]\tVal-rmse:204.981\n",
            "[154]\tVal-rmse:204.958\n",
            "[155]\tVal-rmse:204.943\n",
            "[156]\tVal-rmse:204.863\n",
            "[157]\tVal-rmse:204.823\n",
            "[158]\tVal-rmse:204.807\n",
            "[159]\tVal-rmse:204.785\n",
            "[160]\tVal-rmse:204.739\n",
            "[161]\tVal-rmse:204.705\n",
            "[162]\tVal-rmse:204.681\n",
            "[163]\tVal-rmse:204.537\n",
            "[164]\tVal-rmse:204.526\n",
            "[165]\tVal-rmse:204.501\n",
            "[166]\tVal-rmse:204.407\n",
            "[167]\tVal-rmse:204.402\n",
            "[168]\tVal-rmse:204.382\n",
            "[169]\tVal-rmse:204.329\n",
            "[170]\tVal-rmse:204.377\n",
            "[171]\tVal-rmse:204.366\n",
            "[172]\tVal-rmse:204.402\n",
            "[173]\tVal-rmse:204.428\n",
            "[174]\tVal-rmse:204.366\n",
            "[175]\tVal-rmse:204.305\n",
            "[176]\tVal-rmse:204.287\n",
            "[177]\tVal-rmse:204.251\n",
            "[178]\tVal-rmse:204.248\n",
            "[179]\tVal-rmse:204.235\n",
            "[180]\tVal-rmse:204.241\n",
            "[181]\tVal-rmse:204.236\n",
            "[182]\tVal-rmse:204.113\n",
            "[183]\tVal-rmse:204.094\n",
            "[184]\tVal-rmse:204.092\n",
            "[185]\tVal-rmse:204.006\n",
            "[186]\tVal-rmse:203.935\n",
            "[187]\tVal-rmse:203.886\n",
            "[188]\tVal-rmse:203.864\n",
            "[189]\tVal-rmse:203.833\n",
            "[190]\tVal-rmse:203.693\n",
            "[191]\tVal-rmse:203.615\n",
            "[192]\tVal-rmse:203.586\n",
            "[193]\tVal-rmse:203.566\n",
            "[194]\tVal-rmse:203.526\n",
            "[195]\tVal-rmse:203.516\n",
            "[196]\tVal-rmse:203.507\n",
            "[197]\tVal-rmse:203.448\n",
            "[198]\tVal-rmse:203.44\n",
            "[199]\tVal-rmse:203.443\n",
            "[200]\tVal-rmse:203.473\n",
            "[201]\tVal-rmse:203.475\n",
            "[202]\tVal-rmse:203.453\n",
            "[203]\tVal-rmse:203.422\n",
            "[204]\tVal-rmse:203.472\n",
            "[205]\tVal-rmse:203.507\n",
            "[206]\tVal-rmse:203.489\n",
            "[207]\tVal-rmse:203.482\n",
            "[208]\tVal-rmse:203.545\n",
            "[209]\tVal-rmse:203.563\n",
            "[210]\tVal-rmse:203.597\n",
            "[211]\tVal-rmse:203.592\n",
            "[212]\tVal-rmse:203.581\n",
            "[213]\tVal-rmse:203.513\n",
            "[214]\tVal-rmse:203.333\n",
            "[215]\tVal-rmse:203.121\n",
            "[216]\tVal-rmse:203.074\n",
            "[217]\tVal-rmse:202.963\n",
            "[218]\tVal-rmse:202.935\n",
            "[219]\tVal-rmse:202.821\n",
            "[220]\tVal-rmse:202.787\n",
            "[221]\tVal-rmse:202.822\n",
            "[222]\tVal-rmse:202.761\n",
            "[223]\tVal-rmse:202.776\n",
            "[224]\tVal-rmse:202.685\n",
            "[225]\tVal-rmse:202.651\n",
            "[226]\tVal-rmse:202.652\n",
            "[227]\tVal-rmse:202.672\n",
            "[228]\tVal-rmse:202.581\n",
            "[229]\tVal-rmse:202.551\n",
            "[230]\tVal-rmse:202.542\n",
            "[231]\tVal-rmse:202.52\n",
            "[232]\tVal-rmse:202.529\n",
            "[233]\tVal-rmse:202.51\n",
            "[234]\tVal-rmse:202.473\n",
            "[235]\tVal-rmse:202.391\n",
            "[236]\tVal-rmse:202.373\n",
            "[237]\tVal-rmse:202.394\n",
            "[238]\tVal-rmse:202.315\n",
            "[239]\tVal-rmse:202.26\n",
            "[240]\tVal-rmse:202.179\n",
            "[241]\tVal-rmse:202.177\n",
            "[242]\tVal-rmse:202.169\n",
            "[243]\tVal-rmse:202.127\n",
            "[244]\tVal-rmse:202.107\n",
            "[245]\tVal-rmse:202.131\n",
            "[246]\tVal-rmse:202.133\n",
            "[247]\tVal-rmse:202.195\n",
            "[248]\tVal-rmse:202.198\n",
            "[249]\tVal-rmse:202.14\n",
            "[250]\tVal-rmse:202.141\n",
            "[251]\tVal-rmse:202.08\n",
            "[252]\tVal-rmse:202.046\n",
            "[253]\tVal-rmse:201.987\n",
            "[254]\tVal-rmse:201.951\n",
            "[255]\tVal-rmse:201.958\n",
            "[256]\tVal-rmse:201.949\n",
            "[257]\tVal-rmse:201.948\n",
            "[258]\tVal-rmse:201.888\n",
            "[259]\tVal-rmse:201.89\n",
            "[260]\tVal-rmse:201.88\n",
            "[261]\tVal-rmse:201.838\n",
            "[262]\tVal-rmse:201.836\n",
            "[263]\tVal-rmse:201.833\n",
            "[264]\tVal-rmse:201.824\n",
            "[265]\tVal-rmse:201.76\n",
            "[266]\tVal-rmse:201.649\n",
            "[267]\tVal-rmse:201.607\n",
            "[268]\tVal-rmse:201.565\n",
            "[269]\tVal-rmse:201.563\n",
            "[270]\tVal-rmse:201.54\n",
            "[271]\tVal-rmse:201.54\n",
            "[272]\tVal-rmse:201.522\n",
            "[273]\tVal-rmse:201.456\n",
            "[274]\tVal-rmse:201.422\n",
            "[275]\tVal-rmse:201.395\n",
            "[276]\tVal-rmse:201.379\n",
            "[277]\tVal-rmse:201.266\n",
            "[278]\tVal-rmse:201.249\n",
            "[279]\tVal-rmse:201.248\n",
            "[280]\tVal-rmse:201.24\n",
            "[281]\tVal-rmse:201.198\n",
            "[282]\tVal-rmse:201.19\n",
            "[283]\tVal-rmse:201.154\n",
            "[284]\tVal-rmse:201.178\n",
            "[285]\tVal-rmse:201.172\n",
            "[286]\tVal-rmse:201.17\n",
            "[287]\tVal-rmse:201.171\n",
            "[288]\tVal-rmse:201.114\n",
            "[289]\tVal-rmse:201.081\n",
            "[290]\tVal-rmse:201.05\n",
            "[291]\tVal-rmse:201.047\n",
            "[292]\tVal-rmse:200.982\n",
            "[293]\tVal-rmse:200.91\n",
            "[294]\tVal-rmse:200.924\n",
            "[295]\tVal-rmse:200.934\n",
            "[296]\tVal-rmse:200.941\n",
            "[297]\tVal-rmse:200.973\n",
            "[298]\tVal-rmse:200.951\n",
            "[299]\tVal-rmse:200.879\n",
            "[300]\tVal-rmse:200.873\n",
            "[301]\tVal-rmse:200.875\n",
            "[302]\tVal-rmse:200.868\n",
            "[303]\tVal-rmse:200.912\n",
            "[304]\tVal-rmse:200.897\n",
            "[305]\tVal-rmse:200.9\n",
            "[306]\tVal-rmse:200.844\n",
            "[307]\tVal-rmse:200.816\n",
            "[308]\tVal-rmse:200.829\n",
            "[309]\tVal-rmse:200.782\n",
            "[310]\tVal-rmse:200.775\n",
            "[311]\tVal-rmse:200.808\n",
            "[312]\tVal-rmse:200.79\n",
            "[313]\tVal-rmse:200.769\n",
            "[314]\tVal-rmse:200.766\n",
            "[315]\tVal-rmse:200.739\n",
            "[316]\tVal-rmse:200.731\n",
            "[317]\tVal-rmse:200.72\n",
            "[318]\tVal-rmse:200.754\n",
            "[319]\tVal-rmse:200.748\n",
            "[320]\tVal-rmse:200.775\n",
            "[321]\tVal-rmse:200.806\n",
            "[322]\tVal-rmse:200.792\n",
            "[323]\tVal-rmse:200.802\n",
            "[324]\tVal-rmse:200.773\n",
            "[325]\tVal-rmse:200.702\n",
            "[326]\tVal-rmse:200.679\n",
            "[327]\tVal-rmse:200.657\n",
            "[328]\tVal-rmse:200.656\n",
            "[329]\tVal-rmse:200.634\n",
            "[330]\tVal-rmse:200.597\n",
            "[331]\tVal-rmse:200.576\n",
            "[332]\tVal-rmse:200.542\n",
            "[333]\tVal-rmse:200.504\n",
            "[334]\tVal-rmse:200.498\n",
            "[335]\tVal-rmse:200.495\n",
            "[336]\tVal-rmse:200.465\n",
            "[337]\tVal-rmse:200.484\n",
            "[338]\tVal-rmse:200.477\n",
            "[339]\tVal-rmse:200.463\n",
            "[340]\tVal-rmse:200.469\n",
            "[341]\tVal-rmse:200.381\n",
            "[342]\tVal-rmse:200.347\n",
            "[343]\tVal-rmse:200.258\n",
            "[344]\tVal-rmse:200.239\n",
            "[345]\tVal-rmse:200.19\n",
            "[346]\tVal-rmse:200.178\n",
            "[347]\tVal-rmse:200.146\n",
            "[348]\tVal-rmse:200.174\n",
            "[349]\tVal-rmse:200.175\n",
            "[350]\tVal-rmse:200.167\n",
            "[351]\tVal-rmse:200.129\n",
            "[352]\tVal-rmse:200.154\n",
            "[353]\tVal-rmse:200.151\n",
            "[354]\tVal-rmse:200.133\n",
            "[355]\tVal-rmse:200.111\n",
            "[356]\tVal-rmse:200.114\n",
            "[357]\tVal-rmse:200.066\n",
            "[358]\tVal-rmse:200.064\n",
            "[359]\tVal-rmse:200.037\n",
            "[360]\tVal-rmse:200.034\n",
            "[361]\tVal-rmse:199.991\n",
            "[362]\tVal-rmse:199.976\n",
            "[363]\tVal-rmse:200.005\n",
            "[364]\tVal-rmse:199.998\n",
            "[365]\tVal-rmse:199.969\n",
            "[366]\tVal-rmse:199.956\n",
            "[367]\tVal-rmse:199.952\n",
            "[368]\tVal-rmse:199.906\n",
            "[369]\tVal-rmse:199.903\n",
            "[370]\tVal-rmse:199.91\n",
            "[371]\tVal-rmse:199.917\n",
            "[372]\tVal-rmse:199.913\n",
            "[373]\tVal-rmse:199.916\n",
            "[374]\tVal-rmse:199.876\n",
            "[375]\tVal-rmse:199.887\n",
            "[376]\tVal-rmse:199.841\n",
            "[377]\tVal-rmse:199.83\n",
            "[378]\tVal-rmse:199.804\n",
            "[379]\tVal-rmse:199.803\n",
            "[380]\tVal-rmse:199.812\n",
            "[381]\tVal-rmse:199.788\n",
            "[382]\tVal-rmse:199.779\n",
            "[383]\tVal-rmse:199.759\n",
            "[384]\tVal-rmse:199.74\n",
            "[385]\tVal-rmse:199.731\n",
            "[386]\tVal-rmse:199.726\n",
            "[387]\tVal-rmse:199.747\n",
            "[388]\tVal-rmse:199.763\n",
            "[389]\tVal-rmse:199.747\n",
            "[390]\tVal-rmse:199.764\n",
            "[391]\tVal-rmse:199.741\n",
