Food_Data_Science_Project_Shemsu.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gc8x8QimJY_H"
      },
      "source": [
        "# Project Foundations for Data Science: FoodHub Data Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NqikjkWwJY_L"
      },
      "source": [
        "### **Context**\n",
        "\n",
        "The number of restaurants in New York is increasing day by day. Lots of students and busy professionals rely on those restaurants due to their hectic lifestyles. Online food delivery service is a great option for them. It provides them with good food from their favorite restaurants. A food aggregator company FoodHub offers access to multiple restaurants through a single smartphone app.\n",
        "\n",
        "The app allows the restaurants to receive a direct online order from a customer. The app assigns a delivery person from the company to pick up the order after it is confirmed by the restaurant. The delivery person then uses the map to reach the restaurant and waits for the food package. Once the food package is handed over to the delivery person, he/she confirms the pick-up in the app and travels to the customer's location to deliver the food. The delivery person confirms the drop-off in the app after delivering the food package to the customer. The customer can rate the order in the app. The food aggregator earns money by collecting a fixed margin of the delivery order from the restaurants.\n",
        "\n",
        "### **Objective**\n",
        "\n",
        "The food aggregator company has stored the data of the different orders made by the registered customers in their online portal. They want to analyze the data to get a fair idea about the demand of different restaurants which will help them in enhancing their customer experience. Suppose you are hired as a Data Scientist in this company and the Data Science team has shared some of the key questions that need to be answered. Perform the data analysis to find answers to these questions that will help the company to improve the business.\n",
        "\n",
        "### **Data Description**\n",
        "\n",
        "The data contains the different data related to a food order. The detailed data dictionary is given below.\n",
        "\n",
        "### **Data Dictionary**\n",
        "\n",
        "* order_id: Unique ID of the order\n",
        "* customer_id: ID of the customer who ordered the food\n",
        "* restaurant_name: Name of the restaurant\n",
        "* cuisine_type: Cuisine ordered by the customer\n",
        "* cost: Cost of the order\n",
        "* day_of_the_week: Indicates whether the order is placed on a weekday or weekend (The weekday is from Monday to Friday and the weekend is Saturday and Sunday)\n",
        "* rating: Rating given by the customer out of 5\n",
        "* food_preparation_time: Time (in minutes) taken by the restaurant to prepare the food. This is calculated by taking the difference between the timestamps of the restaurant's order confirmation and the delivery person's pick-up confirmation.\n",
        "* delivery_time: Time (in minutes) taken by the delivery person to deliver the food package. This is calculated by taking the difference between the timestamps of the delivery person's pick-up confirmation and drop-off information"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cEtNUxNAJY_M"
      },
      "source": [
        "### Let us start by importing the required libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u6PlpJAPJY_N"
      },
      "outputs": [],
      "source": [
        "# import libraries for data manipulation\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# import libraries for data visualization\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# mapping the appropriate drive to save/import data\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QUzGrIKA594k",
        "outputId": "c1dd5186-927d-4670-b75d-41d97f8bb8cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AdracY0HJY_O"
      },
      "source": [
        "### Understanding the structure of the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GF01IZDbJY_O",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        },
        "outputId": "c326225e-2fa5-4731-e177-51f2ee8bf064"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-8e1404d3f3bb>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# reading the data and observing the first 10 rows\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive/MyDrive/MIT/Course 1/Project Assessment - FoodHub/foodhub_order.csv'\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      3\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
          ]
        }
      ],
      "source": [
        "# reading the data and observing the first 10 rows\n",
        "df = pd.read_csv('/content/drive/MyDrive/MIT/Course 1/Project Assessment - FoodHub/foodhub_order.csv')\n",
        "df.head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J_Y9aJDJJY_P"
      },
      "source": [
        "#### Observations:\n",
        "\n",
        "The DataFrame has 9 columns as mentioned in the Data Dictionary. Data in each row corresponds to the order placed by a customer."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c3kenV4vJY_Q"
      },
      "source": [
        "### **Question 1:** How many rows and columns are present in the data?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Yvz5i-PhJY_Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0b061f0f-9382-4481-d459-722162246f44"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1898, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# getting dataframe shape\n",
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UsTNy3unJY_R"
      },
      "source": [
        "#### Observations: There are 1898 rows and 9 columns.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jRXakVrUJY_R"
      },
      "source": [
        "### **Question 2:** What are the datatypes of the different columns in the dataset?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Kp70Z0BNJY_R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fefb2528-d2c0-49a0-db29-c296029b6682"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1898 entries, 0 to 1897\n",
