AV - Food_Demand_Forecasting.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNZVB+yZmIzYnDX0riBY/Jq",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mukul-mschauhan/linear-regression/blob/main/Food_Demand_Forecasting.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Genpact Machine Learning Hackathon\n",
        "\n",
        "Your client is a meal delivery company which operates in multiple cities. They have various **fulfillment centers** in these cities for dispatching meal orders to their customers. The client wants you to help these centers with **demand forecasting** for upcoming weeks so that these centers will plan the stock of raw materials accordingly.\n",
        "\n",
        "The replenishment of majority of raw materials is done on weekly basis and since the raw material is perishable, the procurement planning is of utmost importance. Secondly, **staffing of the centers** is also one area wherein accurate demand forecasts are really helpful. Given the following information, the task is to **predict the demand for the next 10 weeks** (Weeks: 146-155) for the center-meal combinations in the test set:\n",
        "\n",
        "* Historical data of demand for a product-center combination (Weeks: 1 to 145)\n",
        "* Product(Meal) features such as category, sub-category, current price and discount\n",
        "* Information for fulfillment center like center area, city information etc."
      ],
      "metadata": {
        "id": "Ofz56wt0-9MO"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "mnlIEyFP-uvv"
      },
      "outputs": [],
      "source": [
        "# Import the Libbraries\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train = pd.read_csv(\"/content/train.csv\")\n",
        "test = pd.read_csv(\"/content/test_QoiMO9B.csv\")\n",
        "meal = pd.read_csv(\"/content/meal_info.csv\")\n",
        "centres = pd.read_csv(\"/content/fulfilment_center_info.csv\")\n",
        "submission = pd.read_csv(\"/content/sample_submission_hSlSoT6.csv\")"
      ],
      "metadata": {
        "id": "gc0l-MnPAYZ1"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the Dataset\n",
        "train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "oxTpEgkgAx6e",
        "outputId": "8e541ba5-f9f9-44da-e0b8-59c5398abdc5"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        id  week  center_id  meal_id  checkout_price  base_price  \\\n",
              "0  1379560     1         55     1885          136.83      152.29   \n",
              "1  1466964     1         55     1993          136.83      135.83   \n",
              "2  1346989     1         55     2539          134.86      135.86   \n",
              "3  1338232     1         55     2139          339.50      437.53   \n",
              "4  1448490     1         55     2631          243.50      242.50   \n",
              "\n",
              "   emailer_for_promotion  homepage_featured  num_orders  \n",
              "0                      0                  0         177  \n",
              "1                      0                  0         270  \n",
              "2                      0                  0         189  \n",
              "3                      0                  0          54  \n",
              "4                      0                  0          40  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d821450d-2973-4295-944b-c0a4b7114168\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>week</th>\n",
              "      <th>center_id</th>\n",
              "      <th>meal_id</th>\n",
              "      <th>checkout_price</th>\n",
              "      <th>base_price</th>\n",
              "      <th>emailer_for_promotion</th>\n",
              "      <th>homepage_featured</th>\n",
              "      <th>num_orders</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1379560</td>\n",
              "      <td>1</td>\n",
              "      <td>55</td>\n",
              "      <td>1885</td>\n",
              "      <td>136.83</td>\n",
              "      <td>152.29</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1466964</td>\n",
              "      <td>1</td>\n",
              "      <td>55</td>\n",
              "      <td>1993</td>\n",
              "      <td>136.83</td>\n",
              "      <td>135.83</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1346989</td>\n",
              "      <td>1</td>\n",
              "      <td>55</td>\n",
              "      <td>2539</td>\n",
              "      <td>134.86</td>\n",
              "      <td>135.86</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1338232</td>\n",
              "      <td>1</td>\n",
              "      <td>55</td>\n",
              "      <td>2139</td>\n",
              "      <td>339.50</td>\n",
              "      <td>437.53</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1448490</td>\n",
              "      <td>1</td>\n",
              "      <td>55</td>\n",
              "      <td>2631</td>\n",
              "      <td>243.50</td>\n",
              "      <td>242.50</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d821450d-2973-4295-944b-c0a4b7114168')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-d821450d-2973-4295-944b-c0a4b7114168 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-d821450d-2973-4295-944b-c0a4b7114168');\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",
              "\n",
              "\n",
              "<div id=\"df-37cd686b-95f1-4e80-9ac0-9ec7271319ff\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-37cd686b-95f1-4e80-9ac0-9ec7271319ff')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-37cd686b-95f1-4e80-9ac0-9ec7271319ff button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.columns, test.columns, centres.columns, meal.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GQtYPUPeBVoq",
        "outputId": "e8793a3b-80a8-4d6a-dcfa-c2e2ef9f3935"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Index(['id', 'week', 'center_id', 'meal_id', 'checkout_price', 'base_price',\n",
              "        'emailer_for_promotion', 'homepage_featured', 'num_orders'],\n",
              "       dtype='object'),\n",
              " Index(['id', 'week', 'center_id', 'meal_id', 'checkout_price', 'base_price',\n",
              "        'emailer_for_promotion', 'homepage_featured'],\n",
              "       dtype='object'),\n",
              " Index(['center_id', 'city_code', 'region_code', 'center_type', 'op_area'], dtype='object'),\n",
              " Index(['meal_id', 'category', 'cuisine'], dtype='object'))"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Combine Train and Test\n",
        "combined = pd.concat([train, test], ignore_index = True)\n",
        "combined = pd.merge(combined, centres, how = \"left\", on = \"center_id\")\n",
        "combined = pd.merge(combined, meal, how = \"left\", on = \"meal_id\")"
      ],
      "metadata": {
        "id": "AFGlXakNBnmq"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.shape, test.shape, meal.shape, centres.shape, combined.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9XvsTYvnCQQ6",
        "outputId": "e7b2d80e-15e0-44f0-df24-28f498ecd374"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((456548, 9), (32573, 8), (51, 3), (77, 5), (489121, 15))"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "combined.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "36ivOg9-CYUR",
        "outputId": "bfc53faf-0b5e-4acf-b360-84fb09f7e111"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        id  week  center_id  meal_id  checkout_price  base_price  \\\n",
              "0  1379560     1         55     1885          136.83      152.29   \n",
              "1  1466964     1         55     1993          136.83      135.83   \n",
              "2  1346989     1         55     2539          134.86      135.86   \n",
              "3  1338232     1         55     2139          339.50      437.53   \n",
              "4  1448490     1         55     2631          243.50      242.50   \n",
              "\n",
              "   emailer_for_promotion  homepage_featured  num_orders  city_code  \\\n",
              "0                      0                  0       177.0        647   \n",
              "1                      0                  0       270.0        647   \n",
              "2                      0                  0       189.0        647   \n",
              "3                      0                  0        54.0        647   \n",
              "4                      0                  0        40.0        647   \n",
              "\n",
              "   region_code center_type  op_area   category cuisine  log_target  \\\n",
              "0           56      TYPE_C      2.0  Beverages    Thai    5.176150   \n",
              "1           56      TYPE_C      2.0  Beverages    Thai    5.598422   \n",
              "2           56      TYPE_C      2.0  Beverages    Thai    5.241747   \n",
              "3           56      TYPE_C      2.0  Beverages  Indian    3.988984   \n",
              "4           56      TYPE_C      2.0  Beverages  Indian    3.688879   \n",
              "\n",
              "   price_diff   price_cat  marketing  \n",
              "0       15.46  Addnl_Chgs          0  \n",
              "1       -1.00    Discount          0  \n",
              "2        1.00       Taxes          0  \n",
              "3       98.03  Addnl_Chgs          0  \n",
              "4       -1.00    Discount          0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-087d2f9b-2696-46f2-b84f-5261a36d283a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>week</th>\n",
              "      <th>center_id</th>\n",
              "      <th>meal_id</th>\n",
              "      <th>checkout_price</th>\n",
              "      <th>base_price</th>\n",
              "      <th>emailer_for_promotion</th>\n",
              "      <th>homepage_featured</th>\n",
              "      <th>num_orders</th>\n",
              "      <th>city_code</th>\n",
              "      <th>region_code</th>\n",
              "      <th>center_type</th>\n",
              "      <th>op_area</th>\n",
              "      <th>category</th>\n",
              "      <th>cuisine</th>\n",
              "      <th>log_target</th>\n",
              "      <th>price_diff</th>\n",
              "      <th>price_cat</th>\n",
              "      <th>marketing</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1379560</td>\n",
              "      <td>1</td>\n",
              "      <td>55</td>\n",
              "      <td>1885</td>\n",
              "      <td>136.83</td>\n",
              "      <td>152.29</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>177.0</td>\n",
              "      <td>647</td>\n",
              "      <td>56</td>\n",
              "      <td>TYPE_C</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Beverages</td>\n",
              "      <td>Thai</td>\n",
              "      <td>5.176150</td>\n",
              "      <td>15.46</td>\n",
              "      <td>Addnl_Chgs</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1466964</td>\n",
              "      <td>1</td>\n",
              "      <td>55</td>\n",
              "      <td>1993</td>\n",
              "      <td>136.83</td>\n",
              "      <td>135.83</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>270.0</td>\n",
              "      <td>647</td>\n",
              "      <td>56</td>\n",
              "      <td>TYPE_C</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Beverages</td>\n",
              "      <td>Thai</td>\n",