            "[392]\tVal-rmse:199.725\n",
            "[393]\tVal-rmse:199.668\n",
            "[394]\tVal-rmse:199.667\n",
            "[395]\tVal-rmse:199.672\n",
            "[396]\tVal-rmse:199.667\n",
            "[397]\tVal-rmse:199.661\n",
            "[398]\tVal-rmse:199.627\n",
            "[399]\tVal-rmse:199.633\n",
            "[400]\tVal-rmse:199.631\n",
            "[401]\tVal-rmse:199.629\n",
            "[402]\tVal-rmse:199.624\n",
            "[403]\tVal-rmse:199.601\n",
            "[404]\tVal-rmse:199.578\n",
            "[405]\tVal-rmse:199.577\n",
            "[406]\tVal-rmse:199.633\n",
            "[407]\tVal-rmse:199.608\n",
            "[408]\tVal-rmse:199.59\n",
            "[409]\tVal-rmse:199.594\n",
            "[410]\tVal-rmse:199.57\n",
            "[411]\tVal-rmse:199.568\n",
            "[412]\tVal-rmse:199.619\n",
            "[413]\tVal-rmse:199.612\n",
            "[414]\tVal-rmse:199.591\n",
            "[415]\tVal-rmse:199.571\n",
            "[416]\tVal-rmse:199.561\n",
            "[417]\tVal-rmse:199.556\n",
            "[418]\tVal-rmse:199.556\n",
            "[419]\tVal-rmse:199.554\n",
            "[420]\tVal-rmse:199.542\n",
            "[421]\tVal-rmse:199.546\n",
            "[422]\tVal-rmse:199.528\n",
            "[423]\tVal-rmse:199.508\n",
            "[424]\tVal-rmse:199.492\n",
            "[425]\tVal-rmse:199.505\n",
            "[426]\tVal-rmse:199.489\n",
            "[427]\tVal-rmse:199.471\n",
            "[428]\tVal-rmse:199.461\n",
            "[429]\tVal-rmse:199.447\n",
            "[430]\tVal-rmse:199.445\n",
            "[431]\tVal-rmse:199.442\n",
            "[432]\tVal-rmse:199.438\n",
            "[433]\tVal-rmse:199.466\n",
            "[434]\tVal-rmse:199.465\n",
            "[435]\tVal-rmse:199.468\n",
            "[436]\tVal-rmse:199.48\n",
            "[437]\tVal-rmse:199.472\n",
            "[438]\tVal-rmse:199.454\n",
            "[439]\tVal-rmse:199.447\n",
            "[440]\tVal-rmse:199.491\n",
            "[441]\tVal-rmse:199.491\n",
            "[442]\tVal-rmse:199.491\n",
            "[443]\tVal-rmse:199.483\n",
            "[444]\tVal-rmse:199.478\n",
            "[445]\tVal-rmse:199.476\n",
            "[446]\tVal-rmse:199.484\n",
            "[447]\tVal-rmse:199.483\n",
            "[448]\tVal-rmse:199.495\n",
            "[449]\tVal-rmse:199.48\n",
            "[450]\tVal-rmse:199.475\n",
            "[451]\tVal-rmse:199.452\n",
            "[452]\tVal-rmse:199.455\n",
            "Stopping. Best iteration:\n",
            "[432]\tVal-rmse:199.438\n",
            "\n",
            "Best rmse: 199.44 with 433 rounds\n"
          ]
        }
      ],
      "source": [
        "num_boost_round = 1000\n",
        "\n",
        "model = xgb.train(\n",
        "    params,\n",
        "    dtrain,\n",
        "    num_boost_round=num_boost_round,\n",
        "    evals=[(dval, \"Val\")],\n",
        "    early_stopping_rounds=20\n",
        ")\n",
        "\n",
        "print(\"Best rmse: {:.2f} with {} rounds\".format(\n",
        "                 model.best_score,\n",
        "                 model.best_iteration+1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "MS5MidKOyfNc",
        "outputId": "5b9d8ad0-155c-466d-d8bc-5aef4e065b14"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     train-rmse-mean  train-rmse-std  test-rmse-mean  test-rmse-std\n",
              "0         859.235062        4.805607      859.978958      14.382449\n",
              "1         781.717545        4.486880      783.476944      13.516075\n",
              "2         712.387512        4.102546      715.413651      13.593757\n",
              "3         650.286179        3.772440      654.879989      13.780268\n",
              "4         594.713578        3.560288      601.174958      13.933330\n",
              "..               ...             ...             ...            ...\n",
              "677        32.244683        1.124562      205.962105      14.040431\n",
              "678        32.213986        1.136499      205.961864      14.041560\n",
              "679        32.161231        1.150804      205.959423      14.053117\n",
              "680        32.116847        1.172583      205.956543      14.057994\n",
              "681        32.075182        1.189818      205.954907      14.056224\n",
              "\n",
              "[682 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f5dd486c-9169-440b-82f5-f9abe77a16ff\">\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>train-rmse-mean</th>\n",
              "      <th>train-rmse-std</th>\n",
              "      <th>test-rmse-mean</th>\n",
              "      <th>test-rmse-std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>859.235062</td>\n",
              "      <td>4.805607</td>\n",
              "      <td>859.978958</td>\n",
              "      <td>14.382449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>781.717545</td>\n",
              "      <td>4.486880</td>\n",
              "      <td>783.476944</td>\n",
              "      <td>13.516075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>712.387512</td>\n",
              "      <td>4.102546</td>\n",
              "      <td>715.413651</td>\n",
              "      <td>13.593757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>650.286179</td>\n",
              "      <td>3.772440</td>\n",
              "      <td>654.879989</td>\n",
              "      <td>13.780268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>594.713578</td>\n",
              "      <td>3.560288</td>\n",
              "      <td>601.174958</td>\n",
              "      <td>13.933330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>677</th>\n",
              "      <td>32.244683</td>\n",
              "      <td>1.124562</td>\n",
              "      <td>205.962105</td>\n",
              "      <td>14.040431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>678</th>\n",
              "      <td>32.213986</td>\n",
              "      <td>1.136499</td>\n",
              "      <td>205.961864</td>\n",
              "      <td>14.041560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>679</th>\n",
              "      <td>32.161231</td>\n",
              "      <td>1.150804</td>\n",
              "      <td>205.959423</td>\n",
              "      <td>14.053117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>680</th>\n",
              "      <td>32.116847</td>\n",
              "      <td>1.172583</td>\n",
              "      <td>205.956543</td>\n",
              "      <td>14.057994</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>681</th>\n",
              "      <td>32.075182</td>\n",
              "      <td>1.189818</td>\n",
              "      <td>205.954907</td>\n",
              "      <td>14.056224</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>682 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f5dd486c-9169-440b-82f5-f9abe77a16ff')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-convert:hover {\n",
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              "\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-f5dd486c-9169-440b-82f5-f9abe77a16ff 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-f5dd486c-9169-440b-82f5-f9abe77a16ff');\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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 198
        }
      ],
      "source": [
        "cv_results = xgb.cv(\n",
        "    params,\n",
        "    dcv,\n",
        "    num_boost_round=num_boost_round,\n",
        "    seed=42,\n",
        "    nfold=4,\n",
        "    metrics={'rmse'},\n",
        "    early_stopping_rounds=20\n",
        ")\n",
        "\n",
        "cv_results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AeRt-9UiynjN",
        "outputId": "d50ec6ad-7bfb-45bc-a11d-10a468308397"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "205.95490650000002"
            ]
          },
          "metadata": {},
          "execution_count": 199
        }
      ],
      "source": [
        "cv_results['test-rmse-mean'].min()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WAJmBNq9ypAZ"
      },
      "outputs": [],
      "source": [
        "gridsearch_params = [\n",
        "    (max_depth, min_child_weight)\n",
        "    for max_depth in range(7,12)\n",
        "    for min_child_weight in range(1,5)\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AJIa5GSSPHno",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5d6b4d2f-22d4-405f-e383-19c398f58210"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CV with max_depth=7, min_child_weight=1\n",
            "\trmse 202.0266324 for 663 rounds\n",
            "CV with max_depth=7, min_child_weight=2\n",
            "\trmse 203.0938384 for 441 rounds\n",
            "CV with max_depth=7, min_child_weight=3\n",
            "\trmse 202.8456544 for 530 rounds\n",
            "CV with max_depth=7, min_child_weight=4\n",
            "\trmse 204.2039796 for 649 rounds\n",
            "CV with max_depth=8, min_child_weight=1\n",
            "\trmse 203.65827940000003 for 492 rounds\n",
            "CV with max_depth=8, min_child_weight=2\n",
            "\trmse 201.77164620000002 for 501 rounds\n",
            "CV with max_depth=8, min_child_weight=3\n",
            "\trmse 202.37065139999999 for 586 rounds\n",
            "CV with max_depth=8, min_child_weight=4\n",
            "\trmse 205.07647699999998 for 446 rounds\n",
            "CV with max_depth=9, min_child_weight=1\n",
            "\trmse 204.665311 for 400 rounds\n",
            "CV with max_depth=9, min_child_weight=2\n",
            "\trmse 203.1260012 for 456 rounds\n",
            "CV with max_depth=9, min_child_weight=3\n",