            "Data columns (total 9 columns):\n",
            " #   Column                 Non-Null Count  Dtype  \n",
            "---  ------                 --------------  -----  \n",
            " 0   order_id               1898 non-null   int64  \n",
            " 1   customer_id            1898 non-null   int64  \n",
            " 2   restaurant_name        1898 non-null   object \n",
            " 3   cuisine_type           1898 non-null   object \n",
            " 4   cost_of_the_order      1898 non-null   float64\n",
            " 5   day_of_the_week        1898 non-null   object \n",
            " 6   rating                 1898 non-null   object \n",
            " 7   food_preparation_time  1898 non-null   int64  \n",
            " 8   delivery_time          1898 non-null   int64  \n",
            "dtypes: float64(1), int64(4), object(4)\n",
            "memory usage: 133.6+ KB\n"
          ]
        }
      ],
      "source": [
        "# getting data types\n",
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WJes5GP2JY_S"
      },
      "source": [
        "#### Observations: column data types: Columns [0, 1, 7, 8] == integer, Columns [2, 3, 5, 6] == object, Column [4] == float"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pF_92MN6JY_S"
      },
      "source": [
        "### **Question 3:** Are there any missing values in the data? If yes, treat them using an appropriate method. [1 mark]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Zth-RR1PJY_S",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7840a8b5-288f-4dd2-ade9-fb5698b2ac46"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "order_id                 1898\n",
              "customer_id              1200\n",
              "restaurant_name           178\n",
              "cuisine_type               14\n",
              "cost_of_the_order         312\n",
              "day_of_the_week             2\n",
              "rating                      4\n",
              "food_preparation_time      16\n",
              "delivery_time              19\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# looking for missing values\n",
        "df.nunique(axis = 0, dropna = False)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# exlcuding any missing values\n",
        "df.nunique(axis = 0, dropna = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SEREGnbX-Tt-",
        "outputId": "83a25960-cabb-499c-df3d-7019087ba3e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "order_id                 1898\n",
              "customer_id              1200\n",
              "restaurant_name           178\n",
              "cuisine_type               14\n",
              "cost_of_the_order         312\n",
              "day_of_the_week             2\n",
              "rating                      4\n",
              "food_preparation_time      16\n",
              "delivery_time              19\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jDHT3vD1JY_S"
      },
      "source": [
        "#### Observations: Since `dropna` didn't result in any differences in the dataframe's shape, I can assume that there likely are no missing values to treat.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MnJo7MWrJY_S"
      },
      "source": [
        "### **Question 4:** Check the statistical summary of the data. What is the minimum, average, and maximum time it takes for food to be prepared once an order is placed?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WprQBgOSJY_T",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "outputId": "8557a758-5bcf-45af-b971-bfc2c51b6c80"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            order_id    customer_id restaurant_name cuisine_type  \\\n",
              "count   1.898000e+03    1898.000000            1898         1898   \n",
              "unique           NaN            NaN             178           14   \n",
              "top              NaN            NaN     Shake Shack     American   \n",
              "freq             NaN            NaN             219          584   \n",
              "mean    1.477496e+06  171168.478398             NaN          NaN   \n",
              "std     5.480497e+02  113698.139743             NaN          NaN   \n",
              "min     1.476547e+06    1311.000000             NaN          NaN   \n",
              "25%     1.477021e+06   77787.750000             NaN          NaN   \n",
              "50%     1.477496e+06  128600.000000             NaN          NaN   \n",
              "75%     1.477970e+06  270525.000000             NaN          NaN   \n",
              "max     1.478444e+06  405334.000000             NaN          NaN   \n",
              "\n",
              "        cost_of_the_order day_of_the_week     rating  food_preparation_time  \\\n",
              "count         1898.000000            1898       1898            1898.000000   \n",
              "unique                NaN               2          4                    NaN   \n",
              "top                   NaN         Weekend  Not given                    NaN   \n",
              "freq                  NaN            1351        736                    NaN   \n",
              "mean            16.498851             NaN        NaN              27.371970   \n",
              "std              7.483812             NaN        NaN               4.632481   \n",
              "min              4.470000             NaN        NaN              20.000000   \n",
              "25%             12.080000             NaN        NaN              23.000000   \n",
              "50%             14.140000             NaN        NaN              27.000000   \n",
              "75%             22.297500             NaN        NaN              31.000000   \n",
              "max             35.410000             NaN        NaN              35.000000   \n",
              "\n",
              "        delivery_time  \n",
              "count     1898.000000  \n",
              "unique            NaN  \n",
              "top               NaN  \n",
              "freq              NaN  \n",