              "      <td>5.598422</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>Discount</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1346989</td>\n",
              "      <td>1</td>\n",
              "      <td>55</td>\n",
              "      <td>2539</td>\n",
              "      <td>134.86</td>\n",
              "      <td>135.86</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>189.0</td>\n",
              "      <td>647</td>\n",
              "      <td>56</td>\n",
              "      <td>TYPE_C</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Beverages</td>\n",
              "      <td>Thai</td>\n",
              "      <td>5.241747</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Taxes</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1338232</td>\n",
              "      <td>1</td>\n",
              "      <td>55</td>\n",
              "      <td>2139</td>\n",
              "      <td>339.50</td>\n",
              "      <td>437.53</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>647</td>\n",
              "      <td>56</td>\n",
              "      <td>TYPE_C</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Beverages</td>\n",
              "      <td>Indian</td>\n",
              "      <td>3.988984</td>\n",
              "      <td>98.03</td>\n",
              "      <td>Addnl_Chgs</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1448490</td>\n",
              "      <td>1</td>\n",
              "      <td>55</td>\n",
              "      <td>2631</td>\n",
              "      <td>243.50</td>\n",
              "      <td>242.50</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>647</td>\n",
              "      <td>56</td>\n",
              "      <td>TYPE_C</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Beverages</td>\n",
              "      <td>Indian</td>\n",
              "      <td>3.688879</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>Discount</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-087d2f9b-2696-46f2-b84f-5261a36d283a')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-087d2f9b-2696-46f2-b84f-5261a36d283a 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-087d2f9b-2696-46f2-b84f-5261a36d283a');\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",
              "\n",
              "\n",
              "<div id=\"df-dd713d36-a827-4943-ac85-cd8e5c34b0b0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dd713d36-a827-4943-ac85-cd8e5c34b0b0')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-dd713d36-a827-4943-ac85-cd8e5c34b0b0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "combined"
            }
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Target Variable\n",
        "\n",
        "sns.displot(x = \"num_orders\", data = combined)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "id": "WcDVT3JrDB6j",
        "outputId": "b10dcd35-1e18-4fd8-bd5e-8ed759abaf44"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(np.log(combined.num_orders))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "xXd5SkfpDvNq",
        "outputId": "60d6efc6-3c57-470f-b8a1-487a8f58586b"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.log(combined.num_orders).describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "az5nX-_iD9dS",
        "outputId": "99b7fcbe-d0f1-4829-99b5-1b7ab905a227"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    456548.000000\n",
              "mean          4.873096\n",
              "std           1.219120\n",
              "min           2.564949\n",
              "25%           3.988984\n",
              "50%           4.912655\n",
              "75%           5.780744\n",
              "max          10.098190\n",
              "Name: num_orders, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>num_orders</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>456548.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.873096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.219120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.564949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.988984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.912655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5.780744</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>10.098190</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "combined[\"log_target\"] = np.log(combined.num_orders)"
      ],
      "metadata": {
        "id": "FS0LPVfuEg3o"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Base Price\n",
        "\n",
        "sns.displot(combined.base_price)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "id": "vSWoXbiEEtg4",
        "outputId": "a1c43ed2-632b-41e4-bdb5-f3d71a142083"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(combined.checkout_price)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "id": "qD3RtG1yFPDX",
        "outputId": "6156c5bd-08ac-4afd-e84b-7631379b5537"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Treatment of Prices\n",
        "combined[\"price_diff\"] = combined.base_price - combined.checkout_price\n",
        "\n",
        "def price(x):\n",
        "  if x<0:\n",
        "    return(\"Discount\")\n",
        "  elif x<=10:\n",
        "    return(\"Taxes\")\n",
        "  else:\n",
        "    return(\"Addnl_Chgs\")"
      ],
      "metadata": {
        "id": "k8LjsbRMFXrP"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "combined[\"price_cat\"] = combined.price_diff.apply(price)"
      ],
      "metadata": {
        "id": "EN0urhDLFot5"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "combined.price_cat.value_counts(normalize = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "id": "y22dDwMRGAr4",
        "outputId": "8f9b11d8-95a1-4fb5-9f3c-6f419f80fdce"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "price_cat\n",
              "Taxes         0.495810\n",
              "Discount      0.255638\n",
              "Addnl_Chgs    0.248552\n",
              "Name: proportion, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>proportion</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>price_cat</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Taxes</th>\n",
              "      <td>0.495810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Discount</th>\n",
              "      <td>0.255638</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Addnl_Chgs</th>\n",
              "      <td>0.248552</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "combined.loc[:, [\"price_diff\", \"num_orders\"]].corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "Q-rFdlJSI4Pf",
        "outputId": "987cce62-262f-4c80-82cf-b3d5c3943f83"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            price_diff  num_orders\n",
              "price_diff     1.00000     0.15295\n",
              "num_orders     0.15295     1.00000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-506ab428-6156-4081-bebf-4840f3c19511\" 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>price_diff</th>\n",
              "      <th>num_orders</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>price_diff</th>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.15295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_orders</th>\n",
              "      <td>0.15295</td>\n",
              "      <td>1.00000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-506ab428-6156-4081-bebf-4840f3c19511')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-506ab428-6156-4081-bebf-4840f3c19511 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-506ab428-6156-4081-bebf-4840f3c19511');\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",
              "\n",
              "\n",
              "<div id=\"df-d8eba839-71c4-4462-ae43-6f875aa3cb70\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d8eba839-71c4-4462-ae43-6f875aa3cb70')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d8eba839-71c4-4462-ae43-6f875aa3cb70 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"combined\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"price_diff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5989548330014707,\n        \"min\": 0.15294995192040792,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.15294995192040792,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_orders\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5989548330014707,\n        \"min\": 0.15294995192040792,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.15294995192040792\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x=\"price_cat\", y = \"num_orders\", data = combined)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "2qfl1IiHG8NA",
        "outputId": "890d618c-676b-492b-fe09-9d3df3aa63cd"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Social Medial\n",
        "combined[\"marketing\"] = combined.emailer_for_promotion+\\\n",
        "combined.homepage_featured"
      ],
      "metadata": {
        "id": "tkskYHP_Ijqn"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x = \"marketing\", y = \"num_orders\", data = combined)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "Hk6Zj3hZKN71",
        "outputId": "f055818c-e29a-4bec-af2a-d3e0e20af19a"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Cuisine\n",
        "combined.cuisine.value_counts().plot(kind = \"bar\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "id": "OlHd4zQXK7xV",
        "outputId": "dd82906b-5438-4df8-b26f-0606adf13a28"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='cuisine'>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x = \"cuisine\", y = \"num_orders\", data = combined)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "sHsrcNsuLnLE",
        "outputId": "d347e7df-60bd-4d9c-e135-443d6560b0f6"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAk0AAAGwCAYAAAC0HlECAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAABP1ElEQVR4nO3de1xUZf4H8M/MwAyQXFUYSFTwmgjeoZFCSxLLda2s1Fi1sloF97dpmrmiSGq6lXZb0O60SlvadlUzAVE2HSEVRBG1FEMTMBUYFeUyc35/uHOWAaQDDszt83695vWaOefLmWc4zMyH55zzPDJBEAQQERERUYvklm4AERERkS1gaCIiIiKSgKGJiIiISAKGJiIiIiIJGJqIiIiIJGBoIiIiIpKAoYmIiIhIAidLN8BeGAwGnDt3Du7u7pDJZJZuDhEREUkgCAIuX76MgIAAyOUt9yUxNJnJuXPnEBgYaOlmEBERURucOXMG3bp1a7GGoclM3N3dAdz4pXt4eFi4NURERCSFTqdDYGCg+D3eEoYmMzEekvPw8GBoIiIisjFSTq3hieBEREREElg0NK1atQojRoyAu7s7fH198eCDD+L48eMmNaNHj4ZMJjO5zZo1y6SmpKQE48ePh5ubG3x9fbFgwQLU19eb1OzatQtDhw6FSqVC7969kZqa2qQ9ycnJ6NmzJ1xcXBAREYHc3Fyzv2YiIiKyTRYNTbt370Z8fDz27duH9PR01NXVYezYsbh69apJ3TPPPIPS0lLx9sorr4jr9Ho9xo8fj9raWuzduxcff/wxUlNTsXTpUrGmuLgY48ePxz333IP8/Hw899xzePrpp/H999+LNZ999hnmzZuHxMREHDx4EIMGDUJMTAzOnz/f/r8IIiIisnoyQRAESzfC6LfffoOvry92796NqKgoADd6mgYPHow33nij2Z/57rvv8Ic//AHnzp2Dn58fAGD9+vVYuHAhfvvtNyiVSixcuBBbt27FkSNHxJ+bMmUKKisrsX37dgBAREQERowYgX/84x8AbgwhEBgYiL/85S948cUXmzxvTU0NampqxMfGE8mqqqp4ThMREZGN0Ol08PT0lPT9bVXnNFVVVQEAfHx8TJanpaWhS5cuGDhwIBYtWoTq6mpxnVarRWhoqBiYACAmJgY6nQ6FhYViTXR0tMk2Y2JioNVqAQC1tbU4cOCASY1cLkd0dLRY09iqVavg6ekp3jjcABERkX2zmqvnDAYDnnvuOURGRmLgwIHi8scffxw9evRAQEAACgoKsHDhQhw/fhxffPEFAKCsrMwkMAEQH5eVlbVYo9PpcO3aNVRUVECv1zdbc+zYsWbbu2jRIsybN098bOxpIiIiIvtkNaEpPj4eR44cwQ8//GCy/NlnnxXvh4aGwt/fH2PGjMHJkyfRq1evjm6mSKVSQaVSWez5iYiIqGNZxeG5OXPmYMuWLcjKyvrd0TgjIiIAAD///DMAQK1Wo7y83KTG+FitVrdY4+HhAVdXV3Tp0gUKhaLZGuM2iIiIyLFZNDQJgoA5c+bgyy+/xM6dOxEUFPS7P5Ofnw8A8Pf3BwBoNBocPnzY5Cq39PR0eHh4YMCAAWJNZmamyXbS09Oh0WgAAEqlEsOGDTOpMRgMyMzMFGuIiIjIsVn08Fx8fDw++eQTfP3113B3dxfPQfL09ISrqytOnjyJTz75BA888AA6d+6MgoICzJ07F1FRUQgLCwMAjB07FgMGDMC0adPwyiuvoKysDAkJCYiPjxcPn82aNQv/+Mc/8MILL+Cpp57Czp07sWnTJmzdulVsy7x58zBjxgwMHz4c4eHheOONN3D16lU8+eSTHf+LIbJCer0eBQUFuHTpEnx8fBAWFgaFQmHpZhERdRzBggA0e/voo48EQRCEkpISISoqSvDx8RFUKpXQu3dvYcGCBUJVVZXJdk6fPi3cf//9gqurq9ClSxfh+eefF+rq6kxqsrKyhMGDBwtKpVIIDg4Wn6Oht99+W+jevbugVCqF8PBwYd++