            "\trmse 202.993515 for 493 rounds\n",
            "CV with max_depth=9, min_child_weight=4\n",
            "\trmse 205.02352000000002 for 411 rounds\n",
            "CV with max_depth=10, min_child_weight=1\n",
            "\trmse 204.8752014 for 349 rounds\n",
            "CV with max_depth=10, min_child_weight=2\n",
            "\trmse 202.26952219999998 for 337 rounds\n",
            "CV with max_depth=10, min_child_weight=3\n",
            "\trmse 203.252475 for 348 rounds\n",
            "CV with max_depth=10, min_child_weight=4\n",
            "\trmse 203.5194306 for 378 rounds\n",
            "CV with max_depth=11, min_child_weight=1\n",
            "\trmse 205.652893 for 305 rounds\n",
            "CV with max_depth=11, min_child_weight=2\n",
            "\trmse 204.951346 for 277 rounds\n",
            "CV with max_depth=11, min_child_weight=3\n",
            "\trmse 205.16015000000002 for 285 rounds\n",
            "CV with max_depth=11, min_child_weight=4\n",
            "\trmse 204.87404479999995 for 271 rounds\n",
            "Best params: 8, 2, rmse: 201.77164620000002\n"
          ]
        }
      ],
      "source": [
        "min_rmse = float(\"Inf\")\n",
        "best_params = None\n",
        "for max_depth, min_child_weight in gridsearch_params:\n",
        "    print(\"CV with max_depth={}, min_child_weight={}\".format(\n",
        "                             max_depth,\n",
        "                             min_child_weight))\n",
        "\n",
        "    params['max_depth'] = max_depth\n",
        "    params['min_child_weight'] = min_child_weight\n",
        "\n",
        "    cv_results = xgb.cv(\n",
        "        params,\n",
        "        dcv,\n",
        "        num_boost_round=num_boost_round,\n",
        "        seed=42,\n",
        "        nfold=5,\n",
        "        metrics={'rmse'},\n",
        "        early_stopping_rounds=10\n",
        "    )\n",
        "\n",
        "    mean_rmse = cv_results['test-rmse-mean'].min()\n",
        "    boost_rounds = cv_results['test-rmse-mean'].argmin()\n",
        "    print(\"\\trmse {} for {} rounds\".format(mean_rmse, boost_rounds))\n",
        "    if mean_rmse < min_rmse:\n",
        "        min_rmse = mean_rmse\n",
        "        best_params = (max_depth,min_child_weight)\n",
        "print(\"Best params: {}, {}, rmse: {}\".format(best_params[0], best_params[1], min_rmse))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "params['max_depth'] = best_params[0]\n",
        "params['min_child_weight'] = best_params[1]\n",
        "params"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0NL-SOrmOubY",
        "outputId": "084be263-c0d8-4afa-bb47-4a10e2dee90e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'colsample_bytree': 1,\n",
              " 'eta': 0.1,\n",
              " 'eval_metric': 'rmse',\n",
              " 'max_depth': 8,\n",
              " 'min_child_weight': 2,\n",
              " 'subsample': 1}"
            ]
          },
          "metadata": {},
          "execution_count": 202
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gridsearch_params = [\n",
        "    (subsample, colsample)\n",
        "    for subsample in [i/10. for i in range(5,11)]\n",
        "    for colsample in [i/10. for i in range(5,11)]\n",
        "]"
      ],
      "metadata": {
        "id": "Vy-BeBCpZMJc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "min_rmse = float(\"Inf\")\n",
        "best_params = None\n",
        "\n",
        "for subsample, colsample in reversed(gridsearch_params):\n",
        "    print(\"CV with subsample={}, colsample={}\".format(\n",
        "                             subsample,\n",
        "                             colsample))\n",
        "\n",
        "    params['subsample'] = subsample\n",
        "    params['colsample_bytree'] = colsample\n",
        "\n",
        "    cv_results = xgb.cv(\n",
        "        params,\n",
        "        dcv,\n",
        "        num_boost_round=num_boost_round,\n",
        "        seed=42,\n",
        "        nfold=5,\n",
        "        metrics={'rmse'},\n",
        "        early_stopping_rounds=10\n",
        "    )\n",
        "\n",
        "    mean_rmse = cv_results['test-rmse-mean'].min()\n",
        "    boost_rounds = cv_results['test-rmse-mean'].argmin()\n",
        "    print(\"\\trmse {} for {} rounds\".format(mean_rmse, boost_rounds))\n",
        "    if mean_rmse < min_rmse:\n",
        "        min_rmse = mean_rmse\n",
        "        best_params = (subsample,colsample)\n",
        "print(\"Best params: {}, {}, rmse: {}\".format(best_params[0], best_params[1], min_rmse))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b6ibVJMgZMqc",
        "outputId": "cf8027cc-4e0a-408b-d03a-95dce8fbb613"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CV with subsample=1.0, colsample=1.0\n",
            "\trmse 201.77164620000002 for 501 rounds\n",
            "CV with subsample=1.0, colsample=0.9\n",
            "\trmse 203.1915436 for 526 rounds\n",
            "CV with subsample=1.0, colsample=0.8\n",
            "\trmse 201.9258728 for 675 rounds\n",
            "CV with subsample=1.0, colsample=0.7\n",
            "\trmse 203.0518918 for 528 rounds\n",
            "CV with subsample=1.0, colsample=0.6\n",
            "\trmse 202.3562346 for 601 rounds\n",
            "CV with subsample=1.0, colsample=0.5\n",
            "\trmse 203.41009219999998 for 680 rounds\n",
            "CV with subsample=0.9, colsample=1.0\n",
            "\trmse 201.7560668 for 422 rounds\n",
            "CV with subsample=0.9, colsample=0.9\n",
            "\trmse 199.5444824 for 515 rounds\n",
            "CV with subsample=0.9, colsample=0.8\n",
            "\trmse 200.0997496 for 425 rounds\n",
            "CV with subsample=0.9, colsample=0.7\n",
            "\trmse 201.04949319999997 for 358 rounds\n",
            "CV with subsample=0.9, colsample=0.6\n",
            "\trmse 203.3647766 for 423 rounds\n",
            "CV with subsample=0.9, colsample=0.5\n",
            "\trmse 204.7879092 for 326 rounds\n",
            "CV with subsample=0.8, colsample=1.0\n",
            "\trmse 202.54666760000003 for 424 rounds\n",
            "CV with subsample=0.8, colsample=0.9\n",
            "\trmse 202.644928 for 489 rounds\n",
            "CV with subsample=0.8, colsample=0.8\n",
            "\trmse 203.7752412 for 327 rounds\n",
            "CV with subsample=0.8, colsample=0.7\n",
            "\trmse 201.173593 for 414 rounds\n",
            "CV with subsample=0.8, colsample=0.6\n",
            "\trmse 203.64012459999998 for 541 rounds\n",
            "CV with subsample=0.8, colsample=0.5\n",
            "\trmse 205.09068299999998 for 551 rounds\n",
            "CV with subsample=0.7, colsample=1.0\n",
            "\trmse 202.353891 for 412 rounds\n",
            "CV with subsample=0.7, colsample=0.9\n",
            "\trmse 202.0980348 for 381 rounds\n",
            "CV with subsample=0.7, colsample=0.8\n",
            "\trmse 202.6424194 for 390 rounds\n",
            "CV with subsample=0.7, colsample=0.7\n",
            "\trmse 202.1667236 for 326 rounds\n",
            "CV with subsample=0.7, colsample=0.6\n",
            "\trmse 205.07116379999997 for 327 rounds\n",
            "CV with subsample=0.7, colsample=0.5\n",
            "\trmse 206.77969359999997 for 441 rounds\n",
            "CV with subsample=0.6, colsample=1.0\n",
            "\trmse 205.083795 for 390 rounds\n",
            "CV with subsample=0.6, colsample=0.9\n",
            "\trmse 204.421515 for 405 rounds\n",
            "CV with subsample=0.6, colsample=0.8\n",
            "\trmse 201.6495974 for 453 rounds\n",
            "CV with subsample=0.6, colsample=0.7\n",
            "\trmse 203.47010500000002 for 356 rounds\n",
            "CV with subsample=0.6, colsample=0.6\n",
            "\trmse 202.7260346 for 480 rounds\n",
            "CV with subsample=0.6, colsample=0.5\n",
            "\trmse 206.93576939999997 for 482 rounds\n",
            "CV with subsample=0.5, colsample=1.0\n",
            "\trmse 205.658493 for 402 rounds\n",
            "CV with subsample=0.5, colsample=0.9\n",
            "\trmse 206.2918826 for 344 rounds\n",
            "CV with subsample=0.5, colsample=0.8\n",
            "\trmse 204.65205379999998 for 406 rounds\n",
            "CV with subsample=0.5, colsample=0.7\n",
            "\trmse 206.12210399999998 for 402 rounds\n",
            "CV with subsample=0.5, colsample=0.6\n",
            "\trmse 207.4977508 for 405 rounds\n",
            "CV with subsample=0.5, colsample=0.5\n",
            "\trmse 213.2180084 for 253 rounds\n",
            "Best params: 0.9, 0.9, rmse: 199.5444824\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "params['subsample'] = best_params[0]\n",
        "params['colsample_bytree'] = best_params[1]\n",
        "params"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uvjrZLHuZX3_",
        "outputId": "b7788397-f442-4e3a-976b-5488ebe06f2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'colsample_bytree': 0.9,\n",
              " 'eta': 0.1,\n",
              " 'eval_metric': 'rmse',\n",
              " 'max_depth': 8,\n",
              " 'min_child_weight': 2,\n",
              " 'subsample': 0.9}"
            ]
          },
          "metadata": {},
          "execution_count": 205
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%time\n",
        "\n",
        "min_rmse = float(\"Inf\")\n",
        "best_params = None\n",
        "for eta in [.3, .1, .03, .01]:\n",
        "    print(\"CV with eta={}\".format(eta))\n",
        "\n",
        "    params['eta'] = eta\n",
        "\n",