              "mean        24.161749  \n",
              "std          4.972637  \n",
              "min         15.000000  \n",
              "25%         20.000000  \n",
              "50%         25.000000  \n",
              "75%         28.000000  \n",
              "max         33.000000  "
            ],
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              "      <td>1.898000e+03</td>\n",
              "      <td>1898.000000</td>\n",
              "      <td>1898</td>\n",
              "      <td>1898</td>\n",
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              "      <td>1898</td>\n",
              "      <td>1898</td>\n",
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              "      <td>219</td>\n",
              "      <td>584</td>\n",
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              "      <td>NaN</td>\n",
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              "      <th>mean</th>\n",
              "      <td>1.477496e+06</td>\n",
              "      <td>171168.478398</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>7.483812</td>\n",
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              "      <td>4.972637</td>\n",
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              "      <td>4.470000</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>15.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.477021e+06</td>\n",
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              "  <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-cfdd1fb7-0f59-4094-acbb-b6620dbed5e3 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-cfdd1fb7-0f59-4094-acbb-b6620dbed5e3');\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-c376d372-6e71-4af4-9043-828c93d0e522\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c376d372-6e71-4af4-9043-828c93d0e522')\"\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-c376d372-6e71-4af4-9043-828c93d0e522 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# getting statistical summary of the dataframe\n",
        "df.describe(include = 'all')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9YbSOR5wJY_T"
      },
      "source": [
        "#### Observations: Minimum = 20, Average = ~27.4, Maximum = 35\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cw3l58z_JY_T"
      },
      "source": [
        "### **Question 5:** How many orders are not rated?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hEzDUL9wgksi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "317407dc-653a-4dde-f29a-b506344a65e6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "736"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# getting unrated rows\n",
        "df['rating'].value_counts()['Not given']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5E0O_oviJY_T"
      },
      "source": [
        "#### Observations: Of the total rows within the column 'rating', 736 orders are unrated.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7msIfXE-JY_T"
      },
      "source": [
        "### Exploratory Data Analysis (EDA)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-2xfI28YJY_T"
      },
      "source": [
        "### Univariate Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0RHZnugBJY_U"
      },
      "source": [
        "### **Question 6:** Explore all the variables and provide observations on their distributions. (Generally, histograms, boxplots, countplots, etc. are used for univariate exploration.) [9 marks]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# exploring when orders are being placed\n",
        "sns.countplot(data = df, x = 'day_of_the_week')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "NtmMY0Qj3raj",
        "outputId": "6cd15b5c-39be-4681-d67d-654e5dee445d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='day_of_the_week', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "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": [
        "# exploring distribution of cost per order\n",
        "sns.histplot(data = df, x = 'cost_of_the_order', bins = 10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "6qC59FpDyjlx",
        "outputId": "6db197e7-563a-4f72-fc4f-731c94502770"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='cost_of_the_order', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "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": [
        "# exploring distribution of ratings per order\n",
        "sns.countplot(data = df, x = 'rating')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "J-uEnzxotYtx",
        "outputId": "8edfc512-2f96-4e3a-bc2d-bfc894d88df6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='rating', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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QQZJUV1fXKMUAAAA0tO8Udv7RoUOH9Ic//EHl5eVnhZ+pU6dedGEAAAANoV5vY7388svq1q2bpk6dqtdee02rVq2yltWrV9erkJkzZ8rhcGjChAlWW1VVlbKyshQXF6eWLVsqPT1dZWVlQduVlJQoLS1NLVq0UHx8vCZNmqTTp0/XqwYAAGCeel3Zeeqpp/T0009r8uTJDVJEUVGRFi5cqJ49ewa1T5w4UW+//bZeffVVuVwujRs3TnfddZe2b98u6e9vgKWlpcnj8WjHjh0qLS3Vvffeq2bNmmnGjBkNUhsAALi01evKzldffaXhw4c3SAEnTpxQRkaGXn75ZV1xxRVWe2VlpRYtWqTnn39egwYNUp8+fbR48WLt2LFDO3fulCRt3LhR+/fv1+9+9zv16tVLQ4cO1ZNPPqn8/PxzfgfojOrqavn9/qAFAACYqV5hZ/jw4dq4cWODFJCVlaW0tDSlpKQEtRcXF+vUqVNB7V27dlX79u1VWFgoSSosLFSPHj3kdrutMampqfL7/dq3b995j5mXlyeXy2UtiYmJDTIXAAAQeup1G+uaa67R448/rp07d6pHjx5q1qxZUP8vfvGLC9rPihUr9Mc//lFFRUVn9fl8PkVGRio2Njao3e12Wx8z9Pl8QUHnTP+ZvvPJyclRdna2te73+wk8AAAYql5h56WXXlLLli1VUFCggoKCoD6Hw3FBYefIkSN6+OGHtWnTJkVHR9enjHqLiopSVFRUkx4TAADYo15h5/Dhwxd94OLiYpWXl6t3795WW21trbZt26YXX3xRGzZsUE1NjSoqKoKu7pSVlcnj8UiSPB6Pdu/eHbTfM29rnRkDAAAub/V6ZqchDB48WHv37tUHH3xgLX379lVGRob1c7NmzbRlyxZrm4MHD6qkpERer1eS5PV6tXfvXpWXl1tjNm3aJKfTqeTk5CafEwAACD31urLzwAMPfGP/K6+88q37aNWqlbp37x7UFhMTo7i4OKt99OjRys7OVuvWreV0OjV+/Hh5vV4NGDBAkjRkyBAlJydr1KhRmjVrlnw+n6ZMmaKsrCxuUwEAAEn1DDtfffVV0PqpU6f00UcfqaKi4py/ILS+Zs+erbCwMKWnp6u6ulqpqamaN2+e1R8eHq41a9Zo7Nix8nq9iomJUWZmpnJzcxusBgAAcGmrV9hZtWrVWW11dXUaO3asrr766noXs3Xr1qD16Oho5efnKz8//7zbdOjQQWvXrq33MQEAl7eCm35gdwn4Pz/YVvDtg+qhwZ7ZCQsLU3Z2tmbPnt1QuwQAALhoDfqA8qeffsrvpQIAACGlXrex/vGDfJIUCARUWlqqt99+W5mZmQ1SGAAAQEOoV9h5//33g9bDwsLUpk0bPffcc9/6phYAAEBTqlfY+cMf/tDQdQAAADSKeoWdM7788ksdPHhQktSlSxe1adOmQYoCAABoKPV6QPnkyZN64IEH1LZtW91000266aablJCQoNGjR+vrr79u6BoBAADqrV5hJzs7WwUFBXrrrbdUUVGhiooK/f73v1dBQYEeeeSRhq4RAACg3up1G+v111/Xa6+9pptvvtlqu+2229S8eXPdfffdmj9/fkPVBwAAcFHqdWXn66+/ltvtPqs9Pj6e21gAACCk1CvseL1eTZs2TVVVVVbb3/72Nz3xxBPWbyQHAAAIBfW6jTVnzhzdeuutateuna677jpJ0p49exQVFaWNGzc2aIEAAAAXo15hp0ePHjp06JCWLVumAwcOSJJGjhypjIwMNW/evEELBAAAuBj1Cjt5eXlyu9168MEHg9pfeeUVffnll5o8eXKDFAcAAHCx6vXMzsKFC9W1a9ez2q+99lotWLDgoosCAABoKPUKOz6fT23btj2rvU2bNiotLb3oogAAABpKvcJOYmKitm/fflb79u3blZCQcNFFAQAANJR6PbPz4IMPasKECTp16pQGDRokSdqyZYsee+wxvqAMAABCSr3CzqRJk/TXv/5VP//5z1VTUyNJio6O1uTJk5WTk9OgBQIAAFyMeoUdh8OhZ555Ro8//rg+/vhjNW/eXJ06dVJUVFRD1wcAAHBR6hV2zmjZsqX69evXULUAAAA0uHo9oAwAAHCpIOwAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjGZr2Jk/f7569uwpp9Mpp9Mpr9erdevWWf1VVVXKyspSXFycWrZsqfT0dJWVlQXto6SkRGlpaWrRooXi4+M1adIknT59uqmnAgAAQpStYaddu3aaOXOmiouL9d5772nQoEG64447tG/fPknSxIkT9dZbb+nVV19VQUGBjh49qrvuusvavra2VmlpaaqpqdGOHTu0dOlSLVmyRFOnTrVrSgAAIMRE2Hnw22+/PWj96aef1vz587Vz5061a9dOixYt0vLlyzVo0CBJ0uLFi9WtWzft3LlTAwYM0MaNG7V//35t3rxZbrdbvXr10pNPPqnJkydr+vTpioyMPOdxq6urVV1dba37/f7GmyQAALBVyDyzU1tbqxUrVujkyZPyer0qLi7WqVOnlJKSYo3p2rWr2rdvr8LCQklSYWGhevToIbfbbY1JTU2V3++3rg6dS15enlwul7UkJiY23sQAAICtbA87e/fuVcuWLRUVFaWHHnpIq1atUnJysnw+nyIjIxUbGxs03u12y+fzSZJ8Pl9Q0DnTf6bvfHJyclRZWWktR44cadhJAQCAkGHrbSxJ6tKliz744ANVVlbqtddeU2ZmpgoKChr1mFFRUYqKimrUYwAAgNBge9iJjIzUNddcI0nq06ePioqK9Otf/1r33HOPampqVFFREXR1p6ysTB6PR5Lk8Xi0e/fuoP2deVvrzBgAAHB5s/021j+rq6tTdXW1+vTpo2bNmmnLli1W38GDB1VSUiKv1ytJ8nq92rt3r8rLy60xmzZtktPpVHJycpPXDgAAQo+tV3ZycnI0dOhQtW/fXsePH9fy5cu1detWbdiwQS6XS6NHj1Z2drZat24tp9Op8ePHy+v1asCAAZKkIUOGKDk5WaNGjdKsWbPk8/k0ZcoUZWVlcZsKAABIsjnslJeX695771VpaalcLpd69uypDRs26JZbbpEkzZ49W2FhYUpPT1d1dbVSU1M1b948a/vw8HCtWbNGY8eOldfrVUxMjDIzM5Wbm2vXlAAAQIixNewsWrToG/ujo6OVn5+v/Pz8847p0KGD1q5d29ClAQAAQ4TcMzsAAAANibADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKPZGnby8vLUr18/tWrVSvHx8Ro2bJgOHjwYNKaqqkpZWVmKi4tTy5YtlZ6errKysqAxJSUlSktLU4sWLRQfH69Jkybp9OnTTTkVAAAQomwNOwUFBcrKytLOnTu1adMmnTp1SkOGDNHJkyetMRMnTtRbb72lV199VQUFBTp69Kjuuusuq7+2tlZpaWmqqanRjh07tHTpUi1ZskRTp061Y0oAACDERNh58PXr1wetL1myRPHx8SouLtZNN92kyspKLVq0SMuXL9egQYMkSYsXL1a3bt20c+dODRgwQBs3btT+/fu1efNmud1u9erVS08++aQmT56s6dOnKzIy0o6pAQCAEBFSz+xUVlZKklq3bi1JKi4u1qlTp5SSkmKN6dq1q9q3b6/CwkJJUmFhoXr06CG3222NSU1Nld/v1759+855nOrqavn9/qAFAACYKWTCTl1dnSZMmKCBAweqe/fukiSfz6fIyEjFxsYGjXW73fL5fNaYfww6Z/rP9J1LXl6eXC6XtSQmJjbwbAAAQKgImbCTlZWljz76SCtWrGj0Y+Xk5KiystJajhw50ujHBAAA9rD1mZ0zxo0bpzVr1mjbtm1q166d1e7xeFRTU6OKioqgqztlZWXyeDzWmN27dwft78zbWmfG/LOoqChFRUU18CwAAEAosvXKTiAQ0Lhx47Rq1Sq988476tixY1B/nz591KxZM23ZssVqO3jwoEpKSuT1eiVJXq9Xe/fuVXl5uTVm06ZNcjqdSk5ObpqJAACAkGXrlZ2srCwtX75cv//979WqVSvrGRuXy6XmzZvL5XJp9OjRys7OVuvWreV0OjV+/Hh5vV4NGDBAkjRkyBAlJydr1KhRmjVrlnw+n6ZMmaKsrCyu3gAAAHvDzvz58yVJN998c1D74sWLdd9990mSZs+erbCwMKWnp6u6ulqpqamaN2+eNTY8PFxr1qzR2LFj5fV6FRMTo8zMTOXm5jbVNAAAQAizNewEAoFvHRMdHa38/Hzl5+efd0yHDh20du3ahiwNwGVs4AsD7S4B/2f7+O12lwADhMzbWAAAAI2BsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0WwNO9u2bdPtt9+uhIQEORwOrV69Oqg/EAho6tSpatu2rZo3b66UlBQdOnQoaMyxY8eUkZEhp9Op2NhYjR49WidOnGjCWQAAgFBma9g5efKkrrvuOuXn55+zf9asWZo7d64WLFigXbt2KSYmRqmpqaqqqrLGZGRkaN++fdq0aZPWrFmjbdu2acyYMU01BQAAEOIi7Dz40KFDNXTo0HP2BQIBzZkzR1OmTNEdd9whSfrNb34jt9ut1atXa8SIEfr444+1fv16FRUVqW/fvpKkF154Qbfddpv+/d//XQkJCU02FwAAEJpC9pmdw4cPy+fzKSUlxWpzuVzq37+/CgsLJUmFhYWKjY21go4kpaSkKCwsTLt27Trvvqurq+X3+4MWAABgppANOz6fT5LkdruD2t1ut9Xn8/kUHx8f1B8REaHWrVtbY84lLy9PLpfLWhITExu4egAAECpCNuw0ppycHFVWVlrLkSNH7C4JAAA0kpANOx6PR5JUVlYW1F5WVmb1eTwelZeXB/WfPn1ax44ds8acS1RUlJxOZ9ACAADMFLJhp2PHjvJ4PNqyZYvV5vf7tWvXLnm9XkmS1+tVRUWFiouLrTHvvPOO6urq1L9//yavGQAAhB5b38Y6ceKEPvnkE2v98OHD+uCDD9S6dWu1b99eEyZM0FNPPaVOnTqpY8eOevzxx5WQkKBhw4ZJkrp166Zbb71VDz74oBYsWKBTp05p3LhxGjFiBG9iAQAASTaHnffee08//OEPrfXs7GxJUmZmppYsWaLHHntMJ0+e1JgxY1RRUaEbb7xR69evV3R0tLXNsmXLNG7cOA0ePFhhYWFKT0/X3Llzm3wuAAAgNNkadm6++WYFAoHz9jscDuXm5io3N/e8Y1q3bq3ly5c3RnkAAMAAIfvMDgAAQEMg7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNGPCTn5+vpKSkhQdHa3+/ftr9+7ddpcEAABCgBFh57/+67+UnZ2tadOm6Y9//KOuu+46paamqry83O7SAACAzYwIO88//7wefPBB3X///UpOTtaCBQvUokULvfLKK3aXBgAAbBZhdwEXq6amRsXFxcrJybHawsLClJKSosLCwnNuU11drerqamu9srJSkuT3+y/4uLXVf6tnxWho3+W81dfxqtpGPwYuTFOc79N/O93ox8CFaYrzffI05ztUfNfzfWZ8IBD4xnGXfNj5y1/+otraWrnd7qB2t9utAwcOnHObvLw8PfHEE2e1JyYmNkqNaFyuFx6yuwQ0pTyX3RWgCbkmc74vK676ne/jx4/L9Q3bXvJhpz5ycnKUnZ1trdfV1enYsWOKi4uTw+GwsbKm5ff7lZiYqCNHjsjpdNpdDhoZ5/vywvm+vFyu5zsQCOj48eNKSEj4xnGXfNi58sorFR4errKysqD2srIyeTyec24TFRWlqKiooLbY2NjGKjHkOZ3Oy+p/jssd5/vywvm+vFyO5/ubruiccck/oBwZGak+ffpoy5YtVltdXZ22bNkir9drY2UAACAUXPJXdiQpOztbmZmZ6tu3r66//nrNmTNHJ0+e1P333293aQAAwGZGhJ177rlHX375paZOnSqfz6devXpp/fr1Zz20jGBRUVGaNm3aWbf0YCbO9+WF83154Xx/M0fg297XAgAAuIRd8s/sAAAAfBPCDgAAMBphBwAAGI2wgyCfffaZHA6HPvjgA7tLAQCgQRB2bHTffffJ4XBo5syZQe2rV6/+zl9yTkpK0pw5cy66psTERJWWlqp79+4XvS/Yb/r06XI4HEFL165d7S4LjWT+/Pnq2bOn9WE5r9erdevW2V0WmsjMmTPlcDg0YcIEu0sJOYQdm0VHR+uZZ57RV199ZXcpkqTw8HB5PB5FRBjxVQJIuvbaa1VaWmot//3f/213SWgk7dq108yZM1VcXKz33ntPgwYN0h133KF9+/bZXRoaWVFRkRYuXKiePXvaXUpIIuzYLCUlRR6PR3l5ed847vXXX9e1116rqKgoJSUl6bnnnrP6br75Zn3++eeaOHGi9a/38zlw4IBuvPFGRUdHKzk5WZs3b5bD4dDq1aslBd/GqqurU7t27TR//vygfbz//vsKCwvT559/LkmqqKjQT3/6U7Vp00ZOp1ODBg3Snj17rPHTp09Xr1699Nvf/lZJSUlyuVwaMWKEjh8//l3/uFAPERER8ng81nLllVfaXRIaye23367bbrtNnTp1UufOnfX000+rZcuW2rlzp92loRGdOHFCGRkZevnll3XFFVfYXU5IIuzYLDw8XDNmzNALL7ygL7744pxjiouLdffdd2vEiBHau3evpk+frscff1xLliyRJL3xxhtq166dcnNzrX+9n0ttba2GDRumFi1aaNeuXXrppZf0q1/96ry1hYWFaeTIkVq+fHlQ+7JlyzRw4EB16NBBkjR8+HCVl5dr3bp1Ki4uVu/evTV48GAdO3bM2ubTTz/V6tWrtWbNGq1Zs0YFBQVn3b5D4zh06JASEhJ01VVXKSMjQyUlJXaXhCZQW1urFStW6OTJk/zqHMNlZWUpLS1NKSkpdpcSugKwTWZmZuCOO+4IBAKBwIABAwIPPPBAIBAIBFatWhX4x1Pzk5/8JHDLLbcEbTtp0qRAcnKytd6hQ4fA7Nmzv/F469atC0RERARKS0uttk2bNgUkBVatWhUIBAKBw4cPByQF3n///UAgEAi8//77AYfDEfj8888DgUAgUFtbG/je974XmD9/fiAQCATefffdgNPpDFRVVQUd6+qrrw4sXLgwEAgEAtOmTQu0aNEi4Pf7g+rv37//N9aLi7d27drAypUrA3v27AmsX78+4PV6A+3btw86FzDLhx9+GIiJiQmEh4cHXC5X4O2337a7JDSi//zP/wx079498Le//S0QCAQCP/jBDwIPP/ywvUWFIK7shIhnnnlGS5cu1ccff3xW38cff6yBAwcGtQ0cOFCHDh1SbW3tBR/j4MGDSkxMDPpt8Ndff/03btOrVy9169bNurpTUFCg8vJyDR8+XJK0Z88enThxQnFxcWrZsqW1HD58WJ9++qm1n6