fZJfS1VVlQCgSduI7MHu3buFyZMnC6NGjRJvkydPFnbv3m3pphER3ZLWfH9b1ThNtqw14zwQ2ZLs7GwkJiZCo9EgNjYWQUFBKC4uRlpaGrRaLZKSksRx1YiIbE1rvr8ZmsyEoYnskV6vR2xsLIKDg7FixQrI5f87DdJgMCAhIQHFxcXYuHEjD9URkU2y2cEtici6FBQUoKysDLGxsSaBCbgxAGxsbCxKS0tRUFBgoRYSEXUchiYiuqlLly4BwE2vbDUuN9YREdkzhiYiuinjlEbFxcXNrjcubzz1ERGRPWJoIqKbCgsLg1qtRlpaGgwGg8k6g8GAtLQ0+Pv7i0OAEBHZM4YmIrophUKBuLg4aLVaJCQkoLCwENXV1SgsLERCQgK0Wi1mz57Nk8CJyCHw6jkz4dVzZM+ys7ORkpIiDkAL3BiVf/bs2RxugIhsGoccsACGJrJ3HBGciOxRa76/LTqNChHZDoVCgSFDhli6GUREFsNzmoiIiIgkYGgiIiIikoChiYiIiEgChiYiIiIiCRiaiIiIiCRgaCIiIiKSgKGJiIiISAKGJiIiIiIJGJqIiIiIJGBoIiIiIpKAoYmIiIhIAoYmIiIiIgkYmoiIiIgkYGgiIiIikoChiYiIiEgChiYiIiIiCRiaiIiIiCRgaCIiIiKSgKGJiIiISAKGJiIiIiIJGJqIiIiIJGBoIiIiIpKAoYmIiIhIAoYmIiIiIgkYmoiIiIgkYGgiIiIikoChiYiIiEgChiYiIiIiCRiaiIiIiCRgaCIiIiKSgKGJiIiISAKGJiIiIiIJGJqIiIiIJGBoIiIiIpKAoYmIiIhIAoYmIiIiIgkYmoiIiIgkYGgiIiIikoChiYiIiEgChiYiIiIiCRiaiIiIiCRgaCIiIiKSgKGJiIiISAKGJiIiIiIJGJqIiIiIJGBoIiIiIpKAoYmIiIhIAoYmIiIiIgkYmoiIiIgksGhoWrVqFUaMGAF3d3f4+vriwQcfxPHjx01qrl+/jvj4eHTu3BmdOnXCpEmTUF5eblJTUlKC8ePHw83NDb6+vliwYAHq6+tNanbt2oWhQ4dCpVKhd+/eSE1NbdKe5ORk9OzZEy4uLoiIiEBubq7ZXzMRERHZJouGpt27dyM+Ph779u1Deno66urqMHbsWFy9elWsmTt3Lr799lts3rwZu3fvxrlz5/Dwww+L6/V6PcaPH4/a2lrs3bsXH3/8MVJTU7F06VKxpri4GOPHj8c999yD/Px8PPfcc3j66afx/fffizWfffYZ5s2bh8TERBw8eBCDBg1CTEwMzp8/3zG/DCIiIrJughU5f/68AEDYvXu3IAiCUFlZKTg7OwubN28Wa4qKigQAglarFQRBELZt2ybI5XKhrKxMrFm3bp3g4eEh1NTUCIIgCC+88IIQEhJi8lyTJ08WYmJixMfh4eFCfHy8+Fiv1wsBAQHCqlWrmm3r9evXhaqqKvF25swZAYBQVVV1i78FIiIi6ihVVVWSv7+t6pymqqoqAICPjw8A4MCBA6irq0N0dLRY079/f3Tv3h1arRYAoNVqERoaCj8/P7EmJiYGOp0OhYWFYk3DbRhrjNuora3FgQMHTGrkcjmio6PFmsZWrVoFT09P8RYYGHirL5+IiIismNWEJoPBgOeeew6RkZEYOHAgAKCsrAxKpRJeXl4mtX5+figrKxNrGgYm43rjupZqdDodrl27hgsXLkCv1zdbY9xGY4sWLUJVVZV4O3PmTNteOBEREdkEJ0s3wCg+Ph5HjhzBDz/8YOmmSKJSqaBSqSzdDCIiIuogVtHTNGfOHGzZsgVZWVno1q2buFytVqO2thaVlZUm9eXl5VCr1WJN46vpjI9/r8bDwwOurq7o0qULFApFszXGbRAREZFjs2hoEgQBc+bMwZdffomdO3ciKCjIZP2wYcPg7OyMzMxMcdnx48dRUlICjUYDANBoNDh8+LDJVW7p6enw8PDAgAEDxJqG2zDWGLehVCoxbNgwkxqDwYDMzEyxhoiIiBxc+5+XfnOzZ88WPD09hV27dgmlpaXirbq6WqyZNWuW0L17d2Hnzp3C/v37BY1GI2g0GnF9fX29MHDgQGHs2LFCfn6+sH37dqFr167CokWLxJpTp04Jbm5uwoIFC4SioiIhOTlZUCgUwvbt28WaTz/9VFCpVEJqaqpw9OhR4dlnnxW8vLxMrsprSWvOviciIiLr0Jrvb4uGJgDN3j766COx5tq1a0JcXJzg7e0tuLm5CQ899JBQWlpqsp3Tp08L999/v+Dq6ip06dJFeP7554W6ujqTmqysLGHw4MGCUqkUgoODTZ7D6O233xa6d+8uKJVKITw8XNi3b5/k18LQREREZHta8/0tEwRBsFQvlz3R6XTw9PREVVUVPDw8LN0cIiIikqA1399WcSI4ERERkbVjaCIiIiKSgKGJiIiISAKGJiIiIiIJGJqIiIiIJGBoIiIiIpKAoYmIiIhIAoYmIiIiIgkYmoiIiIgkYGgiIiIikoChiYiIiEgChiYiIiIiCRiaiIiIiCRgaCIiIiKSgKGJiIiISAKGJiIiIiIJGJqIiIiIJGBoIiIiIpKAoYmIiIhIAoYmIiIiIgkYmoiIiIgkYGgiIiIikoChiYiIiEgChiYiIiIiCRiaiIiIiCRgaCIiIiKSgKGJiIiISAKGJiIiIiIJGJqIiIiIJGBoIiIiIpKAoYmIiIhIAoYmIiIiIgkYmoiIiIgkYGgiIiIikoChiYiIiEgChiYiIiIiCRiaiIiIiCRgaCIiIiKSgKGJiIiISAKGJiIiIiIJGJqIiIiIJGBoIiIiIpKAoYmIiIhIAoYmIiIiIgkYmoiIiIgkYGgiIiIikoChiYiIiEgChiYiIiIiCRiaiIiIiCRgaCIiIiKSgKGJiIiISAKGJiIiIiIJnCzdACKyDXq9HgUFBbh06RJ8fHwQFhYGhUJh6WYREXUYhiYi+l3Z2dlISUlBWVmZuEytViMuLg5RUVEWbBkRUcfh4TkialF2djYSExMRHByM5ORkbNu2DcnJyQgODkZiYiKys7Mt3UQiog4hEwRBsHQj7IFOp4Onpyeqqqrg4eFh6eYQmYVer0dsbCyCg4OxYsUKyOX/+z/LYDAgISEBxcXF2LhxIw/VEZFNas33N3uaiOimCgoKUFZWhtjYWJPABAByuRyxsbEoLS1FQUGBhVpIRNRxGJqI6KYuXboEAAgKCmp2vXG5sY6IyJ5ZNDRlZ2djwoQJCAgIgEwmw1dffWWy/oknnoBMJjO5jRs3zqTm0qVLiI2NhYeHB7y8vDBz5kxcuXLFpKagoAB33303XFxcEBgYiFdeeaVJWzZv3oz+/fvDxcUFoaGh2LZtm9lfL5Gt8fHxAQAUFxc3u9643FhHRGTPLBqarl69ikGDBiE5OfmmNePGjUNpaal4+9e//mWyPjY2FoWFhUhPT8eWLVuQnZ2NZ599Vlyv0+kwduxY9OjRAwcOHMCrr76KZcuW4d133xVr9u7di6lTp2LmzJnIy8vDgw8+iAcffBBHjhwx/4smsiFhYWFQq9VIS0uDwWAwWWcwGJCWlgZ/f3+EhYVZqIVERB1IsBIAhC+//NJk2YwZM4SJEyfe9GeOHj0qABB+/PFHcdl3330nyGQy4ddffxUEQRBSUlIEb29voaamRqxZuHCh0K9fP/HxY489JowfP95k2xEREcKf//xnye2vqqoSAAhVVVWSf4bIFuzevVsYPXq0sGjRIuHIkSPC1atXhSNHjgiLFi0SRo8eLezevdvSTSQiarPWfH9b/TlNu3btgq+vL/r164fZs2fj4sWL4jqtVgsvLy8MHz5cXBYdHQ25XI6cnByxJioqCkqlUqyJiYnB8ePHUVFRIdZER0ebPG9MTAy0Wu1N21VTUwOdTmdyI7JHUVFRSEpKwqlTpxAfH48HHngA8fHxKC4uRlJSEsdpIiKHYdWDW44bNw4PP/wwgoKCcPLkSfztb3/D/fffD61WC4VCgbKyMvj6+pr8jJOTE3x8fMRB+MrKypqcxOrn5yeu8/b2RllZmbisYU3DgfwaW7VqFZKSkszxMomsXlRUFCIjIzkiOBE5NKsOTVOmTBHvh4aGIiwsDL169cKuXbswZswYC7YMWLRoEebNmyc+1ul0CAwMtGCLiNqXQqHAkCFDLN0MIiKLadPhuTNnzuDs2bPi49zcXDz33HMmJ1e3h+DgYHTp0gU///wzgBvTOJw/f96kpr6+HpcuXYJarRZrysvLTWqMj3+vxri+OSqVCh4eHiY3IiIisl9tCk2PP/44srKyANw4xHXfffchNzcXixcvxksvvWTWBjZ09uxZXLx4Ef7+/gAAjUaDyspKHDhwQKzZuXMnDAYDIiIixJrs7GzU1dWJNenp6ejXrx+8vb3FmszMTJPnSk9Ph0ajabfXQkRERLalTaHpyJEjCA8PBwBs2rQJAwcOxN69e5GWlobU1FTJ27ly5Qry8/ORn58P4MaYL/n5+SgpKcGVK1ewYMEC7Nu3D6dPn0ZmZiYmTpyI3r17IyYmBgBwxx13YNy4cXjmmWeQm5uLPXv2YM6cOZgyZQoCAgIA3Ah4SqUSM2fORGFhIT777DO8+eabJofW/vrXv2L79u1Ys2YNjh07hmXLlmH//v2YM2dOW349REREZI/acnnebbfdJhQXFwuCIAgTJkwQVq9eLQiCIPzyyy+Ci4uL5O1kZWUJAJrcZsyYIVRXVwtjx44VunbtKjg7Ows9evQQnnnmGaGsrMxkGxcvXhSmTp0qdOrUSfDw8BCefPJJ4fLlyyY1hw4dEu666y5BpVIJt99+u9jehjZt2iT07dtXUCqVQkhIiLB169ZW/U445ADZu/r6euHgwYNCRkaGcPDgQaG+vt7STSIiumWt+f5u04S9ERERuOeeezB+/HiMHTsW+/btw6BBg7Bv3z488sgjJuc7OQpO2Ev2LDs7GykpKSZXlKrVasTFxXHIASKyae0+Ye/f//53vPPOOxg9ejSmTp2KQYMGAQC++eYb8bAdEdmH7OxsJCYmIjg4GMnJydi2bRuSk5MRHByMxMREZGdnW7qJREQdotU9TYIg4MyZM/D29kZ9fb14MjUAnD59Gm5ubk3GTnIE7Gkie6TX6xEbG4vg4GCsWLECcvn//s8yGAxISEhAcXExNm7cyDGbiMgmtWtPkyAI6N27tzgwZEM9e/Z0yMBEZK8KCgpQVlaG2NhYk8AEAHK5HLGxsSgtLUVBQYGFWkhE1HFaHZrkcjn69OljMp0JEdmnS5cuAUCTUfWNjMuNdURE9qxN5zStXr0aCxYswJEjR8zdHiKyIj4+PgBuDAfSHONyYx0RkT1rU2iaPn06cnNzMWjQILi6usLHx8fkRkT2ISwsDGq1GmlpaTAYDCbrDAYD0tLS4O/vj7CwMAu1kIio47Rp7rk33njDzM0gImukUCgQFxeHxMREJCQkIDY2FkFBQSguLkZaWhq0Wi2SkpJ4EjgROYQ2jdNETfHqObJnzY3T5O/vj9mzZ3OcJiKyae0+ThMAnDx5EgkJCZg6dao4ae53332HwsLCtm6SiKxY4/+vGh+uIyKyd20KTbt370ZoaChycnLwxRdf4MqVKwCAQ4cOITEx0awNJCLLMg5u2atXL5PBLXv16sXBLYnIobQpNL344otYsWIF0tPToVQqxeX33nsv9u3bZ7bGEZFl6fV6pKSkQKPRYMWKFQgJCYGbmxtCQkKwYsUKaDQarFu3Dnq93tJNJSJqd20KTYcPH8ZDDz3UZLmvry8uXLhwy40iIuvAwS2JiP6nTaHJy8sLpaWlTZbn5eXh9ttvv+VGEZF14OCWRET/06bQNGXKFCxcuBBlZWWQyWQwGAzYs2cP5s+fj+nTp5u7jURkIRzc0nHp9Xrk5eUhMzMTeXl5PARLhDaO0/Tyyy8jPj4egYGB0Ov1GDBgAPR6PR5//HEkJCSYu41EZCENB7dsbsJeDm5pn5obYkKtViMuLo5DTJBDu6VxmkpKSnDkyBFcuXIFQ4YMQZ8+fczZNpvCcZrIXhmvntNoNDcd3JJfpPaD+5scTWu+vzm4pZkwNJE94+CWjkGv1yM2NhbBwcHN9iwmJCSguLgYGzdu5CjwZDda8/0t+fDcvHnzJDdg7dq1kmuJyPpFRUUhMjISBQUFuHTpEnx8fBAWFsYvTjtjvFpyyZIlN71aMj4+HgUFBRgyZIiFWklkOZJDU15ensnjgwcPor6+Hv369QMAnDhxAgqFAsOGDTNvC4nIKigUCn5R2jleLUnUMsmhKSsrS7y/du1auLu74+OPP4a3tzcAoKKiAk8++STuvvtu87eSiIjaXcOrJUNCQpqs59WS5OjaNOTAmjVrsGrVKjEwAYC3tzdWrFiBNWvWmK1xRETUcRpeLdl4bkFeLUnUxtCk0+nw22+/NVn+22+/4fLly7fcKCIi6ngKhQJxcXHQarVISEhAYWEhqqurUVhYiISEBGi1WsyePZvnspHDatPVc9OnT8d//vMfrFmzBuHh4QCAnJwcLFiwAHfffTc+/vhjszfU2vHqObJ3er2eJ4I7CF4tSY6k3YccqK6uxvz58/Hhhx+irq4OAODk5ISZM2fi1VdfxW233da2ltswhiayZxzs0PEwJJOjaNfQpNfrsWfPHoSGhkKpVOLkyZMAgF69ejlkWDJiaCJ7xcEOicietXtPk4uLC4qKim56WaojYmgie8TBDh0Xe5rIUbTL4JYNDRw4EKdOnWJoIrJzHOzQMfFwLFHz2nT13IoVKzB//nxs2bIFpaWl0Ol0Jjcisg8NBzusra3F5s2b8eabb2Lz5s2ora3lYId2yHg4Njg4GMnJydi2bRuSk5MRHByMxMREZGdnW7qJRBbTpsNzDf/jlMlk4n1BECCTyaDX683TOhvCw3Nkj/Ly8jB37lxER0cjKyvL5L2tUChwzz33ICMjA6+//jp7muwAD8eSI2r3w3MNRwcnIvsVFhYGNzc3ZGRkwNvbGzNnzoRGo4FWq8UHH3yAjIwMuLm5cbBDO8HDsUQta1NoGjVqlLnbQURWSK/X4/r16wCAfv36ISgoCK6urggKCkK/fv2wb98+XL9+HXq9nj0PdoBzzxG1rE2hCQAqKyvxwQcfoKioCAAQEhKCp556Cp6enmZrHBFZ1tdffw2DwYA//vGPyM3NRXx8vLjO398fEyZMwLfffouvv/4ajz76qAVbSubAueeIWtam0LR//37ExMTA1dVVHBF87dq1WLlyJXbs2IGhQ4eatZFEZBnnzp0DAMyYMQN//etfm1yCXlFRgW+//VasI9vWcO655s5p4txz5OjadPXc3Llz8cc//hGnT5/GF198gS+++ALFxcX4wx/+gOeee87MTSQiSwkICAAAaLVaKBQKDBkyBGPGjMGQIUOgUCig1WpN6si2ce45opa16eo5V1dX5OXloX///ibLjx49iuHDh6O6utpsDbQVvHqO7FFtbS3uv/9+eHh4YPPmzXBy+l/ndH19PR599FHodDp89913UCqVFmwpmRPnniNH0u5Xz3l4eKCkpKRJaDpz5gzc3d3bskkiskJKpRKPPvooPv30Uzz66KN46qmnxKvnPvzwQ1RUVGDKlCkMTHYmKioKkZGRHBGcqJE2habJkydj5syZeO211zBy5EgAwJ49e7BgwQJMnTrVrA0kIsuaNWsWAGDz5s1Ys2aNuFyhUGDKlCnierIvxsOxRPQ/bTo8V1tbiwULFmD9+vWor68HADg7O2P27NlYvXo1VCqV2Rtq7Xh4juxdbW0tvv76a5w7dw4BAQGYOHEie5iIyOa1+4S9RtXV1Th58iQAoFevXnBzczNZf/bsWQQEBDQZJM0eMTQRkT3hhL3kKNr9nCYjNzc3hIaG3nT9gAEDkJ+fj+Dg4Ft5GiKyAvwSdRycsJeoebcUmn7PLXRiEZEVyc7ORnJyMsrLy8Vlfn5+iI+P55eonTFO2KvRaLBkyRIEBQWhuLgYaWlpSExMRFJSEvc5OSz7P25GRLckOzsbS5cuRWVlpcnyyspKLF26lLPe2xG9Xo+UlBRoNBokJSWhtrYWWq0WtbW1SEpKgkajwbp16xxyUnYioJ17mojItun1eqxduxYAMHToUPzpT38Sex42btwIrVaLtWvXIjIykofq7IBxwt4JEybgT3/6U5OexT/+8Y/Yu3cvJ+wlh8WeJiK6qfz8fFRWViI0NBQrV65ESEgI3NzcEBISgpUrVyI0NBSVlZXIz8+3dFPJDIwT8b733nvN9iy+9957JnVEjqZdQ5NMJmvPzRNROzOGoSeffLLJVbByuRxPPPGESR3ZNi8vL/H+0KFDkZycjG3btiE5OdlkTtGGdUSOpF1DE08EJ7IPfC87BoPBAABwd3fH8uXLTXoWly9fLs74YKwjcjTtGpqOHj2KHj16tOdTEFE7Gjx4MAAgNTUVdXV1yMvLQ2ZmJvLy8lBXV4fU1FSTOrJtBQUFAIDLly9j6dKlJhP2Ll26FJcvXzapI3I0bToR/Pr163j77beRlZWF8+fPN/mv4+DBgwCAwMDAW28hEVnM4MGD4eXlhcOHD2P8+PGora0V1ymVStTW1sLb25uhyc488cQT2L59O+Lj48Vl/v7+mD59Ov75z39asGVEltWm0DRz5kzs2LEDjzzyCMLDw3nuEpGdUigUGDduHD799FNxyiQj4+OYmBheOWcnBg8ejA0bNuDAgQPYsGEDjhw5Ig5mOnDgQMybN0+sI3JEbZpGxdPTE9u2bUNkZGR7tMkmcRoVskd6vR6xsbHw9PREZWVlk0vQvby8oNPpsHHjRgYnO6DX6zFp0iRUVlZCo9E0O8SEt7c3Pv/8c+5vshut+f5u0zlNt99+u3hCIBHZL+O4PVFRUU16lGUyGaKiolBaWspzXOyEQqHAvHnzIJPJcPDgQcTHx+OBBx5AfHw8Dh48CJlMhrlz5zIwkcNqU2has2YNFi5ciF9++cXc7SEiK2Icj+f9999Hz549MWnSJEyYMAGTJk1Cz5498f7775vUke2LiopCUlISvL29TZb7+PhwChVyeG06p2n48OG4fv06goOD4ebmBmdnZ5P1/AAlsg/G8Xg6d+6M3Nxck4s+5HI5OnfujAsXLnDcHjsTFRWFyMhITtBM1EibQtPUqVPx66+/4uWXX4afnx9PBCeycxcuXIC3tzfuu+8+BAQE4Ny5c0hPT8eFCxcs3TRqJwqFglOlEDXSptC0d+9eaLVaDBo0yNztISIr0jAUXblyBZs2bRIfN+xhZngiIkfQpnOa+vfvj2vXrpm7LURkZYqKisT7dXV1JusaPm5YR/ZBr9ebDGaq1+st3SQii2tTT9Pq1avx/PPPixN2Nj6niZfcE9mHhiOSGAezbO4xp1mxL9nZ2UhJSUFZWZm4TK1WIy4ujieCk0NrU2gaN24cAGDMmDEmywVBgEwm438kRHbotttuw1/+8hdoNBpotVp8+OGHJiGK7EN2djYSExOh0WiwZMkScZymtLQ0JCYm8go6cmhtOjyXlZWFrKws7Ny50+RmXCZVdnY2JkyYgICAAMhkMnz11Vcm6wVBwNKlS+Hv7w9XV1dER0fjp59+Mqm5dOkSYmNj4eHhAS8vL8ycORNXrlwxqSkoKMDdd98NFxcXBAYG4pVXXmnSls2bN6N///5wcXFBaGgotm3bJv0XQmSn3NzcANy4Uk6pVGLNmjV45JFHsGbNGqhUKsjlcpM6sm16vR4pKSnQaDRYsWKFyYS9K1asgEajwbp16/iPMTmsNoWmUaNGtXiT6urVqxg0aBCSk5ObXf/KK6/grbfewvr165GTk4PbbrsNMTExuH79ulgTGxuLwsJCpKenY8uWLcjOzsazzz4rrtfpdBg7dix69OiBAwcO4NVXX8WyZcvw7rvvijV79+7F1KlTMXPmTOTl5eHBBx/Egw8+iCNHjrTht0NkP4wneBsMBly7dg29evVCYGAgevXqherqanEIAp4Ibh+Mg5nGxsaKgdhILpcjNjaWg5mSQ2vT4bns7OwW10vtur3//vtx//33N7tOEAS88cYbSEhIwMSJEwEA//znP+Hn54evvvoKU6ZMQVFREbZv344ff/wRw4cPBwC8/fbbeOCBB/Daa68hICAAaWlpqK2txYcffgilUomQkBDk5+dj7dq1Yrh68803MW7cOCxYsAAAsHz5cqSnp+Mf//gH1q9fL+m1ENkjPz8/AICTkxN0Oh10Op3JeicnJ9TX14t1ZNuMY+wFBQVBr9c3GacpKCjIpI7I0bQpNI0ePbrJsoZjNZmj67a4uBhlZWWIjo4Wl3l6eiIiIgJarRZTpkyBVquFl5eXGJgAIDo6GnK5HDk5OXjooYeg1WoRFRUFpVIp1sTExODvf/87Kioq4O3tDa1WK05E2bCm8eHChmpqalBTUyM+bvxlQmQPhgwZgo0bN4qT8/bt21ccp+nEiRPico7nYx98fHwAAF9++SW+/fbbJieCT5gwwaSOyNG06fBcRUWFye38+fPYvn07RowYgR07dpilYcY3a+P/YP38/MR1ZWVl8PX1NVnv5OQEHx8fk5rmttHwOW5W0/ADo7FVq1bB09NTvAUGBrb2JRJZvX79+on3nZ2dceLECezatQsnTpwwuWq2YR3ZrrCwMHh5eeG9995DUFAQkpOTsW3bNiQnJyMoKAjvvfcevLy8EBYWZummEllEm0JTw7Dg6emJLl264L777sPf//53vPDCC+Zuo1VatGgRqqqqxNuZM2cs3SQiszPOLQeg2XNcmqsj+yAIgklINg4rwRkgyJG16fDczfj5+eH48eNm2ZZarQYAlJeXw9/fX1xeXl6OwYMHizXnz583+bn6+npcunRJ/Hm1Wo3y8nKTGuPj36sxrm+OSqWCSqVqwysjsh1nz54FAPz1r3/Fp59+avI+8fb2xmOPPYa33npLrCPbVlBQgMrKSkRHRyMrKwv79u0T1ykUCkRHRyMjIwMFBQU8JEsOqU2hqfGVE4IgoLS0FKtXrxYDza0KCgqCWq1GZmamuE2dToecnBzMnj0bAKDRaFBZWYkDBw5g2LBhAICdO3fCYDAgIiJCrFm8eDHq6urEwwnp6eno16+fOIu3RqNBZmYmnnvuOfH509PTodFozPJaiGxVt27dsH//fvz222/45JNPmpwYbOxh6tatm4VbSuZgPME7IyMDGo0G4eHhUKlUqKmpQW5uLjIyMkzqiByO0AYymUyQy+WCTCYzuWk0GqGoqEjydi5fvizk5eUJeXl5AgBh7dq1Ql5envDLL78IgiAIq1evFry8vISvv/5aKCgoECZOnCgEBQUJ165dE7cxbtw4YciQIUJOTo7www8/CH369BGmTp0qrq+srBT8/PyEadOmCUeOHBE+/fRTwc3NTXjnnXfEmj179ghOTk7Ca6+9JhQVFQmJiYmCs7OzcPjwYcmvpaqqSgAgVFVVSf4ZW1dfXy8cPHhQyMjIEA4ePCjU19dbuklkZtXV1cKoUaOE6OhooaamxmRdTU2NEB0dLYwaNUqorq62UAvJnPbv3y+MGjVKmDNnjqDX603W6fV6Yc6cOcKoUaOE/fv3W6iFRObXmu/vNvU0FRcXmzyWy+Xo2rUrXFxcWrWd/fv345577hEfG69gmzFjBlJTU/HCCy/g6tWrePbZZ1FZWYm77roL27dvN3metLQ0zJkzB2PGjIFcLsekSZPw1ltvies9PT2xY8cOxMfHY9iwYejSpQuWLl1qMpbTyJEj8cknnyAhIQF/+9vf0KdPH3z11VcYOHBgq16PI+E0C47B1dUVkZGR2LNnD8aPH4+7774b/fr1w/Hjx/Gf//wHdXV1iIyMhKurq6WbSkTU7mSC0LZJozIzM5GZmYnz58+LA9wZffjhh2ZpnC3R6XTw9PREVVWV3c+913CahdjYWJNpFrRaLadZsEOzZs3CsWPHmizv378/xzKzI5mZmVi+fDlkMhnuvPPOJofn9u3bB0EQsGTJkibTaBHZqtZ8f7eppykpKQkvvfQShg8fDn9/f15N4UAaT7NgvILKOM1CQkIC1q1bh8jISCgUCgu3lswhOzsbx48fR3h4OK5fv46qqip4enrCxcUFP/74I7KzsxmS7YRx/KUxY8YgKysLWq1WXKdQKDBmzBhkZGRwnCZyWG0KTevXr0dqaiqmTZtm7vaQlTNOs7BkyRIIgoC8vDyTE4NjY2MRHx/Pq2vshDEk9+3bFyUlJU0Ox/bt25ch2Y4Yx2nKyMhAREQEunXrhpqaGqhUKpw9exYZGRkcp4kcWptCU21tLUaOHGnutpANMF41c+7cOSxfvrzJl+jMmTNN6si2GUNyWVlZkyE2KioqxP3PkGx/8vPzkZOTIz427n8eWSBH1qbQ9PTTT+OTTz7BkiVLzN0esnLGbvmXX34ZGo0GS5YsMTmn6eWXXzapI9vWcCLewYMHo1u3bqitrYVSqcTZs2fFL1VO2GsfjOM0ATCZJqrh44qKCoZkclhtCk3Xr1/Hu+++i4yMDISFhZlMpwAAa9euNUvjyPqEhIRAoVDAw8MDL730EpycnMTlL730Eh599FHodDqEhIRYuKVkDsYeQ09PT/z4448mPQ9yuVw8eZI9i/ahYfh1dnZGXV1ds48ZkslRtXlwS+OAk0eOHDFZx65b+1ZYWAi9Xo+KigosXbq0ydVzFRUVYh3/E7V9xomojSd/9+zZE4IgQCaT4fTp06iqqjKpI9vWMPw2/ixv+JghmRxVm0JTVlaWudtBNsL4Ybl48WJ88MEHiI+PF9f5+/tj8eLFWLlyJT9U7UTDEUmqqqpw6NCh360j22UMwQAwdOhQTJs2TfynaMOGDeK0Kg3riByJWeeeI/tnPFcpICAAaWlpTabVMI7lw3Oa7MPVq1fNWkfWreFcni31NDWe85PIUTA0UauEhYVBrVYjLS0NK1asMDkEZzAYkJaWBn9/f16SbCf0er1Z68g2+Pr64tSpUyY9yWq1Gr6+vgxM5NDklm4A2RaFQoG4uDhotVokJCSgsLAQ1dXVKCwsREJCArRaLWbPns0xe+zE6dOnzVpH1s3Pzw/AjZ4k4/mJRpcuXRIDk7GOyNGwp4laLSoqCklJSUhJSWlyThOnULEvja+MvdU6sm5Dhw5FWloaAKC+vt5kXcPHQ4cO7dB2EVkLhiZqk6ioKERGRjY5p4k9TPaltrZWvK9QKBAWFobOnTvj4sWLKCgoEA/LNawj2xUaGgq5XA6DwQAnJyeT/Wp8LJfLERoaasFWElkOQxO1mUKh4LACdq7hZNx6vR55eXm/W0e2q7CwUNyXja+IND42GAwcUoQcFs9pIqKbun79unhfJpOhW7du6N+/P7p162ZyNVXDOrJdxqFCJk2a1OzhuUmTJpnUETka9jQR0U0Zx+gBbvQ0nD179qZ1ZPuMQ4X8+9//brJOEARxOYcUIUfF0EREN9WnTx/s3LlTfBwQECCe83Lu3DmTOrJ9ISEhkMlkEAQB3t7euO+++3D77bfj119/RXp6OioqKiCTyThNEjkshiYiuqnGPQoNg1JLdWSbDh06JJ67dPnyZWzatElcZ5xnUhAEHDp0CCNGjLBIG4ksiec0EdFNSZ1TjnPP2YcdO3aI91sacqBhHZEjYWgiopvy8PAA8L9ehsaMy411ZNuqq6vF+y1No9KwjsiRMDQR0U0Ze5Aa9zoYGZezp8k+eHt7i/dvNuRA4zoiR8LQREQ31alTJ7PWkXXjBM1ELWNoojYzDnaYmZmJvLw8Ttpqh4qKisxaR9ZN6mS8nLSXHBWvnqM2yc7ORkpKCsrKysRlarUacXFxnHvOjvz2229mrSPrVlNTI943Di1hpFAoxH+MGtYRORL2NFGrZWdnIzExEcHBwUhOTsa2bduQnJyM4OBgJCYmIjs729JNJDO5cOGCeN/JyQn33nsv4uLicO+995qcHN6wjmxXXV2deL/xieA3qyNyJOxpolbR6/VISUmBRqPBihUrIJffyN0hISFYsWIFEhISsG7dOkRGRnLyXjvQsEfBx8cHO3fuFAe79PPzQ3l5eZM6sl3u7u7i/caH2xs+blhH5EjY00StUlBQgLKyMsTGxoqByUgulyM2NhalpaUoKCiwUAvJnBrOct/4EFzD81oa1pHtGjlypFnriOwNQxO1inGizpvNNWZczgk97UPnzp3F+y1dgt6wjmxXw+lwWhqnidPmkKNiaKJWMU6XYZzEtTHjck6rYR8iIyPNWkfWreF4Wy2FZI7LRY6KoYlaJSwsDGq1GmlpaSZX1gCAwWBAWloa/P39ERYWZqEWkjlJ7VFgz4N9kPrPDv8pIkfFE8GpVRQKBeLi4pCYmIjFixcjPDwcKpUKNTU1yM3Nxb59+5CUlMSTwO2E1MOsPBxrH0JCQpoMNdCYXC5HSEhIB7aKyHowNFGrRUVFYfLkydi8eTO0Wq24XKFQYPLkyRynyY4cPnxYcl1MTEw7t4ba2+HDh8XAJJPJcN999+Gxxx7Dpk2bkJ6eDkEQYDAYcPjwYQwbNszCrSXqeAxN1GrZ2dn47LPPcOeddyI8PBwuLi64fv06cnNz8dlnn2HAgAEMTnbi0KFDZq0j65abmwvgRm9S586dsWPHDuzYsQMA4OvriwsXLsBgMCA3N5ehiRwSQxO1ys3GaQKAiRMncpwmO8NpNRxLXl4eAGD06NF48cUX8fXXX+PcuXMICAjAxIkTsWrVKmRlZYl1RI6GoYlaxThO05IlS246TlN8fDwKCgowZMgQC7WSzKXxFVS3Wke24ejRo5g2bZo4eCkAfP755y2OEk7kCBiaqFU4ThOR/erfvz9OnDhhMqekUcMA1b9//45sFpHV4JAD1CoNx2nS6/XIy8tDZmYm8vLyoNfrOU6TnZHas8AeCPvw7LPPmrWOyN6wp4laxThO01tvvYWKigqTc1l8fX3h7e3NcZrsSONDsLdaR9atqKhIct2IESPauTVE1oefdNQqCoUCo0ePxvHjx5uc/Hv+/HkcP34co0aN4kngdoKhybEYr5QzVx2RveEnHbWKXq/HN998A6DpF6Xx8bfffttkhnSyTTU1NWatI+t29epV8b5cLkdAQAC6deuGgIAAk/d7wzoiR8LDc9QqBw8eRHV1Ndzd3fH555/j6NGjuHTpEnx8fDBgwAA88sgjuHz5Mg4ePMjueztQX19v1jqybg1HAvfx8cG5c+fEx126dMGFCxea1BE5EvY0UasYu+WffPJJqFQqDBkyBGPGjMGQIUOgUqnwxBNPmNQRke1oeNXcxYsXTdY1fNzc1XVEjoChiVrl2rVrAAB/f/9m16vVapM6IrIdDXsMG4+91fAxexbJUTE0UauEhoYCAN5//33U1dWZDDlQV1eHDz74wKSOiGxHt27dzFpHZG94ThO1ykMPPYR33nkHJ0+exPjx41FbWyuuUyqVqK2thVwux0MPPWTBVpK5yGQySaN9c5wm+9CzZ0/s27dPUh2RI2JPE7WKUqmERqMBAJPA1PCxRqOBUqns8LaR+bm6upq1jqwbh5ggahn/8qlV9Ho9Tp48CW9v72bXe3t749SpUxxywE7w6jnHotPpzFpHZG94eI5axThhr0wmw5133onbb78dtbW1UCqV+PXXX5GTkwNBEDhhr52oq6szax1Zt9OnT5u1jsjeMDRRqxjHaQkPD8fy5ctx5MgRcZymgQMHIiEhATk5OWId2TYp5zO1po6sm9QeYvYkk6NiaKJWqaysBAD4+flh2rRpJuO1qNVqhIeHm9SRbXNycpJ06M3JiR8l9kDqSN8cEZwcFT/pqFW8vLwAAN988w1UKpXJuoqKCnGKFWMd2TapV8Xx6jn7cP36dbPWEdkbnghOreLj4yPev9nVc43ryHYxNDmWK1eumLWOyN4wNFGrNJxzqvEhmYaPOTeVfeDVc45F6mFWHo4lR8W/fGqV/Px88f6wYcMQEREBlUqFmpoa5OTkiAPj5efnc8JeOyA1/DIk2wep46txHDZyVAxN1Crnz58HADzwwAM4ePCgyejB/v7+uP/++/Hdd9+JdURkO7p06SLpytcuXbp0QGuIrA8Pz1Gr+Pr6AgAOHz7cpHdBr9fj8OHDJnVEZDs4LhdRyxiaqFWGDh0KADhz5gzq6urw/PPP4/PPP8fzzz+Puro6nD171qSOiGyH1KFCOKQIOSoenqNWCQ0NhVwuh8FgQHV1NdasWSOuMw5BIJfLERoaaqkmElEbMTQRtYw9TdQqhYWF4mG5xqNAGx8bDAYUFhZ2eNuI6NbwxH+illl9aFq2bBlkMpnJrX///uL669evIz4+Hp07d0anTp0wadIklJeXm2yjpKQE48ePh5ubG3x9fbFgwYIml0jv2rULQ4cOhUqlQu/evZGamtoRL8/mXLp0CQCwePHiJpP2+vj4YPHixSZ1ZNs4TpNjkculfSVIrSOyNzZxeC4kJAQZGRni44ZjhMydOxdbt27F5s2b4enpiTlz5uDhhx/Gnj17ANw4OXn8+PFQq9XYu3cvSktLMX36dDg7O+Pll18GABQXF2P8+PGYNWsW0tLSkJmZiaeffhr+/v6IiYnp2Bdr5YyDVgYEBOCTTz5BQUGBOPdcWFgYjh07ZlJHto1zzzkWZ2dnSfPKOTs7d0BriKyPTYQmJycnqNXqJsurqqrwwQcf4JNPPsG9994LAPjoo49wxx13YN++fbjzzjuxY8cOHD16FBkZGfDz88PgwYOxfPlyLFy4EMuWLYNSqcT69esRFBQknp9zxx134IcffsDrr7/O0NRIWFgY1Go10tLSsGLFCgwZMkRcZzAYkJaWBn9/f4SFhVmwlUTUFrx6jqhlNtHH+tNPPyEgIADBwcGIjY1FSUkJAODAgQOoq6tDdHS0WNu/f390794dWq0WAKDVahEaGgo/Pz+xJiYmBjqdTjzvRqvVmmzDWGPcRnNqamqg0+lMbo5AoVAgLi4OWq0WCQkJKCwsRHV1NQoLC5GQkACtVovZs2dDoVBYuqlE1EpSeplaU0dkb6y+pykiIgKpqano168fSktLkZSUhLvvvhtHjhxBWVkZlEplk8lh/fz8UFZWBgAoKyszCUzG9cZ1LdXodDpcu3YNrq6uTdq1atUqJCUlmetl2pSoqCgkJSUhJSUF8fHx4nJ/f38kJSUhKirKgq0jIiJqH1Yfmu6//37xflhYGCIiItCjRw9s2rSp2TDTURYtWoR58+aJj3U6HQIDAy3Wno4WFRWFyMjIJuc0sYeJiIjslU0cnmvIy8sLffv2xc8//wy1Wo3a2tomY4aUl5eL50Cp1eomV9MZH/9ejYeHx02DmUqlgoeHh8mNiIiI7JfV9zQ1duXKFZw8eRLTpk3DsGHD4OzsjMzMTEyaNAkAcPz4cZSUlECj0QAANBoNVq5cifPnz4tTe6Snp8PDwwMDBgwQa7Zt22byPOnp6eI2qKns7GykpKSIhziBG+EzLi6Oh+eIiMguWX1P0/z587F7926cPn0ae/fuxUMPPQSFQoGpU6fC09MTM2fOxLx585CVlYUDBw7gySefhEajwZ133gkAGDt2LAYMGIBp06bh0KFD+P7775GQkID4+HhxBOtZs2bh1KlTeOGFF3Ds2DGkpKRg06ZNmDt3riVfutXKzs5GYmIigoODkZycjG3btiE5ORnBwcFITExEdna2pZtIRERkdlbf03T27FlMnToVFy9eRNeuXXHXXXdh37596Nq1KwDg9ddfh1wux6RJk1BTU4OYmBikpKSIP69QKLBlyxbMnj0bGo0Gt912G2bMmIGXXnpJrAkKCsLWrVsxd+5cvPnmm+jWrRvef/99DjfQDL1ej5SUFGg0Grz44ov4+9//jnPnziEgIAAvvvgiVq9ejXXr1iEyMpLnNxERkV2RCRyVzix0Oh08PT1RVVVl1+c35eXlYe7cuejRowd++eWXJuuNy19//XWTMZzINo0ePVpy7a5du9qtHdQxuL/JEbXm+9vqe5rIuhinR/nll18gk8kwbNgwDBkyBHl5eThw4IAYpDiNChER2RuGJmqVhlcTdu7cGfv378f+/fsBAF26dMGFCxea1BEREdkDqz8RnKzLli1bxPvGgNTc44Z1RERE9oChiVqltLTUrHVERES2gqGJWkXqYTceniMiInvD0EStYhzqwVx1REREtoKhiVqluWEGbqWOiIjIVjA0UatIHdaLw38REZG9YWiiVqmtrTVrHRERka1gaKJW0el0Zq0jIiKyFQxN1Co1NTVmrSMiIrIVDE3UKjKZzKx1REREtoKhiVpFqVSatY6IiMhWMDRRq3BwSyIiclQMTdQq1dXVZq0jIiKyFQxNRERERBIwNFGrXLt2zax1REREtoKhiVqFI4ITEZGjYmgiIiIikoChiYiIiEgChiYiIiIiCZws3QAiIiKyLL1ej4KCAly6dAk+Pj4ICwuDQqGwdLOsDkMTERGRA8vOzkZKSgrKysrEZWq1GnFxcYiKirJgy6wPQxMREZGDys7ORmJiIiIiIhAZGYmamhqoVCr8+uuvSExMRFJSEoNTAwxNREREDkiv1yMlJQX+/v7Izc2FwWAQ18nlcvj7+2PdunWIjIzkobr/YmgiIiJyQAUFBSaH5BoyGAw4d+6cWDdkyJCObJrV4tVzREREDuj8+fNmrXMEDE1EREQO6MiRI2atcwQMTURERA7o1KlTZq1zBAxNREREDujKlStmrXMEDE1EREQO6Pr162atcwQMTURERA6ooqLCrHWOgKGJiIjIAdXW1pq1zhEwNBERERFJwNBEREREJAFDExEREZEEDE1EREREEjA0EREREUnA0EREREQkAUMTERERkQQMTUREREQSMDQRERERScDQRERERCQBQxMRERGRBAxNRERERBIwNBERERFJwNBEREREJAFDExEREZEETpZuABERtd7169dRUlJisec/ceKEWbfXvXt3uLi4mHWbRObG0EREZINKSkrw7LPPWuz5zf3c7777Lvr27WvWbRKZG0MTEZEN6t69O959912zbrM1Qcjcz929e3ezbo+oPTA02TF23xPZLxcXF4v2zLBXiBwRQ5MdY/c9EbXGrl27MHr0aEl1RI6IocmOsfvesbBnkczh94ITAxM5MoYmO8bue8fCnkUyl5sFJwYmcnQyQRAESzfCHuh0Onh6eqKqqgoeHh6Wbk67Yve9dWqPniZL9yzaek9TeXk5qqqqLN2MNvvll1+wcuVKLF68GD169LB0c26Jp6cn/Pz8LN2MNuP7u/205vuboclM2hqabPVDtaU3m7nfXB3F1j9U24OUgGzEoGyqvLwcf5o2HXW1NZZuCgFwVqqwccM/bfY9fuLECYv2JJubNfUkt+b7m4fnLMheP1Rt9Y1t6x+q7YEnBrddVVUV6mprcC14FAwunpZujkOTX68CTu1GVVWVzb6/eY6qdWBoaiQ5ORmvvvoqysrKMGjQILz99tsIDw9vl+fih6r16KgPVVvsWXz33Xd/t2fR3CeBd4QO61lkZ77lddA+sMX3t6W090Ur7fX+Zmhq4LPPPsO8efOwfv16RERE4I033kBMTAyOHz8OX1/f9ntifqhaXgfsA/YsWpeO6ll0Lc5u1+2TdbDX97eRrb3P2+v9zdDUwNq1a/HMM8/gySefBACsX78eW7duxYcffogXX3yx3Z6XH6qOwdizeP32oRCUnSzdHIcmq70C/HqwQw7XcH9bnqz2Clx+Pdiuz2F8f9d26QPB2a1dn8vcnEoPQQ5A1mCZAMAAoN5/kGUadQtkddXAhZ/a5f3N0PRftbW1OHDgABYtWiQuk8vliI6OhlarbVJfU1ODmpr//Ueh0+la/Zyenp5wclaivq62bY0ms3JyVsLTs/0Pk7b3hzdZB09PTzgrVQD3t1VwVqra9f3t6ekJuVwB5YWf2u05OpIMgAKAovSQpZvSJnK5ol32N0PTf124cAF6vb5JKvXz88OxY8ea1K9atQpJSUm39Jx+fn5I27ih3Y6BGy8Xthftfdlze5/jwpBsXdo7JPv5+WHjhn/y/S2Rrb+//fz8kJKSjDNnzrTL9ktLS/Hhhx+2y7Yt4amnnoK/v3+7bT8wMLBd9jeHHPivc+fO4fbbb8fevXuh0WjE5S+88AJ2796NnJwck/rmepoCAwOtapwmS48QbW7WNK5HW7XniaI1NTUoKytrl21bglqthkqlarft2/oQE3x/Oxbu7/bDIQfaoEuXLlAoFCgvLzdZXl5eDrVa3aRepVK16we6OVh6RHBqys/Pr12/qENDQ9tt22Rd+P52LNzf1kFu6QZYC6VSiWHDhiEzM1NcZjAYkJmZadLzRERERI6JPU0NzJs3DzNmzMDw4cMRHh6ON954A1evXhWvpiMiIiLHxdDUwOTJk/Hbb79h6dKlKCsrw+DBg7F9+3abPu+BiIiIzIMngpuJI03YS0REZC9a8/3Nc5qIiIiIJGBoIiIiIpKAoYmIiIhIAoYmIiIiIgkYmoiIiIgkYGgiIiIikoChiYiIiEgChiYiIiIiCRiaiIiIiCTgNCpmYhxYXafTWbglREREJJXxe1vKBCkMTWZy+fJlAEBgYKCFW0JEREStdfnyZXh6erZYw7nnzMRgMODcuXNwd3eHTCazdHM6jE6nQ2BgIM6cOcM59xwA97dj4f52LI66vwVBwOXLlxEQEAC5vOWzltjTZCZyuRzdunWzdDMsxsPDw6HeZI6O+9uxcH87Fkfc37/Xw2TEE8GJiIiIJGBoIiIiIpKAoYluiUqlQmJiIlQqlaWbQh2A+9uxcH87Fu7v38cTwYmIiIgkYE8TERERkQQMTUREREQSMDQRERERScDQRJLt2rULMpkMlZWVt7Sdnj174o033jBLm6hjyGQyfPXVVwCA06dPQyaTIT8/36JtovbzxBNP4MEHHxQfjx49Gs8995zF2kPmZa7P8o5iTe1laCIAN74UW7otW7bMbM/1448/4tlnnzXb9qhljb8Ab1VgYCBKS0sxcOBAs22TzKfh/jZX2Pniiy+wfPnyW94OmSorK8Nf/vIXBAcHQ6VSITAwEBMmTEBmZqbZnqO5v4GRI0eitLRU8oCO5pKamgovL68OfU5z44jgBAAoLS0V73/22WdYunQpjh8/Li7r1KkT9u/fb5bn6tq1q1m2Q5ahUCigVqst3QzqQD4+PpZugt05ffo0IiMj4eXlhVdffRWhoaGoq6vD999/j/j4eBw7dqzdnlupVPI93EbsaSIAgFqtFm+enp6QyWQmyzp16iTWHjhwAMOHD4ebmxtGjhxpEq5OnjyJiRMnws/PD506dcKIESOQkZFh8lw8PGc5o0ePxv/93//hhRdegI+PD9RqdZNexJ9++glRUVFwcXHBgAEDkJ6ebrK+8eE5vV6PmTNnIigoCK6urujXrx/efPNNk58x9n689tpr8Pf3R+fOnREfH4+6urr2fLkO7YknnsDu3bvx5ptvij3Gp0+flrS/GmvcW7FhwwYMHz4c7u7uUKvVePzxx3H+/HlxvfFwSmZm5k0/KxxdXFwcZDIZcnNzMWnSJPTt2xchISGYN28e9u3bBwAoKSnBxIkT0alTJ3h4eOCxxx5DeXm5uI1ly5Zh8ODB2LBhA3r27AlPT09MmTJFnED+Zn8DjQ93GXuAvv/+e9xxxx3o1KkTxo0bZ/LPNAC8//77uOOOO+Di4oL+/fsjJSVFXGf8XPjiiy9wzz33wM3NDYMGDYJWqwVw42/iySefRFVVVZMjGL/392RNGJqo1RYvXow1a9Zg//79cHJywlNPPSWuu3LlCh544AFkZmYiLy8P48aNw4QJE1BSUmLBFlNDH3/8MW677Tbk5OTglVdewUsvvSQGI4PBgIcffhhKpRI5OTlYv349Fi5c2OL2DAYDunXrhs2bN+Po0aNYunQp/va3v2HTpk0mdVlZWTh58iSysrLw8ccfIzU1Fampqe31Mh3em2++CY1Gg2eeeQalpaUoLS1FYGCg5P3Vkrq6OixfvhyHDh3CV199hdOnT+OJJ55oUtfSZ4Uju3TpErZv3474+HjcdtttTdZ7eXnBYDBg4sSJuHTpEnbv3o309HScOnUKkydPNqk9efIkvvrqK2zZsgVbtmzB7t27sXr1agA3/xtoTnV1NV577TVs2LAB2dnZKCkpwfz588X1aWlpWLp0KVauXImioiK8/PLLWLJkCT7++GOT7SxevBjz589Hfn4++vbti6lTp6K+vh4jR47EG2+8AQ8PD7Etxu1L/XuyCgJRIx999JHg6enZZHlWVpYAQMjIyBCXbd26VQAgXLt27abbCwkJEd5++23xcY8ePYTXX3/dnE2mFsyYMUOYOHGiIAiCMGrUKOGuu+4yWT9ixAhh4cKFgiAIwvfffy84OTkJv/76q7j+u+++EwAIX375pSAIglBcXCwAEPLy8m76nPHx8cKkSZNM2tCjRw+hvr5eXPboo48KkydPvsVXR4013t9//etff/dnmttfxm1I2c6PP/4oABAuX74sCELbPyscRU5OjgBA+OKLL25as2PHDkGhUAglJSXissLCQgGAkJubKwiCICQmJgpubm6CTqcTaxYsWCBERESIj5vbd8b9U1FRIQjCjc98AMLPP/8s1iQnJwt+fn7i4169egmffPKJyXaWL18uaDQaQRD+97nw/vvvN2lvUVGR+DzNfbc0drO/J2N7LYk9TdRqYWFh4n1/f38AELtSr1y5gvnz5+OOO+6Al5cXOnXqhKKiIvY0WZGG+w+4sQ+N+6+oqAiBgYEICAgQ12s0mt/dZnJyMoYNG4auXbuiU6dOePfdd5vs85CQECgUimaflzqWlP3VkgMHDmDChAno3r073N3dMWrUKABoso2WPiscmSBhIg7je7Fhz9CAAQPg5eWFoqIicVnPnj3h7u4uPm7r+8rNzQ29evVqdjtXr17FyZMnMXPmTHTq1Em8rVixAidPnjTZTlv2udS/J2vAE8Gp1ZydncX7MpkMwI1DNAAwf/58pKen47XXXkPv3r3h6uqKRx55BLW1tRZpKzXVcP8BN/ahcf+1xaeffor58+djzZo10Gg0cHd3x6uvvoqcnJx2fV5qG6n762auXr2KmJgYxMTEIC0tDV27dkVJSQliYmKavM9b+qxwZH369IFMJjPLyd7mel81tx1juLty5QoA4L333kNERIRJXcN/hBpvR8o+b83fkzVgaCKz2rNnD5544gk89NBDAG682U6fPm3ZRpFkd9xxB86cOYPS0lLxv0TjSak3s2fPHowcORJxcXHissb/fZJlKJVK6PV6k2W3ur+OHTuGixcvYvXq1WIviLmurHUUPj4+iImJQXJyMv7v//6vyXlNlZWV4nvxzJkz4u/56NGjqKysxIABAyQ/V3N/A63l5+eHgIAAnDp1CrGxsW3eTnNtsbW/Jx6eI7Pq06cPvvjiC+Tn5+PQoUN4/PHH+Z+lDYmOjkbfvn0xY8YMHDp0CP/5z3+wePHiFn+mT58+2L9/P77//nucOHECS5