        "    %time cv_results = xgb.cv(params, dcv, num_boost_round=num_boost_round, seed=42, nfold=4, metrics=['rmse'], early_stopping_rounds=10)\n",
        "\n",
        "    mean_rmse = cv_results['test-rmse-mean'].min()\n",
        "    boost_rounds = cv_results['test-rmse-mean'].argmin()\n",
        "    print(\"\\trmse {} for {} rounds\\n\".format(mean_rmse, boost_rounds))\n",
        "    if mean_rmse < min_rmse:\n",
        "        min_rmse = mean_rmse\n",
        "        best_params = eta\n",
        "print(\"Best params: {}, rmse: {}\".format(best_params, min_rmse))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sZciAr1ROlx0",
        "outputId": "759f087d-1059-456f-b7e2-03285285779b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 4 µs, sys: 0 ns, total: 4 µs\n",
            "Wall time: 9.3 µs\n",
            "CV with eta=0.3\n",
            "CPU times: user 25.6 s, sys: 93.2 ms, total: 25.7 s\n",
            "Wall time: 13.1 s\n",
            "\trmse 214.0426445 for 163 rounds\n",
            "\n",
            "CV with eta=0.1\n",
            "CPU times: user 1min 1s, sys: 240 ms, total: 1min 1s\n",
            "Wall time: 31.7 s\n",
            "\trmse 206.798855 for 409 rounds\n",
            "\n",
            "CV with eta=0.03\n",
            "CPU times: user 2min 25s, sys: 521 ms, total: 2min 26s\n",
            "Wall time: 1min 14s\n",
            "\trmse 203.81531900000002 for 998 rounds\n",
            "\n",
            "CV with eta=0.01\n",
            "CPU times: user 2min 28s, sys: 491 ms, total: 2min 28s\n",
            "Wall time: 1min 16s\n",
            "\trmse 211.92805099999998 for 999 rounds\n",
            "\n",
            "Best params: 0.03, rmse: 203.81531900000002\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "params['eta'] = best_params\n",
        "params"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vjwwdgu1O00u",
        "outputId": "f22e1e26-e540-4a89-b319-319003896aeb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'colsample_bytree': 0.9,\n",
              " 'eta': 0.03,\n",
              " 'eval_metric': 'rmse',\n",
              " 'max_depth': 8,\n",
              " 'min_child_weight': 2,\n",
              " 'subsample': 0.9}"
            ]
          },
          "metadata": {},
          "execution_count": 207
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_boost_round = 2500\n",
        "\n",
        "model = xgb.train(\n",
        "    params,\n",
        "    dtrain,\n",
        "    num_boost_round=num_boost_round,\n",
        "    evals=[(dval, \"Val\")],\n",
        "    early_stopping_rounds=20\n",
        ")\n",
        "\n",
        "print(\"Best rmse: {:.2f} with {} rounds\".format(\n",
        "                 model.best_score,\n",
        "                 model.best_iteration+1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xh19sVI6UtwE",
        "outputId": "2af99b08-68b4-46e4-d3cb-2021cf308288"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\tVal-rmse:923.872\n",
            "Will train until Val-rmse hasn't improved in 20 rounds.\n",
            "[1]\tVal-rmse:899.018\n",
            "[2]\tVal-rmse:874.932\n",
            "[3]\tVal-rmse:851.673\n",
            "[4]\tVal-rmse:829.203\n",
            "[5]\tVal-rmse:807.241\n",
            "[6]\tVal-rmse:785.925\n",
            "[7]\tVal-rmse:765.418\n",
            "[8]\tVal-rmse:745.467\n",
            "[9]\tVal-rmse:726.056\n",
            "[10]\tVal-rmse:707.361\n",
            "[11]\tVal-rmse:689.421\n",
            "[12]\tVal-rmse:672.043\n",
            "[13]\tVal-rmse:659.135\n",
            "[14]\tVal-rmse:642.881\n",
            "[15]\tVal-rmse:626.962\n",
            "[16]\tVal-rmse:611.716\n",
            "[17]\tVal-rmse:597.045\n",
            "[18]\tVal-rmse:582.651\n",
            "[19]\tVal-rmse:568.849\n",
            "[20]\tVal-rmse:555.469\n",
            "[21]\tVal-rmse:542.446\n",
            "[22]\tVal-rmse:530.038\n",
            "[23]\tVal-rmse:518.063\n",
            "[24]\tVal-rmse:506.56\n",
            "[25]\tVal-rmse:495.216\n",
            "[26]\tVal-rmse:484.392\n",
            "[27]\tVal-rmse:473.925\n",
            "[28]\tVal-rmse:465.895\n",
            "[29]\tVal-rmse:456.053\n",
            "[30]\tVal-rmse:446.491\n",
            "[31]\tVal-rmse:439.418\n",
            "[32]\tVal-rmse:430.557\n",
            "[33]\tVal-rmse:422.015\n",
            "[34]\tVal-rmse:413.599\n",
            "[35]\tVal-rmse:405.652\n",
            "[36]\tVal-rmse:397.826\n",
            "[37]\tVal-rmse:392.156\n",
            "[38]\tVal-rmse:385.323\n",
            "[39]\tVal-rmse:378.523\n",
            "[40]\tVal-rmse:373.488\n",
            "[41]\tVal-rmse:367.195\n",
            "[42]\tVal-rmse:360.827\n",
            "[43]\tVal-rmse:354.812\n",
            "[44]\tVal-rmse:348.947\n",
            "[45]\tVal-rmse:343.628\n",
            "[46]\tVal-rmse:338.621\n",
            "[47]\tVal-rmse:333.412\n",
            "[48]\tVal-rmse:328.687\n",
            "[49]\tVal-rmse:324.14\n",
            "[50]\tVal-rmse:320.755\n",
            "[51]\tVal-rmse:316.152\n",
            "[52]\tVal-rmse:311.99\n",
            "[53]\tVal-rmse:307.891\n",
            "[54]\tVal-rmse:304.001\n",
            "[55]\tVal-rmse:300.411\n",
            "[56]\tVal-rmse:297.043\n",
            "[57]\tVal-rmse:293.78\n",
            "[58]\tVal-rmse:290.903\n",
            "[59]\tVal-rmse:287.787\n",
            "[60]\tVal-rmse:284.681\n",
            "[61]\tVal-rmse:281.724\n",
            "[62]\tVal-rmse:279.043\n",
            "[63]\tVal-rmse:276.278\n",
            "[64]\tVal-rmse:274\n",
            "[65]\tVal-rmse:272.219\n",
            "[66]\tVal-rmse:269.879\n",
            "[67]\tVal-rmse:267.594\n",
            "[68]\tVal-rmse:265.58\n",
            "[69]\tVal-rmse:263.659\n",
            "[70]\tVal-rmse:261.847\n",
            "[71]\tVal-rmse:260.338\n",
            "[72]\tVal-rmse:258.583\n",
            "[73]\tVal-rmse:256.966\n",
            "[74]\tVal-rmse:255.232\n",
            "[75]\tVal-rmse:253.541\n",
            "[76]\tVal-rmse:252.448\n",
            "[77]\tVal-rmse:250.904\n",
            "[78]\tVal-rmse:249.851\n",
            "[79]\tVal-rmse:248.636\n",
            "[80]\tVal-rmse:247.41\n",
            "[81]\tVal-rmse:246.143\n",
            "[82]\tVal-rmse:244.869\n",
            "[83]\tVal-rmse:243.528\n",
            "[84]\tVal-rmse:242.382\n",
            "[85]\tVal-rmse:241.365\n",
            "[86]\tVal-rmse:240.383\n",
            "[87]\tVal-rmse:239.655\n",
            "[88]\tVal-rmse:238.718\n",
            "[89]\tVal-rmse:238.006\n",
            "[90]\tVal-rmse:237.191\n",
            "[91]\tVal-rmse:236.556\n",
            "[92]\tVal-rmse:235.861\n",
            "[93]\tVal-rmse:235.168\n",
            "[94]\tVal-rmse:234.416\n",
            "[95]\tVal-rmse:233.751\n",
            "[96]\tVal-rmse:232.977\n",
            "[97]\tVal-rmse:232.291\n",
            "[98]\tVal-rmse:231.754\n",
            "[99]\tVal-rmse:231.282\n",
            "[100]\tVal-rmse:230.637\n",
            "[101]\tVal-rmse:230.28\n",
            "[102]\tVal-rmse:229.634\n",
            "[103]\tVal-rmse:229.257\n",
            "[104]\tVal-rmse:228.795\n",
            "[105]\tVal-rmse:228.413\n",
            "[106]\tVal-rmse:227.925\n",
            "[107]\tVal-rmse:227.559\n",
            "[108]\tVal-rmse:227.094\n",
            "[109]\tVal-rmse:226.781\n",
            "[110]\tVal-rmse:226.513\n",
            "[111]\tVal-rmse:226.05\n",
            "[112]\tVal-rmse:225.767\n",
            "[113]\tVal-rmse:225.444\n",
            "[114]\tVal-rmse:225.158\n",
            "[115]\tVal-rmse:224.831\n",
            "[116]\tVal-rmse:224.587\n",
            "[117]\tVal-rmse:224.236\n",
            "[118]\tVal-rmse:223.962\n",
            "[119]\tVal-rmse:223.766\n",
            "[120]\tVal-rmse:223.635\n",
            "[121]\tVal-rmse:223.27\n",
            "[122]\tVal-rmse:223.047\n",
            "[123]\tVal-rmse:222.716\n",
            "[124]\tVal-rmse:222.522\n",
            "[125]\tVal-rmse:222.204\n",
            "[126]\tVal-rmse:221.91\n",
            "[127]\tVal-rmse:221.709\n",
            "[128]\tVal-rmse:221.563\n",
            "[129]\tVal-rmse:221.318\n",
            "[130]\tVal-rmse:221.075\n",
            "[131]\tVal-rmse:220.869\n",
            "[132]\tVal-rmse:220.664\n",
            "[133]\tVal-rmse:220.584\n",
            "[134]\tVal-rmse:220.413\n",
            "[135]\tVal-rmse:220.1\n",
            "[136]\tVal-rmse:219.932\n",
            "[137]\tVal-rmse:219.809\n",
            "[138]\tVal-rmse:219.552\n",
            "[139]\tVal-rmse:219.34\n",
            "[140]\tVal-rmse:219.155\n",
            "[141]\tVal-rmse:219.03\n",
            "[142]\tVal-rmse:218.776\n",
            "[143]\tVal-rmse:218.631\n",
            "[144]\tVal-rmse:218.38\n",
            "[145]\tVal-rmse:218.204\n",
            "[146]\tVal-rmse:218.075\n",
            "[147]\tVal-rmse:218.01\n",
            "[148]\tVal-rmse:217.916\n",
            "[149]\tVal-rmse:217.786\n",
            "[150]\tVal-rmse:217.662\n",
            "[151]\tVal-rmse:217.573\n",
            "[152]\tVal-rmse:217.439\n",
            "[153]\tVal-rmse:217.389\n",
            "[154]\tVal-rmse:217.296\n",
            "[155]\tVal-rmse:217.261\n",
            "[156]\tVal-rmse:217.15\n",
            "[157]\tVal-rmse:217.011\n",
            "[158]\tVal-rmse:216.902\n",
            "[159]\tVal-rmse:216.792\n",
            "[160]\tVal-rmse:216.758\n",
            "[161]\tVal-rmse:216.648\n",
            "[162]\tVal-rmse:216.488\n",
            "[163]\tVal-rmse:216.426\n",
            "[164]\tVal-rmse:216.367\n",
            "[165]\tVal-rmse:216.257\n",
            "[166]\tVal-rmse:216.172\n",
            "[167]\tVal-rmse:216.114\n",