SkJLVq1cpab9u2rcrLyy+4dtTP0KFDNXz4cPXs2VOpqalau3atKioqtHLlSrtLQyPp0qWLPvjgA+3atUtjx45VZmam9u/fb3dZaARHjhzRww8/rGXLlik6OtruckIaT6GGiJtuukmpqanKycnRfffdZ3c5QTIyMrR8+XL98pe/1PLly3XrrbcqLi5O0t/vFbdt21Zbt249a7vY2Fjr52bNmgX1ORwO1dXVNWbZOIfY2Fh17txZn3zyid2loJFERkbqmmuukST16dNHRUVF+vWvf62FCxfaXBkaWnFxscrLy9W7d2+rrba2Vtu2bdOLL76o6upqhYeH21hh6CDshJCZM2eqV69e6tKlS1B7t27dtH379qC27du3q3PnztZ/yJGRkd96ladLly46cuSIysrKrF+SWlRU9K11/eQnP9GUKVNUXFys1157TQsWLLD6evfuLZ/Pp4iICCUlJV3INGGjEydO6NNPP9WoUaPsLgVNpK6uTtXV1XaXgUYwePBg7d27N6jt/vvvV9euXTV58mSCzj8g7ISQHj16KCMjQ3Pnzg1qf+SRR9SvXz89+eSTuueee1RYWKgXX3xR8+bNs8YkJSVp27ZtGjFihKKios75xs0tt9yiq6++WpmZmZo1a5aOHz+uKVOmSNI3vsGVlJSkG264QaNHj1Ztba3+9V//1epLSUmR1+vVsGHDNGvWLHXu3FlHjx7V22+/rTvvvFN9+/a92D8WXIRHH31Ut99+uzp06KCjR49q2rRpCg8P18iRI+0uDY0gJydHQ4cOVfv27XX8+HEtX75cW7du1YYNG+wuDY2gVatWZ30TLSYmRnFxcXwr7Z/wzE6Iyc3NPev2Tu/evbVy5UqtWLFC3bt319SpU5Wbmxt0uys3N1efffaZrr76arVp0+ac+w4PD9fq1at14sQJ9evXTz/96U+tt7G+7X5vRkaG9uzZozvvvFPNmze32h0Oh9auXaubbrpJ999/vzp37qwRI0bo888/t64ewT5ffPGFRo4cqS5duujuu+9WXFycdu7ced7/RnBpKy8v17333qsuXbpo8ODBKioq0oYNG3TLLbfYXRpgK0cgEAjYXQTss337dt1444365JNPdPXVV9tdDgAADY6wc5lZtWqVWrZsqU6dOumTTz7Rww8/rCuuuIKv6gIAjMUzO5eZ48ePa/LkySopKdGVV16plJSUoK8xAwBgGq7sAAAAo/GAMgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2ABgtKSlJc+bMsbsMADYi7AAwwpIlSxQbG3tWe1FRkcaMGdP0BQEIGXxUEEDIq6mpUWRkZL225feAAeDKDoCQc/PNN2vcuHGaMGGCrrzySqWmpur5559Xjx49FBMTo8TERP385z/XiRMnJElbt27V/fffr8rKSjkcDjkcDk2fPl3S2bexHA6H/uM//kN33nmnWrRooU6dOunNN98MOv6bb76pTp06KTo6Wj/84Q+1dOlSORwOVVRUNNGfAICGRNgBEJKWLl2qyMhIbd++XQsWLFBYWJjmzp2rffv2aenSpXrnnXf02GOPSZJuuOEGzZkzR06nU6WlpSotLdWjjz563n0/8cQTuvvuu/Xhhx/qtttuU0ZGho4dOyZJOnz4sH784x9r2LBh2rNnj372s5/pV7/6VZPMGUDj4DYWgJDUqVMnzZo1y1rv0qWL9XNSUpKeeuopPfTQQ5o3b54iIyPlcrnkcDjk8Xi+dd/33XefRo4cKUmaMWOG5s6dq927d+vWW2/VwoUL1aVLFz377LPWcT/66CM9/fTTDTxDAE2FsAMgJPXp0ydoffPmzcrLy9OBAwfk9/t1+vRpVVVV6euvv1aLFi2+07579uxp/RwTEyOn06ny8nJJ0sGDB9WvX7+g8ddff309ZwEgFHAbC0BIiomJsX7+7LPP9KMf/Ug9e/bU66+/ruLiYuXn50v6+8PL31WzZs2C1h0Oh+rq6i6uYAAhiys7AEJecXGx6urq9Nxzzyks7O//Rlu5cmXQmMjISNXW1l70sbp06aK1a9cGtRUVFV30fgHYhys7AELeNddco1OnTumFF17Qn//8Z/32t7/VggULgsYkJSXpxIkT2rJli/7yl7/o66+/rtexfvazn+nAgQOaPHmy/vSnP2nlypVasmSJpL9fAQJw6SHsAAh51113nZ5//nk988wz6t69u5YtW6a8vLygMTfccIMeeugh3XPPPWrTpk3Qw83fRceOHfXaa6/pjTfeUM+ePTV//nzrbayoqKiLnguApucIBAIBu4sAgFD29NNPa8GCBTpy5IjdpQCoB57ZAYB/Mm/ePPXr109xcXHavn27nn32WY0bN87usgDUE2EHAP7JoUOH9NRTT+nYsWNq3769HnnkEeXk5NhdFoB64jYWAAAwGg8oAwAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABG+1+Z1OOlVzshtwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# exploring distribution of preparation time\n",
        "sns.boxplot(data = df, x = 'food_preparation_time')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "O-QDa_MNzNqs",
        "outputId": "09b0c72a-466c-4574-83ec-e91f01ffd5bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='food_preparation_time'>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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cfvvtMWPGjJLM2h06+u/5a1/7Wrz++uvx0EMPxeGHHx733ntvnH/++fHII4/E8ccfX6Jp992YMWPi6aefjjfeeCPuvvvuuOiii2LRokWlHqvHdLTeY489trhNuR2rOlrzypUry/Z4tVelLpTLL788NTQ0pJdeeqnd5QsWLEgRkTZs2NDu8sbGxvSf//mfB3DC7tXRetts3LgxNTU1pUmTJqWtW7ce4Om6X0frvfrqq1OhUEh9+/YtfkRE6tOnTzrjjDNKM2w36Gi9L730UoqI9L3vfa/d5eeff376xCc+cSBH7FYdrXflypUpItKzzz7b7vJJkyalz33ucwdyxB4zadKkdPHFF5ftsWp3bettU27Hqj1pW3O5Hq86U7JzEFJKccUVV8S8efPi4YcfjiOOOKLd9SeffHL069cvFixYULxs+fLlsWbNmg7fB+vNOltvxJ9r/KyzzoqKioq47777oqqqqgSTdo/O1jt16tR45pln4umnny5+RETcfPPNcccdd5Rg4v3T2XpHjx4dI0aMyH4UcMWKFTFq1KgDOWq36Gy9W7ZsiYjIfkKjb9++xVdTDnatra2xbdu2sjtWdaRtvRHldazam7Y1l9vxqstKVSaXXnppGjBgQFq4cGFat25d8WPLli3FbS655JLU2NiYHn744fTkk0+mpqam1NTUVKqR90tn633jjTfShAkT0vHHH59WrlzZbpsdO3aUePq3riv7d3dxEL+n15X13nzzzam+vj7NnTs3vfjii+n6669PVVVVaeXKlSWcfN90tt7t27eno48+Op1++unpscceSytXrkzf/OY3U6FQ6PC97N5s6tSpadGiRWn16tXpmWeeSVOnTk2FQiH99Kc/TSmV17Eqpb2vt9yOVW0628e7O5iPV11VskCIiD1+3HHHHcVttm7dmi677LI0cODAVFNTkz7ykY+kdevWlWrk/dLZen/2s591uM3q1atLOvu+6Mr+3dNtDtb/4bq63hkzZqSGhoZUU1OTmpqa0iOPPFKagfdTV9a7YsWKdN5556UhQ4akmpqadMIJJ2Q/9niw+Id/+Ic0atSoVFFRkd7+9renSZMmtXviKKdjVUp7X2+5HavadLaPd3cwH6+6yl9zBAAyveb3IAAAvYdAAAAyAgEAyAgEACAjEACAjEAAADICAQDICAQAICMQKHsppbj44otj0KBBUSgUir9HvbudeeaZcc011/TIfR/sPvOZz8S5555b6jGiUCjEvffeW+ox4KDQK/7cM/Sk+fPnx6xZs2LhwoVx5JFHxuGHH17qkcrWb37zmzjiiCNi6dKlceKJJxYv/9a3vhUH8pe23nDDDXHvvfdmMbhu3boYOHDgAZsDDmYCgbK3atWqGD58eEycOLHUo+yXlpaW6NevX0kee/v27VFRUbHPtx8wYEA3TrPvhg0bVuoR4KDhLQbK2mc+85m48sorY82aNVEoFGL06NGxbdu2uOqqq2LIkCFRVVUVp512WjzxxBPtbrdo0aI45ZRTorKyMoYPHx5Tp06NHTt2FK9/880348ILL4z+/fvH8OHD4z/+4z/e0lyjR4+OG2+8MT7+8Y9HbW1tvOMd74hbb7213TaFQiG+853vxIc+9KGora2Nf/7nf46IiB/+8Idx0kknRVVVVRx55JExffr0drO13W7y5MlRXV0dRx55ZNx9993t7vtLX/pS/NVf/VXU1NTEkUceGV/72teipaWleP0NN9wQJ554Ytx2221xxBFHFP+c7/z58+O0006Lt73tbTF48OA455xzYtWqVcXbtf3Z5/e85z1RKBTizDPPLO6HXd9i6GwfLFy4MAqFQixYsCDGjRsXNTU1MXHixOzPZe/JrFmzYvr06bFs2bIoFApRKBRi1qxZxe9N21sMv/nNb6JQKMRdd90Vp59+elRXV8f48eNjxYoV8cQTT8S4ceOif//+MXny5Hj11VfbPcZtt90WY8eOjaqqqjjmmGPiv/7rvzqdCw46pf1bUdCzXn/99fSNb3wjNTQ0pHXr1qX169enq666Ko0YMSL9+Mc/Ts8991y66KKL0sCBA9Nrr72WUkrplVdeSTU1Nemyyy5LL7zwQpo3b146/PDD07Rp04r3e+mll6bGxsb00EMPpWeeeSadc845qa6uLl199dVdmmvUqFGprq4uzZgxIy1fvjx9+9vfTn379m331+MiIg0ZMiTdfvvtadWqVenll19OP//5z1N9fX2aNWtWWrVqVfrpT3+aRo8enW644YZ2txs8eHCaOXNmWr58ebr++utT37590/PPP1/c5sYbb0yLFy9Oq1evTvfdd18aOnRo+rd/+7fi9dOmTUu1tbXp7LPPTk899VRatmxZSimlu+++O91zzz3pxRdfTEuXLk0f/OAH0/HHH5927tyZUkrp8ccfTxGRHnroobRu3bri9/Siiy5KH/7wh4v339k+aPuLgRMmTEgLFy5Mzz33XDr99NPTxIkTO/3ebtmyJX3hC19Ixx13XPZnqGOXv8C3evXqFBHpmGOOSfPnz0/PP/98eu9735tOPvnkdOaZZ6ZHH300PfXUU+noo49Ol1xySfH+v//976fhw4ene+65J7300kvpnnvuSYMGDUqzZs3q0r6Hg4VAoOzdfPPNadSoUSmllDZv3pz69euXZs+eXbx++/btacSIEemmm25KKaX0la98JY0ZMya1trYWt7n11ltT//79086dO9OmTZtSRUVFuuuuu4rXv/baa6m6uvotBcLZZ5/d7rILLrggTZ48ufh1RKRrrrmm3TaTJk1K//Iv/9Lusu9973tp+PDh7W636xNaSilNmDAhXXrppR3O8+///u/p5JNPLn49bdq01K9fv7R+/fq9ruPVV19NEZF+/etfp5T+8qS7dOnSdtvtGghd2QdtgfDQQw8Vt7n//vtTRKStW7fudaa2+d/97ndnl+8pEG677bbi9T/4wQ9SRKQFCxYUL5sxY0YaM2ZM8eujjjoq/e///m+7+73xxhtTU1NTp3PBwcQ5CBxSVq1aFS0tLXHqqacWL+vXr1+ccsop8cILL0RExAsvvBBNTU1RKBSK25x66qmxefPmeOWVV2LDhg2xffv2mDBhQvH6QYMGxZgxY97SLE1NTdnXt9xyS7vLxo0b1+7rZcuWxeLFi4tvN0RE7Ny5M5qbm2PLli1RU1PT4X3vesLenXfeGd/+9rdj1apVsXnz5tixY0fU19