YswY8//thBLaaW9OzZEzk5OTh9+jQuXLgAg8Fwy/ure/fuUCqVePvtt3Hq1Cl88803HMOpDZKTk6HX6xEeHo5///vf+Omnn1BUVIS33noLGo0G0dHRCA0NRWxsLA4ePIjc3FxMnz4do0aNwvDhwyU/T3N/A22RlJSEVatW4a233sKJEydw+PBhfPTRR1i7dm2r2nLlyhVkZmbiwoULqK6utrm/J4YmMqu1a9fC29sbI0eOxIQJExATE4OhQ4daulkkkVwux5dffolr164hPDwcTz/9NFauXNniz/z5z3/Gww8/jMmTJyMiIgIXL1406cUgy5k/fz4UCgUGDBggHva41f3VtWtXpKamYvPmzRgwYABWr16N1157rR1fhX0KDg7GwYMHcc899+D555/HwIEDcd999yEzMxPr1q2DTCbD119/DW9vb0RFRSE6OhrBwcH47LPPWvU8zf0NtMXTTz+N999/Hx999BFCQ0MxatQopKamIigoSPI2Ro4ciVmzZmHy5Mno2rUrXnnlFZv7e5IJUs5IIyIiInJw7GkiIiIikoChiYiIiEgChiYiIiIiCRiaiIiIiCRgaCIiIiKSgKGJiIiISAKGJiIiIiIJGJqIiIiIJGBoIiL6r9TUVHh5eZm9lojsA0cEJyL6r2vXruHy5cvw9fU1ay0R2QeGJiIiIiIJeHiOiOyKwWDAK6+8gt69e0OlUqF79+5YuXIldu3aBZlMhsrKSrE2Pz8fMpkMp0+fBtD0kNuhQ4dwzz33wN3dHR4eHhg2bBj279/fbO2yZcswePBgbNiwAT179oSnpyemTJmCy5cvm7Rt1apVCAoKgqurKwYNGoTPP/+8PX8dRGRGTpZuABGROS1atAjvvfceXn/9ddx1110oLS3FsWPH2rSt2NhYDBkyBOvWrYNCoUB+fj6cnZ1vWn/y5El89dVX2LJlCyoqKvDYY49h9erVWLlyJQBg1apV2LhxI9avX48+ffogOzsbf/rTn9C1a1eMGjWqTW0koo7D0EREduPy5ct488038Y9//AMzZswAAPTq1Qt33XUXdu3a1ertlZSUYMGCBejfvz8AoE+fPi3WGwwGpKamwt3dHQAwbdo0ZGZmYuXKlaipqcHLL7+MjIwMaDQaAEBwcDB++OEHvPPOOwxNRDaAoYmI7EZRURFqamowZswYs2xv3rx5ePrpp7FhwwZER0fj0UcfRa9evW5a37NnTzEwAYC/vz/Onz8PAPj5559RXV2N++67z+RnamtrMWTIELO0l4jaF0MTEdkNV1fXm66Ty2+cwtnw2pe6uroWt7ds2TI8/vjj2Lp1K7777jskJibi008/xUMPPdRsfeNDdzKZDAaDAQBw5coVAMDWrVtx++23m9SpVKoW20FE1oEnghOR3ejTpw9cXV2RmZnZZF3Xrl0BAKWlpeKy/Pz8391m3759MXfuXOzYsQMPP/wwPvrooza1bcCAAVCpVCgpKUHv3r1NboGBgW3aJhF1LPY0EZHdcHFxwcKFC/HCCy9AqVQiMjISv/32GwoLCzF9+nQEBgZi2bJlWLlyJU6cOIE1a9bcdFvXrl3DggUL8MgjjyAoKAhnz57Fjz/+iEmTJrWpbe7u7pg/fz7mzp0Lg8GAu+66C1VVVdizZw88PDzEc7CIyHoxNBGRXVmyZAmcnJywdOlSnDt3Dv7+/pg1axacnZ3xr3/9C7Nnz0ZYWBhGjBiBFStW4NFHH212OwqFAhcvXsT06dNRXl6OLl264OGHH0ZSUlKb27Z8+XJ07doVq1atwqlTp+Dl5YWhQ4fib3/7W5u3SUQdh4NbEhEREUnAc5qIiIiIJGBoIiIiIpKAoYmIiIhIAoYmIiIiIgkYmoiIiIgkYGgiIiIikoChiYiIiEgChiYiIiIiCRiaiIiIiCRgaCIiIiKSgKGJiIiISIL/B0+Qf9TG7417AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Cuisine\n",
        "combined.category.value_counts().plot(kind = \"bar\")\n",
        "\n",
        "# Top 5: Beverages, Ricebowl, Sandwich, Pizza, Starters"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "N8Kapsr_Lvrc",
        "outputId": "364b95c3-b24d-416a-880c-26beb8e71446"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='category'>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x = \"category\", y = \"num_orders\", data = combined)\n",
        "plt.xticks(rotation = 90)\n",
        "plt.show()\n",
        "\n",
        "# Salad, Beverages, Ricebowl, Sandwich, Other Snacks"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "Pk5fEqcxL8Ul",
        "outputId": "3b6a0618-50f5-45ce-c9ac-d95d0f2b0737"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "combined.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "eIO4thm1M4vc",
        "outputId": "00507c62-912f-4bdd-e96c-35d906389009"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        id  week  center_id  meal_id  checkout_price  base_price  \\\n",
              "0  1379560     1         55     1885          136.83      152.29   \n",
              "1  1466964     1         55     1993          136.83      135.83   \n",
              "2  1346989     1         55     2539          134.86      135.86   \n",
              "3  1338232     1         55     2139          339.50      437.53   \n",
              "4  1448490     1         55     2631          243.50      242.50   \n",
              "\n",
              "   emailer_for_promotion  homepage_featured  num_orders  city_code  \\\n",
              "0                      0                  0       177.0        647   \n",
              "1                      0                  0       270.0        647   \n",
              "2                      0                  0       189.0        647   \n",
              "3                      0                  0        54.0        647   \n",
              "4                      0                  0        40.0        647   \n",
              "\n",
              "   region_code center_type  op_area   category cuisine  log_target  \\\n",
              "0           56      TYPE_C      2.0  Beverages    Thai    5.176150   \n",
              "1           56      TYPE_C      2.0  Beverages    Thai    5.598422   \n",
              "2           56      TYPE_C      2.0  Beverages    Thai    5.241747   \n",
              "3           56      TYPE_C      2.0  Beverages  Indian    3.988984   \n",
              "4           56      TYPE_C      2.0  Beverages  Indian    3.688879   \n",
              "\n",
              "   price_diff   price_cat  marketing  \n",
              "0       15.46  Addnl_Chgs          0  \n",
              "1       -1.00    Discount          0  \n",
              "2        1.00       Taxes          0  \n",
              "3       98.03  Addnl_Chgs          0  \n",
              "4       -1.00    Discount          0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c2a1e6aa-caea-4456-8831-747e96f63065\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>week</th>\n",
              "      <th>center_id</th>\n",
              "      <th>meal_id</th>\n",
              "      <th>checkout_price</th>\n",
              "      <th>base_price</th>\n",
              "      <th>emailer_for_promotion</th>\n",
              "      <th>homepage_featured</th>\n",
              "      <th>num_orders</th>\n",
              "      <th>city_code</th>\n",
              "      <th>region_code</th>\n",
              "      <th>center_type</th>\n",
              "      <th>op_area</th>\n",
              "      <th>category</th>\n",
              "      <th>cuisine</th>\n",
              "      <th>log_target</th>\n",
              "      <th>price_diff</th>\n",
              "      <th>price_cat</th>\n",
              "      <th>marketing</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1379560</td>\n",
              "      <td>1</td>\n",
              "      <td>55</td>\n",
              "      <td>1885</td>\n",
              "      <td>136.83</td>\n",
              "      <td>152.29</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>177.0</td>\n",
              "      <td>647</td>\n",
              "      <td>56</td>\n",
              "      <td>TYPE_C</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Beverages</td>\n",
              "      <td>Thai</td>\n",
              "      <td>5.176150</td>\n",
              "      <td>15.46</td>\n",
              "      <td>Addnl_Chgs</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1466964</td>\n",
              "      <td>1</td>\n",
              "      <td>55</td>\n",
              "      <td>1993</td>\n",
              "      <td>136.83</td>\n",
              "      <td>135.83</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>270.0</td>\n",
              "      <td>647</td>\n",
              "      <td>56</td>\n",
              "      <td>TYPE_C</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Beverages</td>\n",
              "      <td>Thai</td>\n",
              "      <td>5.598422</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>Discount</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1346989</td>\n",
              "      <td>1</td>\n",
              "      <td>55</td>\n",
              "      <td>2539</td>\n",
              "      <td>134.86</td>\n",
              "      <td>135.86</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>189.0</td>\n",
              "      <td>647</td>\n",
              "      <td>56</td>\n",
              "      <td>TYPE_C</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Beverages</td>\n",
              "      <td>Thai</td>\n",
              "      <td>5.241747</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Taxes</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1338232</td>\n",
              "      <td>1</td>\n",
              "      <td>55</td>\n",
              "      <td>2139</td>\n",
              "      <td>339.50</td>\n",
              "      <td>437.53</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>647</td>\n",
              "      <td>56</td>\n",
              "      <td>TYPE_C</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Beverages</td>\n",
              "      <td>Indian</td>\n",
              "      <td>3.988984</td>\n",
              "      <td>98.03</td>\n",
              "      <td>Addnl_Chgs</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1448490</td>\n",
              "      <td>1</td>\n",
              "      <td>55</td>\n",
              "      <td>2631</td>\n",
              "      <td>243.50</td>\n",
              "      <td>242.50</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>647</td>\n",
              "      <td>56</td>\n",
              "      <td>TYPE_C</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Beverages</td>\n",
              "      <td>Indian</td>\n",
              "      <td>3.688879</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>Discount</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c2a1e6aa-caea-4456-8831-747e96f63065')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-c2a1e6aa-caea-4456-8831-747e96f63065 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-c2a1e6aa-caea-4456-8831-747e96f63065');\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",
              "\n",
              "\n",
              "<div id=\"df-f0b4aab7-628f-4e81-90b8-6a2049484589\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f0b4aab7-628f-4e81-90b8-6a2049484589')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f0b4aab7-628f-4e81-90b8-6a2049484589 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "combined"
            }
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "combined.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "TZZOjZhPOQhM",
        "outputId": "6e1517d3-75a1-42c6-adc0-f3dc963feb3d"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        id  week  center_id  meal_id  checkout_price  base_price  \\\n",
              "0  1379560     1         55     1885          136.83      152.29   \n",
              "1  1466964     1         55     1993          136.83      135.83   \n",
              "2  1346989     1         55     2539          134.86      135.86   \n",
              "3  1338232     1         55     2139          339.50      437.53   \n",
              "4  1448490     1         55     2631          243.50      242.50   \n",
              "\n",
              "   emailer_for_promotion  homepage_featured  num_orders  city_code  \\\n",
              "0                      0                  0       177.0        647   \n",
              "1                      0                  0       270.0        647   \n",
              "2                      0                  0       189.0        647   \n",
              "3                      0                  0        54.0        647   \n",
              "4                      0                  0        40.0        647   \n",
              "\n",
              "   region_code center_type  op_area   category cuisine  log_target  \\\n",
              "0           56      TYPE_C      2.0  Beverages    Thai    5.176150   \n",
              "1           56      TYPE_C      2.0  Beverages    Thai    5.598422   \n",
              "2           56      TYPE_C      2.0  Beverages    Thai    5.241747   \n",
              "3           56      TYPE_C      2.0  Beverages  Indian    3.988984   \n",
              "4           56      TYPE_C      2.0  Beverages  Indian    3.688879   \n",
              "\n",
              "   price_diff   price_cat  marketing     radii  \n",
              "0       15.46  Addnl_Chgs          0  0.797885  \n",