            "[168]\tVal-rmse:215.986\n",
            "[169]\tVal-rmse:215.934\n",
            "[170]\tVal-rmse:215.851\n",
            "[171]\tVal-rmse:215.805\n",
            "[172]\tVal-rmse:215.663\n",
            "[173]\tVal-rmse:215.628\n",
            "[174]\tVal-rmse:215.55\n",
            "[175]\tVal-rmse:215.475\n",
            "[176]\tVal-rmse:215.416\n",
            "[177]\tVal-rmse:215.353\n",
            "[178]\tVal-rmse:215.175\n",
            "[179]\tVal-rmse:215.096\n",
            "[180]\tVal-rmse:215.013\n",
            "[181]\tVal-rmse:214.884\n",
            "[182]\tVal-rmse:214.856\n",
            "[183]\tVal-rmse:214.744\n",
            "[184]\tVal-rmse:214.654\n",
            "[185]\tVal-rmse:214.547\n",
            "[186]\tVal-rmse:214.5\n",
            "[187]\tVal-rmse:214.42\n",
            "[188]\tVal-rmse:214.415\n",
            "[189]\tVal-rmse:214.261\n",
            "[190]\tVal-rmse:214.131\n",
            "[191]\tVal-rmse:214.027\n",
            "[192]\tVal-rmse:213.984\n",
            "[193]\tVal-rmse:213.848\n",
            "[194]\tVal-rmse:213.766\n",
            "[195]\tVal-rmse:213.68\n",
            "[196]\tVal-rmse:213.678\n",
            "[197]\tVal-rmse:213.573\n",
            "[198]\tVal-rmse:213.537\n",
            "[199]\tVal-rmse:213.455\n",
            "[200]\tVal-rmse:213.451\n",
            "[201]\tVal-rmse:213.36\n",
            "[202]\tVal-rmse:213.277\n",
            "[203]\tVal-rmse:213.198\n",
            "[204]\tVal-rmse:213.067\n",
            "[205]\tVal-rmse:213.026\n",
            "[206]\tVal-rmse:212.887\n",
            "[207]\tVal-rmse:212.844\n",
            "[208]\tVal-rmse:212.676\n",
            "[209]\tVal-rmse:212.646\n",
            "[210]\tVal-rmse:212.574\n",
            "[211]\tVal-rmse:212.541\n",
            "[212]\tVal-rmse:212.52\n",
            "[213]\tVal-rmse:212.472\n",
            "[214]\tVal-rmse:212.45\n",
            "[215]\tVal-rmse:212.446\n",
            "[216]\tVal-rmse:212.453\n",
            "[217]\tVal-rmse:212.395\n",
            "[218]\tVal-rmse:212.324\n",
            "[219]\tVal-rmse:212.306\n",
            "[220]\tVal-rmse:212.275\n",
            "[221]\tVal-rmse:212.276\n",
            "[222]\tVal-rmse:212.239\n",
            "[223]\tVal-rmse:212.185\n",
            "[224]\tVal-rmse:212.184\n",
            "[225]\tVal-rmse:212.185\n",
            "[226]\tVal-rmse:212.18\n",
            "[227]\tVal-rmse:212.13\n",
            "[228]\tVal-rmse:212.162\n",
            "[229]\tVal-rmse:212.141\n",
            "[230]\tVal-rmse:212.136\n",
            "[231]\tVal-rmse:212.15\n",
            "[232]\tVal-rmse:212.019\n",
            "[233]\tVal-rmse:212.019\n",
            "[234]\tVal-rmse:211.962\n",
            "[235]\tVal-rmse:211.909\n",
            "[236]\tVal-rmse:211.922\n",
            "[237]\tVal-rmse:211.9\n",
            "[238]\tVal-rmse:211.788\n",
            "[239]\tVal-rmse:211.794\n",
            "[240]\tVal-rmse:211.735\n",
            "[241]\tVal-rmse:211.705\n",
            "[242]\tVal-rmse:211.687\n",
            "[243]\tVal-rmse:211.699\n",
            "[244]\tVal-rmse:211.663\n",
            "[245]\tVal-rmse:211.642\n",
            "[246]\tVal-rmse:211.557\n",
            "[247]\tVal-rmse:211.542\n",
            "[248]\tVal-rmse:211.512\n",
            "[249]\tVal-rmse:211.516\n",
            "[250]\tVal-rmse:211.465\n",
            "[251]\tVal-rmse:211.4\n",
            "[252]\tVal-rmse:211.344\n",
            "[253]\tVal-rmse:211.29\n",
            "[254]\tVal-rmse:211.294\n",
            "[255]\tVal-rmse:211.264\n",
            "[256]\tVal-rmse:211.222\n",
            "[257]\tVal-rmse:211.213\n",
            "[258]\tVal-rmse:211.14\n",
            "[259]\tVal-rmse:211.086\n",
            "[260]\tVal-rmse:211.028\n",
            "[261]\tVal-rmse:210.971\n",
            "[262]\tVal-rmse:210.877\n",
            "[263]\tVal-rmse:210.776\n",
            "[264]\tVal-rmse:210.744\n",
            "[265]\tVal-rmse:210.779\n",
            "[266]\tVal-rmse:210.775\n",
            "[267]\tVal-rmse:210.759\n",
            "[268]\tVal-rmse:210.743\n",
            "[269]\tVal-rmse:210.703\n",
            "[270]\tVal-rmse:210.665\n",
            "[271]\tVal-rmse:210.623\n",
            "[272]\tVal-rmse:210.614\n",
            "[273]\tVal-rmse:210.587\n",
            "[274]\tVal-rmse:210.546\n",
            "[275]\tVal-rmse:210.401\n",
            "[276]\tVal-rmse:210.408\n",
            "[277]\tVal-rmse:210.372\n",
            "[278]\tVal-rmse:210.295\n",
            "[279]\tVal-rmse:210.325\n",
            "[280]\tVal-rmse:210.276\n",
            "[281]\tVal-rmse:210.236\n",
            "[282]\tVal-rmse:210.203\n",
            "[283]\tVal-rmse:210.2\n",
            "[284]\tVal-rmse:210.145\n",
            "[285]\tVal-rmse:210.13\n",
            "[286]\tVal-rmse:210.111\n",
            "[287]\tVal-rmse:210.073\n",
            "[288]\tVal-rmse:210.024\n",
            "[289]\tVal-rmse:209.933\n",
            "[290]\tVal-rmse:209.849\n",
            "[291]\tVal-rmse:209.815\n",
            "[292]\tVal-rmse:209.724\n",
            "[293]\tVal-rmse:209.682\n",
            "[294]\tVal-rmse:209.645\n",
            "[295]\tVal-rmse:209.584\n",
            "[296]\tVal-rmse:209.554\n",
            "[297]\tVal-rmse:209.494\n",
            "[298]\tVal-rmse:209.46\n",
            "[299]\tVal-rmse:209.378\n",
            "[300]\tVal-rmse:209.362\n",
            "[301]\tVal-rmse:209.353\n",
            "[302]\tVal-rmse:209.305\n",
            "[303]\tVal-rmse:209.32\n",
            "[304]\tVal-rmse:209.289\n",
            "[305]\tVal-rmse:209.195\n",
            "[306]\tVal-rmse:209.243\n",
            "[307]\tVal-rmse:209.182\n",
            "[308]\tVal-rmse:209.189\n",
            "[309]\tVal-rmse:209.171\n",
            "[310]\tVal-rmse:209.175\n",
            "[311]\tVal-rmse:209.111\n",
            "[312]\tVal-rmse:209.127\n",
            "[313]\tVal-rmse:209.117\n",
            "[314]\tVal-rmse:209.13\n",
            "[315]\tVal-rmse:209.113\n",
            "[316]\tVal-rmse:209.056\n",
            "[317]\tVal-rmse:209.022\n",
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            "[763]\tVal-rmse:200.427\n",
            "[764]\tVal-rmse:200.408\n",
            "[765]\tVal-rmse:200.403\n",
            "[766]\tVal-rmse:200.38\n",
            "[767]\tVal-rmse:200.373\n",
            "[768]\tVal-rmse:200.379\n",
            "[769]\tVal-rmse:200.371\n",
            "[770]\tVal-rmse:200.352\n",
            "[771]\tVal-rmse:200.343\n",
            "[772]\tVal-rmse:200.317\n",
            "[773]\tVal-rmse:200.317\n",
            "[774]\tVal-rmse:200.315\n",
            "[775]\tVal-rmse:200.28\n",
            "[776]\tVal-rmse:200.256\n",
            "[777]\tVal-rmse:200.258\n",
            "[778]\tVal-rmse:200.229\n",
            "[779]\tVal-rmse:200.222\n",
            "[780]\tVal-rmse:200.232\n",
            "[781]\tVal-rmse:200.21\n",
            "[782]\tVal-rmse:200.221\n",
            "[783]\tVal-rmse:200.215\n",
            "[784]\tVal-rmse:200.205\n",
            "[785]\tVal-rmse:200.187\n",
            "[786]\tVal-rmse:200.207\n",
            "[787]\tVal-rmse:200.203\n",
            "[788]\tVal-rmse:200.183\n",
            "[789]\tVal-rmse:200.171\n",
            "[790]\tVal-rmse:200.171\n",
            "[791]\tVal-rmse:200.173\n",
            "[792]\tVal-rmse:200.165\n",
            "[793]\tVal-rmse:200.145\n",
            "[794]\tVal-rmse:200.167\n",
            "[795]\tVal-rmse:200.169\n",
            "[796]\tVal-rmse:200.144\n",
            "[797]\tVal-rmse:200.127\n",
            "[798]\tVal-rmse:200.113\n",
            "[799]\tVal-rmse:200.102\n",
            "[800]\tVal-rmse:200.098\n",
            "[801]\tVal-rmse:200.087\n",
            "[802]\tVal-rmse:200.07\n",
            "[803]\tVal-rmse:200.064\n",
            "[804]\tVal-rmse:200.045\n",
            "[805]\tVal-rmse:200.031\n",
            "[806]\tVal-rmse:200.023\n",
            "[807]\tVal-rmse:200.035\n",
            "[808]\tVal-rmse:200.009\n",
            "[809]\tVal-rmse:199.98\n",
            "[810]\tVal-rmse:199.961\n",
            "[811]\tVal-rmse:199.971\n",
            "[812]\tVal-rmse:199.96\n",
            "[813]\tVal-rmse:199.98\n",
            "[814]\tVal-rmse:199.951\n",
            "[815]\tVal-rmse:199.95\n",
            "[816]\tVal-rmse:199.952\n",
            "[817]\tVal-rmse:199.937\n",
            "[818]\tVal-rmse:199.93\n",
            "[819]\tVal-rmse:199.912\n",
            "[820]\tVal-rmse:199.903\n",
            "[821]\tVal-rmse:199.895\n",
            "[822]\tVal-rmse:199.872\n",
            "[823]\tVal-rmse:199.85\n",
            "[824]\tVal-rmse:199.836\n",
            "[825]\tVal-rmse:199.807\n",
            "[826]\tVal-rmse:199.834\n",
            "[827]\tVal-rmse:199.848\n",
            "[828]\tVal-rmse:199.835\n",
            "[829]\tVal-rmse:199.811\n",
            "[830]\tVal-rmse:199.769\n",
            "[831]\tVal-rmse:199.746\n",
            "[832]\tVal-rmse:199.736\n",
            "[833]\tVal-rmse:199.72\n",
            "[834]\tVal-rmse:199.7\n",
            "[835]\tVal-rmse:199.699\n",
            "[836]\tVal-rmse:199.694\n",
            "[837]\tVal-rmse:199.679\n",
            "[838]\tVal-rmse:199.672\n",
            "[839]\tVal-rmse:199.665\n",
            "[840]\tVal-rmse:199.642\n",
            "[841]\tVal-rmse:199.646\n",
            "[842]\tVal-rmse:199.641\n",
            "[843]\tVal-rmse:199.61\n",
            "[844]\tVal-rmse:199.618\n",
            "[845]\tVal-rmse:199.621\n",
            "[846]\tVal-rmse:199.621\n",
            "[847]\tVal-rmse:199.607\n",
            "[848]\tVal-rmse:199.584\n",
            "[849]\tVal-rmse:199.579\n",
            "[850]\tVal-rmse:199.564\n",
            "[851]\tVal-rmse:199.556\n",