e3u82oUaPi7W9/e7vLXnzxxfj6178ejz32WPzxj3+M1tbWiIhYs2ZNvOtd7+rSuruyD9qccMIJxc+HDx8eERHr16+PxsbGLj1WV+z6GEOHDo2IiOOPP77dZevXr4+IP7+1tGrVqpgyZUr84z/+Y3GbHTt29JrzLKC7CAToxWpra9t9vXnz5pg+fXqcd9552bZt5wl0ZsmSJfHJT34ypk+fHn/7t38bAwYMiDlz5mTnUez+2BERH/zgB2PUqFExc+bMGDFiRLS2tsa73vWu2L59+1tYVdftelJmW7C1RUlPPsbul7U95ubNmyMiYubMme0CMSKib9++3ToXlJqTFDmkHHXUUVFRURGLFy8uXtbS0hJPPPFEHHvssRERMXbs2FiyZEm7H8tbvHhx1NXVRUNDQxx11FHRr1+/eOyxx4rXb9iwIVasWPGWZvnlL3+ZfT127Ni93uakk06K5cuXx9FHH5199Onzl/+d93bfv/jFL2LUqFHx1a9+NcaNGxfvfOc74+WXX+503tdeey2WL18e119/fUyaNCnGjh0bGzZsaLdN20867Ny5s8P76co+2F8VFRV7nWFfDR06NEaMGBEvvfRS9v1vO0ETyoVXEDik1NbWxqWXXhrXXXddDBo0KBobG+Omm26KLVu2xJQpUyIi4rLLLotbbrklrrzyyrjiiiti+fLlMW3atLj22mujT58+0b9//5gyZUpcd911MXjw4BgyZEh89atfbfcE3RWLFy+Om266Kc4999x48MEHY+7cuXH//ffv9TZf//rX45xzzonGxsb4u7/7u+jTp08sW7Ysnn322finf/qn4nZz586NcePGxWmnnRazZ8+Oxx9/PP77v/87IiLe+c53xpo1a2LOnDkxfvz4uP/++2PevHmdzjtw4MAYPHhwfPe7343hw4fHmjVrYurUqe22GTJkSFRXV8f8+fOjoaEhqqqqspfeu7IP9tfo0aNj9erV8fTTT0dDQ0PU1dVFZWVlt9z39OnT46qrrooBAwbE2WefHdu2bYsnn3wyNmzYENdee223PAb0CqU+CQJ62q4nKaaU0tatW9OVV16ZDj/88FRZWZlOPfXU9Pjjj7e7zcKFC9P48eNTRUVFGjZsWPrSl76UWlpaitdv2rQpfepTn0o1NTVp6NCh6aabbkpnnHHGWzpJcfr06eljH/tYqqmpScOGDUvf+ta32m0Tu5xQt6v58+eniRMnpurq6lRfX59OOeWU9N3vfrfd7W699db0N3/zN6mysjKNHj063Xnnne3u47rrrkuDBw9O/fv3TxdccEG6+eab04ABA4rXd3SS34MPPpjGjh2bKisr0wknnJAWLlyYzTlz5sw0cuTI1KdPn3TGGWeklPKfYuhsH7SdpLhhw4biZUuXLk0RkVavXt3h97VNc3Nz+uhHP5re9ra3pYhId9xxR/F7s/tJirueULmnx73jjjvafW9SSmn27NnpxBNPTBUVFWngwIHpfe97X/q///u/TueCg0khpQP4682AiPjzv3CvueaaHvnVzIVCIebNm9crfrUxcPByDgIAkBEI0M0eeeSR6N+/f4cf7L/jjjuuw+/v7NmzSz0elAVvMUA327p1a/zud7/r8Pqjjz76AE5Tnl5++eV2vxp6V0OHDo26uroDPBGUH4EAAGS8xQAAZAQCAJARCABARiAAABmBAABkBAIAkBEIAEBGIAAAmf8H5yL7PClFh6UAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# exploring distribution of delivery time\n",
        "sns.boxplot(data = df, x = 'delivery_time')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "AiuG9d4w0zsa",
        "outputId": "4ad79f7b-16b5-43dd-da5d-3f8c9cc27765"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='delivery_time'>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K9CqA5H_JY_U"
      },
      "source": [
        "### **Question 7**: Which are the top 5 restaurants in terms of the number of orders received?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PaJZ3wlOJY_U",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cbd24edd-18de-41c0-ed81-d5d4047ae5d4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "restaurant_name            restaurant_name          \n",
              "Shake Shack                Shake Shack                  219\n",
              "The Meatball Shop          The Meatball Shop            132\n",
              "Blue Ribbon Sushi          Blue Ribbon Sushi            119\n",
              "Blue Ribbon Fried Chicken  Blue Ribbon Fried Chicken     96\n",
              "Parm                       Parm                          68\n",
              "Name: restaurant_name, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "# grouping restaurant names, counting them, then displaying the top 5 restaurants based on the count\n",
        "df['restaurant_name'].groupby(df['restaurant_name']).value_counts().nlargest(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tnjhWgUfJY_U"
      },
      "source": [
        "#### Observations: The top 5 restaurants by number of orders are Shake Shack, The Meatball Shop, Blue Ribbon Sushi, Blue Ribbon Fried Chicken, and Parm.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gWPMqGq0JY_U"
      },
      "source": [
        "### **Question 8**: Which is the most popular cuisine on weekends?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c-Xnus3VJY_U",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "18a6ec59-4999-4c80-eeae-77e50778f54b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "day_of_the_week  cuisine_type\n",
              "Weekend          American        415\n",
              "                 Japanese        335\n",
              "                 Italian         207\n",
              "Weekday          American        169\n",
              "Weekend          Chinese         163\n",
              "Name: cuisine_type, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "df['cuisine_type'].groupby(df['day_of_the_week']).value_counts().nlargest(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bpQ270TUJY_U"
      },
      "source": [
        "#### Observations: The most popular cuisine on the weekend is American.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "88xgTqkxJY_U"
      },
      "source": [
        "### **Question 9**: What percentage of the orders cost more than 20 dollars?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OTpHyTdzJY_U",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "24e574b1-6dca-4b42-8573-a7620ed411de"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    0.707587\n",
              "True     0.292413\n",
              "Name: cost_of_the_order, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "# calculating percentage of orders over $20\n",
        "over_20 = df['cost_of_the_order'] > 20\n",
        "\n",
        "over_20.value_counts(normalize = True) # normalizing to observe the ratio of T:F as a number between 0 and 1 rather than counting integers, which gives us our percentage."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "77KNAC5zJY_U"
      },
      "source": [
        "#### Observations: ~29.2% of orders cost over $20.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HFyHn7hsJY_V"
      },
      "source": [
        "### **Question 10**: What is the mean order delivery time?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cyFL13hNJY_V",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fbbd0fce-8543-4c42-ca93-28427631d045"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24.161749209694417"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "# calculating the mean delivery time\n",
        "df['delivery_time'].mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F35SRV-HJY_V"
      },
      "source": [
        "#### Observations: The average delivery time is 24.16 minutes, or 24 minutes and 9.72 seconds.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "48IjaOuDJY_V"