              "1       -1.00    Discount          0  0.797885  \n",
              "2        1.00       Taxes          0  0.797885  \n",
              "3       98.03  Addnl_Chgs          0  0.797885  \n",
              "4       -1.00    Discount          0  0.797885  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4e338f47-51e4-4b89-a026-7f23016f87ca\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>week</th>\n",
              "      <th>center_id</th>\n",
              "      <th>meal_id</th>\n",
              "      <th>checkout_price</th>\n",
              "      <th>base_price</th>\n",
              "      <th>emailer_for_promotion</th>\n",
              "      <th>homepage_featured</th>\n",
              "      <th>num_orders</th>\n",
              "      <th>city_code</th>\n",
              "      <th>region_code</th>\n",
              "      <th>center_type</th>\n",
              "      <th>op_area</th>\n",
              "      <th>category</th>\n",
              "      <th>cuisine</th>\n",
              "      <th>log_target</th>\n",
              "      <th>price_diff</th>\n",
              "      <th>price_cat</th>\n",
              "      <th>marketing</th>\n",
              "      <th>radii</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1379560</td>\n",
              "      <td>1</td>\n",
              "      <td>55</td>\n",
              "      <td>1885</td>\n",
              "      <td>136.83</td>\n",
              "      <td>152.29</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>177.0</td>\n",
              "      <td>647</td>\n",
              "      <td>56</td>\n",
              "      <td>TYPE_C</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Beverages</td>\n",
              "      <td>Thai</td>\n",
              "      <td>5.176150</td>\n",
              "      <td>15.46</td>\n",
              "      <td>Addnl_Chgs</td>\n",
              "      <td>0</td>\n",
              "      <td>0.797885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1466964</td>\n",
              "      <td>1</td>\n",
              "      <td>55</td>\n",
              "      <td>1993</td>\n",
              "      <td>136.83</td>\n",
              "      <td>135.83</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>270.0</td>\n",
              "      <td>647</td>\n",
              "      <td>56</td>\n",
              "      <td>TYPE_C</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Beverages</td>\n",
              "      <td>Thai</td>\n",
              "      <td>5.598422</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>Discount</td>\n",
              "      <td>0</td>\n",
              "      <td>0.797885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1346989</td>\n",
              "      <td>1</td>\n",
              "      <td>55</td>\n",
              "      <td>2539</td>\n",
              "      <td>134.86</td>\n",
              "      <td>135.86</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>189.0</td>\n",
              "      <td>647</td>\n",
              "      <td>56</td>\n",
              "      <td>TYPE_C</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Beverages</td>\n",
              "      <td>Thai</td>\n",
              "      <td>5.241747</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Taxes</td>\n",
              "      <td>0</td>\n",
              "      <td>0.797885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1338232</td>\n",
              "      <td>1</td>\n",
              "      <td>55</td>\n",
              "      <td>2139</td>\n",
              "      <td>339.50</td>\n",
              "      <td>437.53</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>647</td>\n",
              "      <td>56</td>\n",
              "      <td>TYPE_C</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Beverages</td>\n",
              "      <td>Indian</td>\n",
              "      <td>3.988984</td>\n",
              "      <td>98.03</td>\n",
              "      <td>Addnl_Chgs</td>\n",
              "      <td>0</td>\n",
              "      <td>0.797885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1448490</td>\n",
              "      <td>1</td>\n",
              "      <td>55</td>\n",
              "      <td>2631</td>\n",
              "      <td>243.50</td>\n",
              "      <td>242.50</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>647</td>\n",
              "      <td>56</td>\n",
              "      <td>TYPE_C</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Beverages</td>\n",
              "      <td>Indian</td>\n",
              "      <td>3.688879</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>Discount</td>\n",
              "      <td>0</td>\n",
              "      <td>0.797885</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4e338f47-51e4-4b89-a026-7f23016f87ca')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-4e338f47-51e4-4b89-a026-7f23016f87ca 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-4e338f47-51e4-4b89-a026-7f23016f87ca');\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",
              "\n",
              "\n",
              "<div id=\"df-3d9c43fb-8c10-4f1d-b172-7ab348383308\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3d9c43fb-8c10-4f1d-b172-7ab348383308')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3d9c43fb-8c10-4f1d-b172-7ab348383308 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "combined"
            }
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import scipy.stats as stats\n",
        "import math\n",
        "\n",
        "def radii(x):\n",
        "  return(np.sqrt(x/math.pi))\n",
        "\n",
        "combined[\"radii\"] = combined.op_area.apply(radii)"
      ],
      "metadata": {
        "id": "abbv1hE6OZ09"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "combined.loc[:, [\"radii\", \"num_orders\"]].corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "NuR-dIsYOpHr",
        "outputId": "b4ad3b0b-e733-4e9f-ac5d-b97559182b68"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              radii  num_orders\n",
              "radii       1.00000     0.17253\n",
              "num_orders  0.17253     1.00000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3afb02ce-f548-42f0-89d4-eddd5fc1a9dd\" 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>radii</th>\n",
              "      <th>num_orders</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>radii</th>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.17253</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_orders</th>\n",
              "      <td>0.17253</td>\n",
              "      <td>1.00000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3afb02ce-f548-42f0-89d4-eddd5fc1a9dd')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-3afb02ce-f548-42f0-89d4-eddd5fc1a9dd 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-3afb02ce-f548-42f0-89d4-eddd5fc1a9dd');\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",
              "\n",
              "\n",
              "<div id=\"df-94341b94-29f2-4a33-abc6-96693e784d23\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-94341b94-29f2-4a33-abc6-96693e784d23')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-94341b94-29f2-4a33-abc6-96693e784d23 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"combined\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"radii\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5851099751452921,\n        \"min\": 0.1725295376697435,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.1725295376697435,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_orders\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5851099751452921,\n        \"min\": 0.1725295376697435,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.1725295376697435\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Center Type, Region code and City Code\n",
        "\n",
        "sns.boxplot(x = \"center_type\", y = \"num_orders\", data = combined)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "XXZuHhwVPWv7",
        "outputId": "bfe95129-c89e-4d28-bebc-b371861e7b4a"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x = \"region_code\", y = \"num_orders\", data = combined)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "WidseNI7QPqT",
        "outputId": "785fe63d-27e1-446c-a0ba-5ca41f580d42"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x = \"city_code\", y = \"num_orders\", data = combined)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "MBx_fPi2QhT7",
        "outputId": "b6255c63-b8d6-4c44-e90d-e62c461b7e45"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "combined.city_code.value_counts()[:5].index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R8h9HF9ERR8q",
        "outputId": "de728131-bbaf-4f3b-b5c0-613d5ad6d83a"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index([590, 526, 638, 522, 517], dtype='int64', name='city_code')"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def year(x):\n",
        "  if x<=52:\n",
        "    return(\"Year1\")\n",
        "  elif x<=104:\n",
        "    return(\"Year2\")\n",
        "  else:\n",
        "    return(\"Year3\")"
      ],
      "metadata": {
        "id": "FcLqvW2jRhMR"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "combined[\"Year\"] = combined.week.apply(year)\n",
        "\n",
        "sns.boxplot(x = \"Year\", y = \"num_orders\", data = combined)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "-Ap51A6gR5ZR",
        "outputId": "0cf3f75f-09d1-4fc5-f2bb-92691e415cef"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# max week  = 155\n",
        "\n",
        "sns.lineplot(x = \"week\", y = \"num_orders\", data = combined)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "_9MUdoDJSWwJ",
        "outputId": "7eeefa87-fea1-42a5-8c04-ac310954a6df"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming 'combined' DataFrame is already created as in the provided code.\n",
        "\n",
        "# Create a new feature 'sin_week' representing the sine transformation of the week number.\n",
        "combined['sin_week'] = np.sin(2 * np.pi * combined['week'] / 52)  # 52 weeks in a year\n",
        "combined['cos_week'] = np.cos(2 * np.pi * combined['week'] / 52)\n",
        "\n",
        "# You can visualize the effect of the sine transformation.\n",
        "plt.figure(figsize=(10, 5))\n",
        "sns.lineplot(x='week', y='sin_week', data=combined)\n",
        "plt.title('Sine Transformation of Week Number')\n",
        "plt.xlabel('Week')\n",
        "plt.ylabel('Sin(Week)')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "moHfN8bfS8Wy",
        "outputId": "00b57bda-5888-4f26-c04e-9331702b531b"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# You can visualize the effect of the sine transformation.\n",
        "plt.figure(figsize=(10, 5))\n",
        "sns.lineplot(x='week', y='cos_week', data=combined)\n",
        "plt.title('Sine Transformation of Week Number')\n",
        "plt.xlabel('Week')\n",
        "plt.ylabel('Sin(Week)')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "LLxVijO-UMCY",
        "outputId": "07113e32-625b-47ae-d99d-6db0543a29ec"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dropping 4 Features\n",
        "combined.drop([\"emailer_for_promotion\",\n",
        "               \"homepage_featured\", \"id\", \"week\"], axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "vMp9mhbbU9xY"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Feature Engineering\n",
        "\n",
        "* center ID wise num_orders\n",
        "* meal_id wise num_orders\n",
        "* city code wise num_orders\n",