            "[852]\tVal-rmse:199.522\n",
            "[853]\tVal-rmse:199.519\n",
            "[854]\tVal-rmse:199.514\n",
            "[855]\tVal-rmse:199.505\n",
            "[856]\tVal-rmse:199.474\n",
            "[857]\tVal-rmse:199.472\n",
            "[858]\tVal-rmse:199.462\n",
            "[859]\tVal-rmse:199.456\n",
            "[860]\tVal-rmse:199.44\n",
            "[861]\tVal-rmse:199.462\n",
            "[862]\tVal-rmse:199.454\n",
            "[863]\tVal-rmse:199.476\n",
            "[864]\tVal-rmse:199.436\n",
            "[865]\tVal-rmse:199.432\n",
            "[866]\tVal-rmse:199.418\n",
            "[867]\tVal-rmse:199.383\n",
            "[868]\tVal-rmse:199.373\n",
            "[869]\tVal-rmse:199.386\n",
            "[870]\tVal-rmse:199.391\n",
            "[871]\tVal-rmse:199.364\n",
            "[872]\tVal-rmse:199.353\n",
            "[873]\tVal-rmse:199.35\n",
            "[874]\tVal-rmse:199.339\n",
            "[875]\tVal-rmse:199.33\n",
            "[876]\tVal-rmse:199.323\n",
            "[877]\tVal-rmse:199.297\n",
            "[878]\tVal-rmse:199.295\n",
            "[879]\tVal-rmse:199.26\n",
            "[880]\tVal-rmse:199.248\n",
            "[881]\tVal-rmse:199.234\n",
            "[882]\tVal-rmse:199.219\n",
            "[883]\tVal-rmse:199.211\n",
            "[884]\tVal-rmse:199.166\n",
            "[885]\tVal-rmse:199.177\n",
            "[886]\tVal-rmse:199.17\n",
            "[887]\tVal-rmse:199.157\n",
            "[888]\tVal-rmse:199.181\n",
            "[889]\tVal-rmse:199.191\n",
            "[890]\tVal-rmse:199.197\n",
            "[891]\tVal-rmse:199.203\n",
            "[892]\tVal-rmse:199.19\n",
            "[893]\tVal-rmse:199.2\n",
            "[894]\tVal-rmse:199.205\n",
            "[895]\tVal-rmse:199.195\n",
            "[896]\tVal-rmse:199.21\n",
            "[897]\tVal-rmse:199.209\n",
            "[898]\tVal-rmse:199.203\n",
            "[899]\tVal-rmse:199.189\n",
            "[900]\tVal-rmse:199.194\n",
            "[901]\tVal-rmse:199.167\n",
            "[902]\tVal-rmse:199.14\n",
            "[903]\tVal-rmse:199.14\n",
            "[904]\tVal-rmse:199.124\n",
            "[905]\tVal-rmse:199.1\n",
            "[906]\tVal-rmse:199.078\n",
            "[907]\tVal-rmse:199.074\n",
            "[908]\tVal-rmse:199.048\n",
            "[909]\tVal-rmse:199.046\n",
            "[910]\tVal-rmse:199.035\n",
            "[911]\tVal-rmse:199.032\n",
            "[912]\tVal-rmse:199.028\n",
            "[913]\tVal-rmse:199.018\n",
            "[914]\tVal-rmse:199.019\n",
            "[915]\tVal-rmse:199.017\n",
            "[916]\tVal-rmse:199.031\n",
            "[917]\tVal-rmse:199.006\n",
            "[918]\tVal-rmse:198.985\n",
            "[919]\tVal-rmse:198.952\n",
            "[920]\tVal-rmse:198.953\n",
            "[921]\tVal-rmse:198.971\n",
            "[922]\tVal-rmse:198.961\n",
            "[923]\tVal-rmse:198.96\n",
            "[924]\tVal-rmse:198.947\n",
            "[925]\tVal-rmse:198.95\n",
            "[926]\tVal-rmse:198.94\n",
            "[927]\tVal-rmse:198.94\n",
            "[928]\tVal-rmse:198.947\n",
            "[929]\tVal-rmse:198.941\n",
            "[930]\tVal-rmse:198.936\n",
            "[931]\tVal-rmse:198.927\n",
            "[932]\tVal-rmse:198.926\n",
            "[933]\tVal-rmse:198.933\n",
            "[934]\tVal-rmse:198.921\n",
            "[935]\tVal-rmse:198.91\n",
            "[936]\tVal-rmse:198.906\n",
            "[937]\tVal-rmse:198.895\n",
            "[938]\tVal-rmse:198.876\n",
            "[939]\tVal-rmse:198.881\n",
            "[940]\tVal-rmse:198.89\n",
            "[941]\tVal-rmse:198.876\n",
            "[942]\tVal-rmse:198.87\n",
            "[943]\tVal-rmse:198.878\n",
            "[944]\tVal-rmse:198.88\n",
            "[945]\tVal-rmse:198.875\n",
            "[946]\tVal-rmse:198.869\n",
            "[947]\tVal-rmse:198.878\n",
            "[948]\tVal-rmse:198.844\n",
            "[949]\tVal-rmse:198.847\n",
            "[950]\tVal-rmse:198.841\n",
            "[951]\tVal-rmse:198.838\n",
            "[952]\tVal-rmse:198.815\n",
            "[953]\tVal-rmse:198.813\n",
            "[954]\tVal-rmse:198.785\n",
            "[955]\tVal-rmse:198.775\n",
            "[956]\tVal-rmse:198.748\n",
            "[957]\tVal-rmse:198.766\n",
            "[958]\tVal-rmse:198.769\n",
            "[959]\tVal-rmse:198.777\n",
            "[960]\tVal-rmse:198.77\n",
            "[961]\tVal-rmse:198.768\n",
            "[962]\tVal-rmse:198.78\n",
            "[963]\tVal-rmse:198.787\n",
            "[964]\tVal-rmse:198.784\n",
            "[965]\tVal-rmse:198.803\n",
            "[966]\tVal-rmse:198.794\n",
            "[967]\tVal-rmse:198.776\n",
            "[968]\tVal-rmse:198.769\n",
            "[969]\tVal-rmse:198.757\n",
            "[970]\tVal-rmse:198.746\n",
            "[971]\tVal-rmse:198.758\n",
            "[972]\tVal-rmse:198.752\n",
            "[973]\tVal-rmse:198.754\n",
            "[974]\tVal-rmse:198.75\n",
            "[975]\tVal-rmse:198.737\n",
            "[976]\tVal-rmse:198.734\n",
            "[977]\tVal-rmse:198.723\n",
            "[978]\tVal-rmse:198.713\n",
            "[979]\tVal-rmse:198.706\n",
            "[980]\tVal-rmse:198.704\n",
            "[981]\tVal-rmse:198.714\n",
            "[982]\tVal-rmse:198.723\n",
            "[983]\tVal-rmse:198.725\n",
            "[984]\tVal-rmse:198.704\n",
            "[985]\tVal-rmse:198.68\n",
            "[986]\tVal-rmse:198.69\n",
            "[987]\tVal-rmse:198.697\n",
            "[988]\tVal-rmse:198.704\n",
            "[989]\tVal-rmse:198.709\n",
            "[990]\tVal-rmse:198.711\n",
            "[991]\tVal-rmse:198.703\n",
            "[992]\tVal-rmse:198.701\n",
            "[993]\tVal-rmse:198.697\n",
            "[994]\tVal-rmse:198.7\n",
            "[995]\tVal-rmse:198.718\n",
            "[996]\tVal-rmse:198.714\n",
            "[997]\tVal-rmse:198.706\n",
            "[998]\tVal-rmse:198.692\n",
            "[999]\tVal-rmse:198.682\n",
            "[1000]\tVal-rmse:198.652\n",
            "[1001]\tVal-rmse:198.652\n",
            "[1002]\tVal-rmse:198.667\n",
            "[1003]\tVal-rmse:198.681\n",
            "[1004]\tVal-rmse:198.679\n",
            "[1005]\tVal-rmse:198.67\n",
            "[1006]\tVal-rmse:198.668\n",
            "[1007]\tVal-rmse:198.667\n",
            "[1008]\tVal-rmse:198.659\n",
            "[1009]\tVal-rmse:198.655\n",
            "[1010]\tVal-rmse:198.642\n",
            "[1011]\tVal-rmse:198.626\n",
            "[1012]\tVal-rmse:198.602\n",
            "[1013]\tVal-rmse:198.58\n",
            "[1014]\tVal-rmse:198.574\n",
            "[1015]\tVal-rmse:198.567\n",
            "[1016]\tVal-rmse:198.556\n",
            "[1017]\tVal-rmse:198.549\n",
            "[1018]\tVal-rmse:198.546\n",
            "[1019]\tVal-rmse:198.544\n",
            "[1020]\tVal-rmse:198.546\n",
            "[1021]\tVal-rmse:198.539\n",
            "[1022]\tVal-rmse:198.534\n",
            "[1023]\tVal-rmse:198.523\n",
            "[1024]\tVal-rmse:198.55\n",
            "[1025]\tVal-rmse:198.52\n",
            "[1026]\tVal-rmse:198.532\n",
            "[1027]\tVal-rmse:198.511\n",
            "[1028]\tVal-rmse:198.514\n",
            "[1029]\tVal-rmse:198.508\n",
            "[1030]\tVal-rmse:198.503\n",
            "[1031]\tVal-rmse:198.5\n",
            "[1032]\tVal-rmse:198.492\n",
            "[1033]\tVal-rmse:198.483\n",
            "[1034]\tVal-rmse:198.477\n",
            "[1035]\tVal-rmse:198.472\n",
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            "[1037]\tVal-rmse:198.468\n",
            "[1038]\tVal-rmse:198.46\n",
            "[1039]\tVal-rmse:198.458\n",
            "[1040]\tVal-rmse:198.453\n",
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            "[1043]\tVal-rmse:198.453\n",
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            "[1053]\tVal-rmse:198.431\n",
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            "[1057]\tVal-rmse:198.387\n",
            "[1058]\tVal-rmse:198.386\n",
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            "[1064]\tVal-rmse:198.343\n",
            "[1065]\tVal-rmse:198.337\n",
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            "[1069]\tVal-rmse:198.314\n",
            "[1070]\tVal-rmse:198.317\n",
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            "[1073]\tVal-rmse:198.318\n",
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            "[1078]\tVal-rmse:198.328\n",
            "[1079]\tVal-rmse:198.32\n",
            "[1080]\tVal-rmse:198.318\n",
            "[1081]\tVal-rmse:198.295\n",
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            "[1084]\tVal-rmse:198.282\n",
            "[1085]\tVal-rmse:198.269\n",
            "[1086]\tVal-rmse:198.268\n",
            "[1087]\tVal-rmse:198.266\n",
            "[1088]\tVal-rmse:198.268\n",
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            "[1090]\tVal-rmse:198.276\n",
            "[1091]\tVal-rmse:198.282\n",
            "[1092]\tVal-rmse:198.275\n",
            "[1093]\tVal-rmse:198.275\n",
            "[1094]\tVal-rmse:198.279\n",
            "[1095]\tVal-rmse:198.26\n",
            "[1096]\tVal-rmse:198.269\n",
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            "[1099]\tVal-rmse:198.252\n",
            "[1100]\tVal-rmse:198.243\n",
            "[1101]\tVal-rmse:198.24\n",
            "[1102]\tVal-rmse:198.24\n",
            "[1103]\tVal-rmse:198.259\n",
            "[1104]\tVal-rmse:198.263\n",
            "[1105]\tVal-rmse:198.266\n",
            "[1106]\tVal-rmse:198.266\n",
            "[1107]\tVal-rmse:198.279\n",
            "[1108]\tVal-rmse:198.285\n",