      },
      "source": [
        "### **Question 11:** The company has decided to give 20% discount vouchers to the top 3 most frequent customers. Find the IDs of these customers and the number of orders they placed."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Hz8-DE4Zge0j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22f283fa-f7a0-4ccb-c590-e4e0d0e2a19e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "customer_id  customer_id\n",
              "52832        52832          13\n",
              "47440        47440          10\n",
              "83287        83287           9\n",
              "Name: customer_id, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "# finding the most frequent customers by grouping by most frequently occurring customer ID number\n",
        "df['customer_id'].groupby(df['customer_id']).value_counts().nlargest(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9tWVwfX6JY_V"
      },
      "source": [
        "#### Observations: Top 3 customers' IDs are 52832, 47440, and 83287.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AfIc2jPaJY_V"
      },
      "source": [
        "### Multivariate Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iqiS0RT_JY_V"
      },
      "source": [
        "### **Question 12**: Perform a multivariate analysis to explore relationships between the important variables in the dataset.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3qt4MCNTJY_V",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "outputId": "c0dab3b7-9f86-4159-bc76-b753f2c16cd7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x78843c71ae30>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 7117x500 with 14 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# exploring potential relationships between delivery time, the day of the week, rating by the cuisine type\n",
        "sns.catplot(data = df, x = 'day_of_the_week', y = 'delivery_time', hue = 'rating', col = 'cuisine_type', kind = 'box', palette = 'pastel')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# exploring potential relationships between the cost of an order, order volume, and the type of cuisine by time of the week\n",
        "sns.catplot(data = df, x = 'cost_of_the_order', y = 'cuisine_type', hue = 'day_of_the_week', col = 'day_of_the_week', kind = 'bar', palette = 'pastel')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "s8pcKX-jYeJt",
        "outputId": "870298e7-56b0-481f-9cde-104c7b1360dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x788437eadd80>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# exploring the potential relationship between the time of the week, how long it takes to prep food, and whether that influences delivery times\n",
        "sns.jointplot(data = df, x = 'delivery_time', y = 'food_preparation_time', hue = 'day_of_the_week')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "id": "IWmGoP7yidGD",
        "outputId": "cdd9622b-4cba-4977-cddb-ce3e9813d868"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7884398cde40>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YLkMf09YJY_V"
      },
      "source": [
        "### **Question 13:** The company wants to provide a promotional offer in the advertisement of the restaurants. The condition to get the offer is that the restaurants must have a rating count of more than 50 and the average rating should be greater than 4. Find the restaurants fulfilling the criteria to get the promotional offer."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# first need to convert rating to an integer by removing\n",
        "df2 = df.drop(df[df['rating'] == 'Not given'].index) # using new df called df2 to avoid messing with original data set\n",
        "\n",
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "JZnX-o6mG3Mt",
        "outputId": "0d0991a4-3aea-4aaf-8d0a-0fcfe88a7bfd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      order_id  customer_id                        restaurant_name  \\\n",
              "2      1477070        66393                            Cafe Habana   \n",
              "3      1477334       106968              Blue Ribbon Fried Chicken   \n",
              "4      1478249        76942                       Dirty Bird to Go   \n",
              "5      1477224       147468                       Tamarind TriBeCa   \n",
              "7      1477859        89574                              Barbounia   \n",
              "...        ...          ...                                    ...   \n",
              "1889   1478190        94152                       RedFarm Broadway   \n",
              "1890   1477316       164776                                    TAO   \n",
              "1893   1476701       292602  Chipotle Mexican Grill $1.99 Delivery   \n",
              "1894   1477421       397537                              The Smile   \n",
              "1896   1477513        64151                      Jack's Wife Freda   \n",
              "\n",
              "       cuisine_type  cost_of_the_order day_of_the_week rating  \\\n",
              "2           Mexican              12.23         Weekday      5   \n",
              "3          American              29.20         Weekend      3   \n",
              "4          American              11.59         Weekday      4   \n",
              "5            Indian              25.22         Weekday      3   \n",
              "7     Mediterranean               5.97         Weekday      3   \n",
              "...             ...                ...             ...    ...   \n",
              "1889        Chinese               8.68         Weekday      3   \n",
              "1890       Japanese              15.67         Weekend      5   \n",
              "1893        Mexican              22.31         Weekend      5   \n",
              "1894       American              12.18         Weekend      5   \n",
              "1896  Mediterranean              12.18         Weekday      5   \n",
              "\n",
              "      food_preparation_time  delivery_time  \n",