        "* region_code wise num_orders\n",
        "* center type wise num_orders\n",
        "* category wise num_orders\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "088paJ2meB6V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "combined.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7WYF6Od4dNBj",
        "outputId": "f1994a24-fd92-4195-e2ee-88666b350a30"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['center_id', 'meal_id', 'checkout_price', 'base_price', 'num_orders',\n",
              "       'city_code', 'region_code', 'center_type', 'op_area', 'category',\n",
              "       'cuisine', 'log_target', 'price_diff', 'price_cat', 'marketing',\n",
              "       'radii', 'Year', 'sin_week', 'cos_week'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# center wise\n",
        "combined[\"magic1\"]=  combined.groupby(\"center_id\")[\"num_orders\"].transform(\"min\")\n",
        "combined[\"magic2\"]=  combined.groupby(\"center_id\")[\"num_orders\"].transform(\"mean\")\n",
        "combined[\"magic3\"]=  combined.groupby(\"center_id\")[\"num_orders\"].transform(\"median\")\n",
        "combined[\"magic4\"]=  combined.groupby(\"center_id\")[\"num_orders\"].transform(\"std\")\n",
        "combined[\"magic5\"]=  combined.groupby(\"center_id\")[\"num_orders\"].transform(\"max\")"
      ],
      "metadata": {
        "id": "mLjzMSHndlPa"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# meal_id\n",
        "combined[\"magic6\"]=  combined.groupby(\"meal_id\")[\"num_orders\"].transform(\"min\")\n",
        "combined[\"magic7\"]=  combined.groupby(\"meal_id\")[\"num_orders\"].transform(\"mean\")\n",
        "combined[\"magic8\"]=  combined.groupby(\"meal_id\")[\"num_orders\"].transform(\"median\")\n",
        "combined[\"magic9\"]=  combined.groupby(\"meal_id\")[\"num_orders\"].transform(\"std\")\n",
        "combined[\"magic10\"]=  combined.groupby(\"meal_id\")[\"num_orders\"].transform(\"max\")"
      ],
      "metadata": {
        "id": "BoJIxGXqejxS"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# city_code\n",
        "combined[\"magic11\"]= combined.groupby(\"city_code\")[\"num_orders\"].transform(\"min\")\n",
        "combined[\"magic12\"]= combined.groupby(\"city_code\")[\"num_orders\"].transform(\"mean\")\n",
        "combined[\"magic13\"]= combined.groupby(\"city_code\")[\"num_orders\"].transform(\"median\")\n",
        "combined[\"magic14\"]= combined.groupby(\"city_code\")[\"num_orders\"].transform(\"std\")\n",
        "combined[\"magic15\"]= combined.groupby(\"city_code\")[\"num_orders\"].transform(\"max\")"
      ],
      "metadata": {
        "id": "hdv0TWFhfc27"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# region_code\n",
        "\n",
        "combined[\"magic16\"]= combined.groupby(\"region_code\")[\"num_orders\"].transform(\"min\")\n",
        "combined[\"magic17\"]= combined.groupby(\"region_code\")[\"num_orders\"].transform(\"mean\")\n",
        "combined[\"magic18\"]= combined.groupby(\"region_code\")[\"num_orders\"].transform(\"median\")\n",
        "combined[\"magic19\"]= combined.groupby(\"region_code\")[\"num_orders\"].transform(\"std\")\n",
        "combined[\"magic20\"]= combined.groupby(\"region_code\")[\"num_orders\"].transform(\"max\")\n"
      ],
      "metadata": {
        "id": "TJR8oUkiftpD"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# category\n",
        "\n",
        "combined[\"magic21\"]= combined.groupby(\"category\")[\"num_orders\"].transform(\"min\")\n",
        "combined[\"magic22\"]= combined.groupby(\"category\")[\"num_orders\"].transform(\"mean\")\n",
        "combined[\"magic23\"]= combined.groupby(\"category\")[\"num_orders\"].transform(\"median\")\n",
        "combined[\"magic24\"]= combined.groupby(\"category\")[\"num_orders\"].transform(\"std\")\n",
        "combined[\"magic25\"]= combined.groupby(\"category\")[\"num_orders\"].transform(\"max\")\n"
      ],
      "metadata": {
        "id": "x9b9U9SKfsCU"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Category Wise CheckOut Price\n",
        "* centerid wise check out price\n",
        "* meal_id wise checkout price\n",
        "* city code wise checkout price\n",
        "* region code wise checkout price"
      ],
      "metadata": {
        "id": "Hh4emLVBhqNM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Category Wise Check Out Price\n",
        "combined[\"magic26\"]= combined.groupby(\"category\")[\"checkout_price\"].transform(\"min\")\n",
        "combined[\"magic27\"]= combined.groupby(\"category\")[\"checkout_price\"].transform(\"mean\")\n",
        "combined[\"magic28\"]= combined.groupby(\"category\")[\"checkout_price\"].transform(\"median\")\n",
        "combined[\"magic29\"]= combined.groupby(\"category\")[\"checkout_price\"].transform(\"std\")\n",
        "combined[\"magic30\"]= combined.groupby(\"category\")[\"checkout_price\"].transform(\"max\")\n"
      ],
      "metadata": {
        "id": "NVDJ6yqhgr8Z"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Center-ID vs Check Out Price\n",
        "combined[\"magic31\"]= combined.groupby(\"center_id\")[\"checkout_price\"].transform(\"min\")\n",
        "combined[\"magic32\"]= combined.groupby(\"center_id\")[\"checkout_price\"].transform(\"mean\")\n",
        "combined[\"magic33\"]= combined.groupby(\"center_id\")[\"checkout_price\"].transform(\"median\")\n",
        "combined[\"magic34\"]= combined.groupby(\"center_id\")[\"checkout_price\"].transform(\"std\")\n",
        "combined[\"magic35\"]= combined.groupby(\"center_id\")[\"checkout_price\"].transform(\"max\")\n"
      ],
      "metadata": {
        "id": "s5jTMbe8gyC7"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Meal-ID vs Check Out Price\n",
        "combined[\"magic36\"]= combined.groupby(\"meal_id\")[\"checkout_price\"].transform(\"min\")\n",
        "combined[\"magic37\"]= combined.groupby(\"meal_id\")[\"checkout_price\"].transform(\"mean\")\n",
        "combined[\"magic38\"]= combined.groupby(\"meal_id\")[\"checkout_price\"].transform(\"median\")\n",
        "combined[\"magic39\"]= combined.groupby(\"meal_id\")[\"checkout_price\"].transform(\"std\")\n",
        "combined[\"magic40\"]= combined.groupby(\"meal_id\")[\"checkout_price\"].transform(\"max\")\n"
      ],
      "metadata": {
        "id": "OITDnm3jiJox"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Region-Code vs Check Out Price\n",
        "combined[\"magic41\"]= combined.groupby(\"region_code\")[\"checkout_price\"].transform(\"min\")\n",
        "combined[\"magic42\"]= combined.groupby(\"region_code\")[\"checkout_price\"].transform(\"mean\")\n",
        "combined[\"magic43\"]= combined.groupby(\"region_code\")[\"checkout_price\"].transform(\"median\")\n",
        "combined[\"magic44\"]= combined.groupby(\"region_code\")[\"checkout_price\"].transform(\"std\")\n",
        "combined[\"magic45\"]= combined.groupby(\"region_code\")[\"checkout_price\"].transform(\"max\")\n"
      ],
      "metadata": {
        "id": "TKt_zZM4iRA6"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# City-Code vs Check Out Price\n",
        "combined[\"magic46\"]= combined.groupby(\"city_code\")[\"checkout_price\"].transform(\"min\")\n",
        "combined[\"magic47\"]= combined.groupby(\"city_code\")[\"checkout_price\"].transform(\"mean\")\n",
        "combined[\"magic48\"]= combined.groupby(\"city_code\")[\"checkout_price\"].transform(\"median\")\n",
        "combined[\"magic49\"]= combined.groupby(\"city_code\")[\"checkout_price\"].transform(\"std\")\n",
        "combined[\"magic50\"]= combined.groupby(\"city_code\")[\"checkout_price\"].transform(\"max\")\n"
      ],
      "metadata": {
        "id": "HxU8natsihup"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the Data into train and test\n",
        "\n",
        "newtrain = combined.loc[0:train.shape[0]-1, :]\n",
        "newtest = combined.loc[train.shape[0]:, :]\n",
        "\n",
        "train.shape, test.shape, newtrain.shape, newtest.shape\n",
        "\n",
        "newtest.drop(\"num_orders\", axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "RS_sEmi0i4nw"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.shape, test.shape, newtrain.shape, newtest.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cdinQ7-DkpRn",
        "outputId": "0fc184a1-0536-4794-d98c-6a7f91f3f153"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((456548, 9), (32573, 8), (456548, 69), (32573, 68))"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Statistical Analysis"
      ],
      "metadata": {
        "id": "moOseZyMlmw4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.set_option('display.max_columns', 100)\n",
        "newtrain.select_dtypes(include = np.number).columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fp_goLOfk0de",
        "outputId": "7c92d367-750e-4fb1-c444-fee378379af0"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['center_id', 'meal_id', 'checkout_price', 'base_price', 'num_orders',\n",
              "       'city_code', 'region_code', 'op_area', 'log_target', 'price_diff',\n",
              "       'marketing', 'radii', 'sin_week', 'cos_week', 'magic1', 'magic2',\n",
              "       'magic3', 'magic4', 'magic5', 'magic6', 'magic7', 'magic8', 'magic9',\n",
              "       'magic10', 'magic11', 'magic12', 'magic13', 'magic14', 'magic15',\n",
              "       'magic16', 'magic17', 'magic18', 'magic19', 'magic20', 'magic21',\n",
              "       'magic22', 'magic23', 'magic24', 'magic25', 'magic26', 'magic27',\n",
              "       'magic28', 'magic29', 'magic30', 'magic31', 'magic32', 'magic33',\n",
              "       'magic34', 'magic35', 'magic36', 'magic37', 'magic38', 'magic39',\n",
              "       'magic40', 'magic41', 'magic42', 'magic43', 'magic44', 'magic45',\n",
              "       'magic46', 'magic47', 'magic48', 'magic49', 'magic50'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nums = ['magic1', 'magic2',\n",
        "       'magic3', 'magic4', 'magic5', 'magic6', 'magic7', 'magic8', 'magic9',\n",
        "       'magic10', 'magic11', 'magic12', 'magic13', 'magic14', 'magic15',\n",
        "       'magic16', 'magic17', 'magic18', 'magic19', 'magic20', 'magic21',\n",
        "       'magic22', 'magic23', 'magic24', 'magic25', 'magic26', 'magic27',\n",
        "       'magic28', 'magic29', 'magic30', 'magic31', 'magic32', 'magic33',\n",
        "       'magic34', 'magic35', 'magic36', 'magic37', 'magic38', 'magic39',\n",
        "       'magic40', 'magic41', 'magic42', 'magic43', 'magic44', 'magic45',\n",
        "       'magic46', 'magic47', 'magic48', 'magic49', 'magic50']"
      ],
      "metadata": {
        "id": "qVkU0zo3lrYf"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Ho: Predictor and Target are Independent\n",
        "# Ha: Ho is False\n",
        "\n",
        "pvalue = []\n",
        "\n",
        "for i in nums:\n",
        "  teststats, pval = stats.ttest_ind(newtrain.loc[:, i],\n",
        "                                    newtrain.num_orders)\n",
        "  pvalue.append(pval)"
      ],
      "metadata": {
        "id": "hLXy0eKWl_Nw"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pvals = pd.DataFrame(pvalue, index = nums, columns = [\"PValue\"])\n",
        "insignificant = pvals.loc[pvals.PValue>0.05].index"
      ],
      "metadata": {
        "id": "adOs38P1maHH"
      },
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Price_Diff vs target\n",
        "# Ho: Price Diff has no relation with the Num_Orders\n",
        "# Ha: No of Orders depend on Price Diff...\n",
        "\n",
        "teststats, pval = stats.ttest_ind(newtrain.loc[:, \"price_diff\"],\n",