            "[1109]\tVal-rmse:198.269\n",
            "[1110]\tVal-rmse:198.275\n",
            "[1111]\tVal-rmse:198.263\n",
            "[1112]\tVal-rmse:198.269\n",
            "[1113]\tVal-rmse:198.264\n",
            "[1114]\tVal-rmse:198.26\n",
            "[1115]\tVal-rmse:198.248\n",
            "[1116]\tVal-rmse:198.243\n",
            "[1117]\tVal-rmse:198.239\n",
            "[1118]\tVal-rmse:198.23\n",
            "[1119]\tVal-rmse:198.243\n",
            "[1120]\tVal-rmse:198.238\n",
            "[1121]\tVal-rmse:198.228\n",
            "[1122]\tVal-rmse:198.219\n",
            "[1123]\tVal-rmse:198.222\n",
            "[1124]\tVal-rmse:198.201\n",
            "[1125]\tVal-rmse:198.197\n",
            "[1126]\tVal-rmse:198.2\n",
            "[1127]\tVal-rmse:198.173\n",
            "[1128]\tVal-rmse:198.174\n",
            "[1129]\tVal-rmse:198.164\n",
            "[1130]\tVal-rmse:198.164\n",
            "[1131]\tVal-rmse:198.161\n",
            "[1132]\tVal-rmse:198.15\n",
            "[1133]\tVal-rmse:198.148\n",
            "[1134]\tVal-rmse:198.136\n",
            "[1135]\tVal-rmse:198.137\n",
            "[1136]\tVal-rmse:198.154\n",
            "[1137]\tVal-rmse:198.156\n",
            "[1138]\tVal-rmse:198.16\n",
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            "[1140]\tVal-rmse:198.148\n",
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            "[1142]\tVal-rmse:198.135\n",
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            "[1145]\tVal-rmse:198.124\n",
            "[1146]\tVal-rmse:198.138\n",
            "[1147]\tVal-rmse:198.166\n",
            "[1148]\tVal-rmse:198.162\n",
            "[1149]\tVal-rmse:198.159\n",
            "[1150]\tVal-rmse:198.156\n",
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            "[1152]\tVal-rmse:198.147\n",
            "[1153]\tVal-rmse:198.14\n",
            "[1154]\tVal-rmse:198.138\n",
            "[1155]\tVal-rmse:198.136\n",
            "[1156]\tVal-rmse:198.115\n",
            "[1157]\tVal-rmse:198.105\n",
            "[1158]\tVal-rmse:198.1\n",
            "[1159]\tVal-rmse:198.09\n",
            "[1160]\tVal-rmse:198.086\n",
            "[1161]\tVal-rmse:198.071\n",
            "[1162]\tVal-rmse:198.068\n",
            "[1163]\tVal-rmse:198.072\n",
            "[1164]\tVal-rmse:198.069\n",
            "[1165]\tVal-rmse:198.065\n",
            "[1166]\tVal-rmse:198.065\n",
            "[1167]\tVal-rmse:198.053\n",
            "[1168]\tVal-rmse:198.048\n",
            "[1169]\tVal-rmse:198.036\n",
            "[1170]\tVal-rmse:198.038\n",
            "[1171]\tVal-rmse:198.036\n",
            "[1172]\tVal-rmse:198.033\n",
            "[1173]\tVal-rmse:198.062\n",
            "[1174]\tVal-rmse:198.059\n",
            "[1175]\tVal-rmse:198.062\n",
            "[1176]\tVal-rmse:198.057\n",
            "[1177]\tVal-rmse:198.051\n",
            "[1178]\tVal-rmse:198.038\n",
            "[1179]\tVal-rmse:198.032\n",
            "[1180]\tVal-rmse:198.021\n",
            "[1181]\tVal-rmse:198.005\n",
            "[1182]\tVal-rmse:198.003\n",
            "[1183]\tVal-rmse:198.005\n",
            "[1184]\tVal-rmse:198.007\n",
            "[1185]\tVal-rmse:198.014\n",
            "[1186]\tVal-rmse:198.022\n",
            "[1187]\tVal-rmse:198.021\n",
            "[1188]\tVal-rmse:198.008\n",
            "[1189]\tVal-rmse:197.995\n",
            "[1190]\tVal-rmse:198.007\n",
            "[1191]\tVal-rmse:198.01\n",
            "[1192]\tVal-rmse:198.009\n",
            "[1193]\tVal-rmse:198.009\n",
            "[1194]\tVal-rmse:197.993\n",
            "[1195]\tVal-rmse:197.989\n",
            "[1196]\tVal-rmse:197.964\n",
            "[1197]\tVal-rmse:197.962\n",
            "[1198]\tVal-rmse:197.967\n",
            "[1199]\tVal-rmse:197.977\n",
            "[1200]\tVal-rmse:197.973\n",
            "[1201]\tVal-rmse:197.962\n",
            "[1202]\tVal-rmse:197.956\n",
            "[1203]\tVal-rmse:197.952\n",
            "[1204]\tVal-rmse:197.951\n",
            "[1205]\tVal-rmse:197.953\n",
            "[1206]\tVal-rmse:197.946\n",
            "[1207]\tVal-rmse:197.954\n",
            "[1208]\tVal-rmse:197.948\n",
            "[1209]\tVal-rmse:197.936\n",
            "[1210]\tVal-rmse:197.935\n",
            "[1211]\tVal-rmse:197.932\n",
            "[1212]\tVal-rmse:197.925\n",
            "[1213]\tVal-rmse:197.923\n",
            "[1214]\tVal-rmse:197.912\n",
            "[1215]\tVal-rmse:197.912\n",
            "[1216]\tVal-rmse:197.906\n",
            "[1217]\tVal-rmse:197.905\n",
            "[1218]\tVal-rmse:197.92\n",
            "[1219]\tVal-rmse:197.914\n",
            "[1220]\tVal-rmse:197.899\n",
            "[1221]\tVal-rmse:197.898\n",
            "[1222]\tVal-rmse:197.894\n",
            "[1223]\tVal-rmse:197.894\n",
            "[1224]\tVal-rmse:197.893\n",
            "[1225]\tVal-rmse:197.892\n",
            "[1226]\tVal-rmse:197.884\n",
            "[1227]\tVal-rmse:197.874\n",
            "[1228]\tVal-rmse:197.862\n",
            "[1229]\tVal-rmse:197.849\n",
            "[1230]\tVal-rmse:197.849\n",
            "[1231]\tVal-rmse:197.85\n",
            "[1232]\tVal-rmse:197.844\n",
            "[1233]\tVal-rmse:197.846\n",
            "[1234]\tVal-rmse:197.845\n",
            "[1235]\tVal-rmse:197.836\n",
            "[1236]\tVal-rmse:197.826\n",
            "[1237]\tVal-rmse:197.83\n",
            "[1238]\tVal-rmse:197.84\n",
            "[1239]\tVal-rmse:197.835\n",
            "[1240]\tVal-rmse:197.828\n",
            "[1241]\tVal-rmse:197.823\n",
            "[1242]\tVal-rmse:197.828\n",
            "[1243]\tVal-rmse:197.812\n",
            "[1244]\tVal-rmse:197.799\n",
            "[1245]\tVal-rmse:197.796\n",
            "[1246]\tVal-rmse:197.812\n",
            "[1247]\tVal-rmse:197.806\n",
            "[1248]\tVal-rmse:197.803\n",
            "[1249]\tVal-rmse:197.797\n",
            "[1250]\tVal-rmse:197.792\n",
            "[1251]\tVal-rmse:197.789\n",
            "[1252]\tVal-rmse:197.77\n",
            "[1253]\tVal-rmse:197.772\n",
            "[1254]\tVal-rmse:197.755\n",
            "[1255]\tVal-rmse:197.749\n",
            "[1256]\tVal-rmse:197.749\n",
            "[1257]\tVal-rmse:197.759\n",
            "[1258]\tVal-rmse:197.748\n",
            "[1259]\tVal-rmse:197.73\n",
            "[1260]\tVal-rmse:197.724\n",
            "[1261]\tVal-rmse:197.742\n",
            "[1262]\tVal-rmse:197.732\n",
            "[1263]\tVal-rmse:197.735\n",
            "[1264]\tVal-rmse:197.722\n",
            "[1265]\tVal-rmse:197.726\n",
            "[1266]\tVal-rmse:197.71\n",
            "[1267]\tVal-rmse:197.712\n",
            "[1268]\tVal-rmse:197.716\n",
            "[1269]\tVal-rmse:197.71\n",
            "[1270]\tVal-rmse:197.707\n",
            "[1271]\tVal-rmse:197.713\n",
            "[1272]\tVal-rmse:197.698\n",
            "[1273]\tVal-rmse:197.704\n",
            "[1274]\tVal-rmse:197.706\n",
            "[1275]\tVal-rmse:197.703\n",
            "[1276]\tVal-rmse:197.7\n",
            "[1277]\tVal-rmse:197.694\n",
            "[1278]\tVal-rmse:197.703\n",
            "[1279]\tVal-rmse:197.684\n",
            "[1280]\tVal-rmse:197.666\n",
            "[1281]\tVal-rmse:197.665\n",
            "[1282]\tVal-rmse:197.652\n",
            "[1283]\tVal-rmse:197.654\n",
            "[1284]\tVal-rmse:197.646\n",
            "[1285]\tVal-rmse:197.635\n",
            "[1286]\tVal-rmse:197.655\n",
            "[1287]\tVal-rmse:197.658\n",
            "[1288]\tVal-rmse:197.647\n",
            "[1289]\tVal-rmse:197.638\n",
            "[1290]\tVal-rmse:197.655\n",
            "[1291]\tVal-rmse:197.653\n",
            "[1292]\tVal-rmse:197.649\n",
            "[1293]\tVal-rmse:197.642\n",
            "[1294]\tVal-rmse:197.638\n",
            "[1295]\tVal-rmse:197.638\n",
            "[1296]\tVal-rmse:197.637\n",
            "[1297]\tVal-rmse:197.636\n",
            "[1298]\tVal-rmse:197.635\n",
            "[1299]\tVal-rmse:197.646\n",
            "[1300]\tVal-rmse:197.653\n",
            "[1301]\tVal-rmse:197.636\n",
            "[1302]\tVal-rmse:197.632\n",
            "[1303]\tVal-rmse:197.618\n",
            "[1304]\tVal-rmse:197.613\n",
            "[1305]\tVal-rmse:197.636\n",
            "[1306]\tVal-rmse:197.63\n",
            "[1307]\tVal-rmse:197.629\n",
            "[1308]\tVal-rmse:197.62\n",
            "[1309]\tVal-rmse:197.612\n",
            "[1310]\tVal-rmse:197.607\n",
            "[1311]\tVal-rmse:197.611\n",
            "[1312]\tVal-rmse:197.629\n",
            "[1313]\tVal-rmse:197.648\n",
            "[1314]\tVal-rmse:197.653\n",
            "[1315]\tVal-rmse:197.661\n",
            "[1316]\tVal-rmse:197.656\n",
            "[1317]\tVal-rmse:197.649\n",
            "[1318]\tVal-rmse:197.647\n",
            "[1319]\tVal-rmse:197.645\n",
            "[1320]\tVal-rmse:197.648\n",
            "[1321]\tVal-rmse:197.619\n",
            "[1322]\tVal-rmse:197.605\n",
            "[1323]\tVal-rmse:197.597\n",
            "[1324]\tVal-rmse:197.597\n",
            "[1325]\tVal-rmse:197.587\n",
            "[1326]\tVal-rmse:197.596\n",
            "[1327]\tVal-rmse:197.594\n",
            "[1328]\tVal-rmse:197.598\n",
            "[1329]\tVal-rmse:197.598\n",
            "[1330]\tVal-rmse:197.596\n",
            "[1331]\tVal-rmse:197.593\n",
            "[1332]\tVal-rmse:197.581\n",
            "[1333]\tVal-rmse:197.584\n",
            "[1334]\tVal-rmse:197.589\n",
            "[1335]\tVal-rmse:197.584\n",
            "[1336]\tVal-rmse:197.57\n",
            "[1337]\tVal-rmse:197.56\n",
            "[1338]\tVal-rmse:197.558\n",
            "[1339]\tVal-rmse:197.547\n",
            "[1340]\tVal-rmse:197.553\n",
            "[1341]\tVal-rmse:197.554\n",
            "[1342]\tVal-rmse:197.552\n",
            "[1343]\tVal-rmse:197.556\n",
            "[1344]\tVal-rmse:197.544\n",
            "[1345]\tVal-rmse:197.556\n",
            "[1346]\tVal-rmse:197.55\n",
            "[1347]\tVal-rmse:197.547\n",
            "[1348]\tVal-rmse:197.532\n",
            "[1349]\tVal-rmse:197.534\n",
            "[1350]\tVal-rmse:197.515\n",
            "[1351]\tVal-rmse:197.511\n",
            "[1352]\tVal-rmse:197.521\n",
            "[1353]\tVal-rmse:197.498\n",
            "[1354]\tVal-rmse:197.497\n",
            "[1355]\tVal-rmse:197.49\n",
            "[1356]\tVal-rmse:197.483\n",
            "[1357]\tVal-rmse:197.476\n",
            "[1358]\tVal-rmse:197.482\n",
            "[1359]\tVal-rmse:197.474\n",
            "[1360]\tVal-rmse:197.46\n",
            "[1361]\tVal-rmse:197.458\n",
            "[1362]\tVal-rmse:197.436\n",
            "[1363]\tVal-rmse:197.439\n",
            "[1364]\tVal-rmse:197.424\n",
            "[1365]\tVal-rmse:197.417\n",
            "[1366]\tVal-rmse:197.416\n",
            "[1367]\tVal-rmse:197.413\n",
            "[1368]\tVal-rmse:197.412\n",
            "[1369]\tVal-rmse:197.409\n",
            "[1370]\tVal-rmse:197.396\n",
            "[1371]\tVal-rmse:197.385\n",
            "[1372]\tVal-rmse:197.388\n",
            "[1373]\tVal-rmse:197.387\n",
            "[1374]\tVal-rmse:197.384\n",
            "[1375]\tVal-rmse:197.386\n",
            "[1376]\tVal-rmse:197.381\n",
            "[1377]\tVal-rmse:197.379\n",
            "[1378]\tVal-rmse:197.372\n",
            "[1379]\tVal-rmse:197.392\n",
            "[1380]\tVal-rmse:197.388\n",
            "[1381]\tVal-rmse:197.379\n",
            "[1382]\tVal-rmse:197.382\n",
            "[1383]\tVal-rmse:197.382\n",
            "[1384]\tVal-rmse:197.371\n",
            "[1385]\tVal-rmse:197.37\n",
            "[1386]\tVal-rmse:197.369\n",
            "[1387]\tVal-rmse:197.366\n",
            "[1388]\tVal-rmse:197.359\n",
            "[1389]\tVal-rmse:197.356\n",
            "[1390]\tVal-rmse:197.335\n",
            "[1391]\tVal-rmse:197.335\n",
            "[1392]\tVal-rmse:197.331\n",
            "[1393]\tVal-rmse:197.329\n",
            "[1394]\tVal-rmse:197.328\n",
            "[1395]\tVal-rmse:197.32\n",
            "[1396]\tVal-rmse:197.32\n",
            "[1397]\tVal-rmse:197.323\n",
            "[1398]\tVal-rmse:197.333\n",
            "[1399]\tVal-rmse:197.337\n",
            "[1400]\tVal-rmse:197.327\n",
            "[1401]\tVal-rmse:197.325\n",
            "[1402]\tVal-rmse:197.319\n",
            "[1403]\tVal-rmse:197.315\n",
            "[1404]\tVal-rmse:197.299\n",
            "[1405]\tVal-rmse:197.297\n",
            "[1406]\tVal-rmse:197.285\n",
            "[1407]\tVal-rmse:197.269\n",
            "[1408]\tVal-rmse:197.267\n",
            "[1409]\tVal-rmse:197.261\n",
            "[1410]\tVal-rmse:197.267\n",
            "[1411]\tVal-rmse:197.264\n",
            "[1412]\tVal-rmse:197.261\n",
            "[1413]\tVal-rmse:197.251\n",
            "[1414]\tVal-rmse:197.253\n",
            "[1415]\tVal-rmse:197.254\n",
            "[1416]\tVal-rmse:197.241\n",
            "[1417]\tVal-rmse:197.252\n",
            "[1418]\tVal-rmse:197.256\n",
            "[1419]\tVal-rmse:197.256\n",
            "[1420]\tVal-rmse:197.253\n",
            "[1421]\tVal-rmse:197.26\n",
            "[1422]\tVal-rmse:197.238\n",
            "[1423]\tVal-rmse:197.242\n",
            "[1424]\tVal-rmse:197.235\n",
            "[1425]\tVal-rmse:197.234\n",
            "[1426]\tVal-rmse:197.218\n",
            "[1427]\tVal-rmse:197.206\n",
            "[1428]\tVal-rmse:197.217\n",
            "[1429]\tVal-rmse:197.217\n",
            "[1430]\tVal-rmse:197.212\n",
            "[1431]\tVal-rmse:197.216\n",
            "[1432]\tVal-rmse:197.216\n",
            "[1433]\tVal-rmse:197.214\n",
            "[1434]\tVal-rmse:197.217\n",
            "[1435]\tVal-rmse:197.224\n",
            "[1436]\tVal-rmse:197.225\n",
            "[1437]\tVal-rmse:197.229\n",
            "[1438]\tVal-rmse:197.226\n",
            "[1439]\tVal-rmse:197.22\n",
            "[1440]\tVal-rmse:197.22\n",
            "[1441]\tVal-rmse:197.213\n",
            "[1442]\tVal-rmse:197.21\n",
            "[1443]\tVal-rmse:197.199\n",
            "[1444]\tVal-rmse:197.189\n",
            "[1445]\tVal-rmse:197.188\n",
            "[1446]\tVal-rmse:197.186\n",
            "[1447]\tVal-rmse:197.176\n",
            "[1448]\tVal-rmse:197.166\n",
            "[1449]\tVal-rmse:197.162\n",
            "[1450]\tVal-rmse:197.15\n",
            "[1451]\tVal-rmse:197.169\n",
            "[1452]\tVal-rmse:197.167\n",
            "[1453]\tVal-rmse:197.165\n",
            "[1454]\tVal-rmse:197.158\n",
            "[1455]\tVal-rmse:197.147\n",
            "[1456]\tVal-rmse:197.14\n",
            "[1457]\tVal-rmse:197.133\n",
            "[1458]\tVal-rmse:197.134\n",
            "[1459]\tVal-rmse:197.127\n",
            "[1460]\tVal-rmse:197.123\n",
            "[1461]\tVal-rmse:197.121\n",
            "[1462]\tVal-rmse:197.109\n",
            "[1463]\tVal-rmse:197.095\n",
            "[1464]\tVal-rmse:197.099\n",
            "[1465]\tVal-rmse:197.097\n",
            "[1466]\tVal-rmse:197.093\n",
            "[1467]\tVal-rmse:197.093\n",
            "[1468]\tVal-rmse:197.095\n",
            "[1469]\tVal-rmse:197.093\n",
            "[1470]\tVal-rmse:197.088\n",
            "[1471]\tVal-rmse:197.079\n",
            "[1472]\tVal-rmse:197.078\n",
            "[1473]\tVal-rmse:197.08\n",
            "[1474]\tVal-rmse:197.078\n",
            "[1475]\tVal-rmse:197.077\n",
            "[1476]\tVal-rmse:197.076\n",
            "[1477]\tVal-rmse:197.071\n",
            "[1478]\tVal-rmse:197.089\n",
            "[1479]\tVal-rmse:197.09\n",
            "[1480]\tVal-rmse:197.099\n",
            "[1481]\tVal-rmse:197.09\n",
            "[1482]\tVal-rmse:197.093\n",
            "[1483]\tVal-rmse:197.088\n",
            "[1484]\tVal-rmse:197.092\n",
            "[1485]\tVal-rmse:197.094\n",
            "[1486]\tVal-rmse:197.088\n",
            "[1487]\tVal-rmse:197.092\n",
            "[1488]\tVal-rmse:197.09\n",
            "[1489]\tVal-rmse:197.089\n",
            "[1490]\tVal-rmse:197.113\n",
            "[1491]\tVal-rmse:197.111\n",
            "[1492]\tVal-rmse:197.111\n",
            "[1493]\tVal-rmse:197.13\n",
            "[1494]\tVal-rmse:197.122\n",
            "[1495]\tVal-rmse:197.126\n",
            "[1496]\tVal-rmse:197.124\n",
            "[1497]\tVal-rmse:197.127\n",
            "Stopping. Best iteration:\n",
            "[1477]\tVal-rmse:197.071\n",
            "\n",
            "Best rmse: 197.07 with 1478 rounds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "params"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5-iv2Uqpj40k",
        "outputId": "02a9fca9-9abb-4ade-b5fe-734d762d2a6c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'colsample_bytree': 0.9,\n",
              " 'eta': 0.03,\n",
              " 'eval_metric': 'rmse',\n",
              " 'max_depth': 8,\n",
              " 'min_child_weight': 2,\n",
              " 'subsample': 0.9}"
            ]
          },
          "metadata": {},
          "execution_count": 224
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JCKbu0PHgGVX"
      },
      "outputs": [],
      "source": [
        "model = xgb.XGBRegressor(n_estimators=20000,\n",
        "                         max_depth=8,\n",
        "                         learning_rate=0.03,\n",
        "                         min_child_weight=2,\n",
        "                         subsample=0.9,\n",
        "                         colsample_bytree=0.9,\n",
        "                         objective='reg:squarederror',\n",
        "                         n_jobs=-1,\n",
        "                         random_state=42)\n",
        "history = model.fit(df1, target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "E85KmWwrmO1D"
      },
      "outputs": [],
      "source": [
        "pred = model.predict(test_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cgom6-Qpm4kd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "bb18b696-5dfc-44d1-be31-03acdcd3c181"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id      price1\n",
              "0   2242  431.359680\n",
              "1  20532  282.660431\n",
              "2  10648  492.789856\n",
              "3  20677  781.311462\n",
              "4  12593  403.916992"
            ],
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              "    .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-355c43e0-bc19-4f21-ab8a-fc06070f997e 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-355c43e0-bc19-4f21-ab8a-fc06070f997e');\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": 62
        }
      ],
      "source": [
        "submission = pd.DataFrame({\n",
        "    'id': test_df.index,\n",
        "    'price1': pred\n",
        "})\n",
        "\n",
        "submission.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dL5ePfiHm8be"
      },
      "outputs": [],
      "source": [
        "submission.to_csv('submission.csv', index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SpGPtlFdm-Gs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f69f8a20-80ba-4c1d-e091-a5de850feec4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "100% 77.2k/77.2k [00:02<00:00, 26.5kB/s]\n",
            "400 - Bad Request\n"
          ]
        }
      ],
      "source": [
        "!kaggle competitions submit -c sa2022 -f submission.csv -m \"Message\""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "a0lEyBCI0x41"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Retail Discount Prediction.ipynb",
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}