        "                                  newtrain.num_orders)\n",
        "print(teststats, pval)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mo2uw8cbomDn",
        "outputId": "6d6090f8-db64-4ff9-d88f-a5f90213e600"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-406.4703738608052 0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Radii and Op Area\n",
        "# Ho: Op Area has no relation with the Num_Orders\n",
        "# Ha: No of Orders depend on Op Area...\n",
        "\n",
        "teststats, pval = stats.ttest_ind(newtrain.loc[:, \"op_area\"],\n",
        "                                  newtrain.num_orders)\n",
        "print(teststats, pval)\n",
        "\n",
        "teststats, pval = stats.ttest_ind(newtrain.loc[:, \"radii\"],\n",
        "                                  newtrain.num_orders)\n",
        "print(teststats, pval)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lAG9bnq5pHyd",
        "outputId": "c637b003-7bf6-4e75-a683-1715a5f7ff65"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-439.9423098622263 0.0\n",
            "-444.98491992280447 0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Cat Vars vs Num"
      ],
      "metadata": {
        "id": "HDpDbqrNrvod"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "newtrain.select_dtypes(include = \"object\").columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u3981TThqej0",
        "outputId": "21177b2f-27b4-407a-bcd9-ed40282230d5"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['center_type', 'category', 'cuisine', 'price_cat', 'Year'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Anova\n",
        "# Ho: Predictors and Target are Independent\n",
        "# Ha: Ho is False\n",
        "import statsmodels.formula.api as sfa\n",
        "from statsmodels.stats.anova import anova_lm\n",
        "\n",
        "model = sfa.ols(\"num_orders~center_type+category+cuisine+price_cat+Year\",\n",
        "                data = newtrain).fit()\n",
        "\n",
        "print(anova_lm(model))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OuHIVikUr1O7",
        "outputId": "db9b3765-26be-4403-de2c-55683fa3a3e6"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   df        sum_sq       mean_sq             F        PR(>F)\n",
            "center_type       2.0  6.090329e+08  3.045165e+08   2802.233558  0.000000e+00\n",
            "category         13.0  1.343108e+10  1.033160e+09   9507.383881  0.000000e+00\n",
            "cuisine           3.0  4.510531e+09  1.503510e+09  13835.663300  0.000000e+00\n",
            "price_cat         2.0  3.397928e+09  1.698964e+09  15634.275816  0.000000e+00\n",
            "Year              2.0  7.186426e+06  3.593213e+06     33.065609  4.373492e-15\n",
            "Residual     456525.0  4.961021e+10  1.086692e+05           NaN           NaN\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop the Insignificant Features from the Train and Test\n",
        "\n",
        "newtrain.drop(insignificant, axis = 1, inplace = True)\n",
        "newtest.drop(insignificant, axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "op1yYwbYsQ7L"
      },
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "newtrain.drop(['center_id', 'meal_id', 'city_code', 'region_code',\n",
        "               \"category\"], axis = 1, inplace = True)\n",
        "\n",
        "newtest.drop(['center_id', 'meal_id', 'city_code', 'region_code',\n",
        "              'category'], axis = 1, inplace = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "_rP41FOBtg5a",
        "outputId": "bab6918e-9f97-43b8-c2eb-c8f1cff3600c"
      },
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "\"['center_id', 'meal_id', 'city_code', 'region_code', 'category'] not found in axis\"",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-146-ea1052212bfc>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m newtrain.drop(['center_id', 'meal_id', 'city_code', 'region_code', \n\u001b[0m\u001b[1;32m      2\u001b[0m                \"category\"], axis = 1, inplace = True)\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m newtest.drop(['center_id', 'meal_id', 'city_code', 'region_code',\n\u001b[1;32m      5\u001b[0m               'category'], axis = 1, inplace = True)\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   5579\u001b[0m                 \u001b[0mweight\u001b[0m  \u001b[0;36m1.0\u001b[0m     \u001b[0;36m0.8\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5580\u001b[0m         \"\"\"\n\u001b[0;32m-> 5581\u001b[0;31m         return super().drop(\n\u001b[0m\u001b[1;32m   5582\u001b[0m             \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5583\u001b[0m             \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4786\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4787\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4788\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4789\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4790\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[1;32m   4828\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4829\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4830\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4831\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_axis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4832\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   7068\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7069\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 7070\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{labels[mask].tolist()} not found in axis\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   7071\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7072\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"['center_id', 'meal_id', 'city_code', 'region_code', 'category'] not found in axis\""
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Modeling\n",
        "\n",
        "* Split the Data in X and y\n",
        "* K Fold CV\n",
        "* Prediction\n",
        "* Submission"
      ],
      "metadata": {
        "id": "KebWp1NMvIk1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the Data in X and y\n",
        "X = newtrain.drop([\"num_orders\", \"log_target\", \"Year\"], axis = 1)\n",
        "y = newtrain.num_orders\n",
        "logy = newtrain.log_target\n",
        "dummyx = pd.get_dummies(X, drop_first=True)\n",
        "newtest.drop(\"log_target\",axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "ppEbdfywtnnK"
      },
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "newtest.drop(\"Year\", axis = 1, inplace=True)\n",
        "dummytest = pd.get_dummies(newtest, drop_first = True)"
      ],
      "metadata": {
        "id": "ah-1D_yZ1B56"
      },
      "execution_count": 148,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# K Fold Model\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
        "\n",
        "from sklearn.metrics import root_mean_squared_log_error, root_mean_squared_error\n",
        "kfold = KFold(n_splits = 5, shuffle = True)\n",
        "\n",
        "lr = LinearRegression()\n",
        "rf = RandomForestRegressor()\n",
        "gbm = GradientBoostingRegressor()"
      ],
      "metadata": {
        "id": "75IIbIWVuOhq"
      },
      "execution_count": 149,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred = []\n",
        "for train_index, test_index in kfold.split(dummyx, y):\n",
        "  xtrain = dummyx.iloc[train_index, :]\n",
        "  xtest = dummyx.iloc[test_index, :]\n",
        "  ytrain = y.iloc[train_index]\n",
        "  ytest = y.iloc[test_index]\n",
        "  # Modelling and Prediction\n",
        "  #pred_lr = lr.fit(xtrain, ytrain).predict(xtest)\n",
        "  # Cost Function\n",
        "  #cost = root_mean_squared_error(ytest, pred_lr)\n",
        "  #print(cost)\n",
        "\n",
        "  # Final Prediction\n",
        "  pred.append(lr.fit(xtrain, ytrain).predict(dummytest))"
      ],
      "metadata": {
        "id": "lwdKVfSVvovh"
      },
      "execution_count": 150,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "submission = pd.DataFrame(pred).T.mean(axis = 1)"
      ],
      "metadata": {
        "id": "hyDp_-J313dR"
      },
      "execution_count": 151,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Sample\n",
        "sample = pd.read_csv(\"/content/sample_submission_hSlSoT6.csv\")"
      ],
      "metadata": {
        "id": "b4DkH-MnvzU6"
      },
      "execution_count": 152,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the Predictions\n",
        "sample[\"num_orders\"] = np.abs(submission)\n",
        "# Export it to csv\n",
        "sample.to_csv(\"LRModel.csv\", index = False) # 106"
      ],
      "metadata": {
        "id": "rAOUnhWE1hCX"
      },
      "execution_count": 160,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Target Variable is Logy\n",
        "pred = []\n",
        "for train_index, test_index in kfold.split(dummyx, logy):\n",
        "  xtrain = dummyx.iloc[train_index, :]\n",
        "  xtest = dummyx.iloc[test_index, :]\n",
        "  ytrain = logy.iloc[train_index]\n",
        "  ytest = logy.iloc[test_index]\n",
        "# Model and Prediction\n",
        "  pred_lr = lr.fit(xtrain, ytrain).predict(xtest)\n",
        "  cost = root_mean_squared_log_error(ytest, pred_lr)\n",
        "  print(cost)\n",
        "# Final Prediction\n",
        "  pred.append(lr.predict(dummytest))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cqllb-4q8nJ-",
        "outputId": "2f806de9-5972-4b88-daab-244ef6e27c5e"
      },
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.12555580938320252\n",
            "0.12466305183153918\n",
            "0.1251009748833096\n",
            "0.1252244404355564\n",
            "0.1250314988546363\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the Predictions\n",
        "submission = pd.DataFrame(np.exp(pred)).T.mean(axis = 1)\n",
        "\n",
        "sample[\"num_orders\"] = np.abs(submission)\n",
        "# Export it to csv\n",
        "sample.to_csv(\"LRLOGModel.csv\", index = False) # 65"
      ],
      "metadata": {
        "id": "RUjlTLXY8oRq"
      },
      "execution_count": 166,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Target Variable is Logy\n",
        "pred = []\n",
        "for train_index, test_index in kfold.split(dummyx, logy):\n",
        "  xtrain = dummyx.iloc[train_index, :]\n",
        "  xtest = dummyx.iloc[test_index, :]\n",
        "  ytrain = logy.iloc[train_index]\n",
        "  ytest = logy.iloc[test_index]\n",
        "# Model and Prediction\n",
        "  pred_lr = rf.fit(xtrain, ytrain).predict(xtest)\n",
        "  cost = root_mean_squared_log_error(ytest, pred_lr)\n",
        "  print(cost)\n",
        "# Final Prediction\n",
        "  pred.append(rf.predict(dummytest))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cfiiQhcV-2-h",
        "outputId": "b81f0519-ff77-4e92-e3d5-b9e7a5833022"
      },
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.09810017048455325\n",
            "0.09808417132134714\n",
            "0.09797937675106864\n",
            "0.0980293417733256\n",
            "0.09816812225002862\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "submission = pd.DataFrame(np.exp(pred)).T.mean(axis = 1)\n",
        "\n",
        "sample[\"num_orders\"] = np.abs(submission)\n",
        "# Export it to csv\n",
        "sample.to_csv(\"PradeepjiModel.csv\", index = False) # 53.66"
      ],
      "metadata": {
        "id": "bgKTaQsYAY8x"
      },
      "execution_count": 169,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KnofTaKbO_Sx"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}