              "2                        23             28  \n",
              "3                        25             15  \n",
              "4                        25             24  \n",
              "5                        20             24  \n",
              "7                        33             30  \n",
              "...                     ...            ...  \n",
              "1889                     33             30  \n",
              "1890                     20             22  \n",
              "1893                     31             17  \n",
              "1894                     31             19  \n",
              "1896                     23             31  \n",
              "\n",
              "[1162 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b1529297-d6d4-4b8f-a718-91d38ee9b478\" 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>order_id</th>\n",
              "      <th>customer_id</th>\n",
              "      <th>restaurant_name</th>\n",
              "      <th>cuisine_type</th>\n",
              "      <th>cost_of_the_order</th>\n",
              "      <th>day_of_the_week</th>\n",
              "      <th>rating</th>\n",
              "      <th>food_preparation_time</th>\n",
              "      <th>delivery_time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1477070</td>\n",
              "      <td>66393</td>\n",
              "      <td>Cafe Habana</td>\n",
              "      <td>Mexican</td>\n",
              "      <td>12.23</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>5</td>\n",
              "      <td>23</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1477334</td>\n",
              "      <td>106968</td>\n",
              "      <td>Blue Ribbon Fried Chicken</td>\n",
              "      <td>American</td>\n",
              "      <td>29.20</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>3</td>\n",
              "      <td>25</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1478249</td>\n",
              "      <td>76942</td>\n",
              "      <td>Dirty Bird to Go</td>\n",
              "      <td>American</td>\n",
              "      <td>11.59</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>4</td>\n",
              "      <td>25</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1477224</td>\n",
              "      <td>147468</td>\n",
              "      <td>Tamarind TriBeCa</td>\n",
              "      <td>Indian</td>\n",
              "      <td>25.22</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>3</td>\n",
              "      <td>20</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1477859</td>\n",
              "      <td>89574</td>\n",
              "      <td>Barbounia</td>\n",
              "      <td>Mediterranean</td>\n",
              "      <td>5.97</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>3</td>\n",
              "      <td>33</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1889</th>\n",
              "      <td>1478190</td>\n",
              "      <td>94152</td>\n",
              "      <td>RedFarm Broadway</td>\n",
              "      <td>Chinese</td>\n",
              "      <td>8.68</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>3</td>\n",
              "      <td>33</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1890</th>\n",
              "      <td>1477316</td>\n",
              "      <td>164776</td>\n",
              "      <td>TAO</td>\n",
              "      <td>Japanese</td>\n",
              "      <td>15.67</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>5</td>\n",
              "      <td>20</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1893</th>\n",
              "      <td>1476701</td>\n",
              "      <td>292602</td>\n",
              "      <td>Chipotle Mexican Grill $1.99 Delivery</td>\n",
              "      <td>Mexican</td>\n",
              "      <td>22.31</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>5</td>\n",
              "      <td>31</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1894</th>\n",
              "      <td>1477421</td>\n",
              "      <td>397537</td>\n",
              "      <td>The Smile</td>\n",
              "      <td>American</td>\n",
              "      <td>12.18</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>5</td>\n",
              "      <td>31</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1896</th>\n",
              "      <td>1477513</td>\n",
              "      <td>64151</td>\n",
              "      <td>Jack's Wife Freda</td>\n",
              "      <td>Mediterranean</td>\n",
              "      <td>12.18</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>5</td>\n",
              "      <td>23</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1162 rows × 9 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b1529297-d6d4-4b8f-a718-91d38ee9b478')\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "    .colab-df-convert {\n",
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              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b1529297-d6d4-4b8f-a718-91d38ee9b478 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-b1529297-d6d4-4b8f-a718-91d38ee9b478');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-aa3233ba-e968-4e99-938e-d86c63dcdb38\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-aa3233ba-e968-4e99-938e-d86c63dcdb38')\"\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",
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              "    </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",
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              "      --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",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    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",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    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-aa3233ba-e968-4e99-938e-d86c63dcdb38 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "Z-35t3t3gE5I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# converting to integer\n",
        "df2['rating'] = pd.to_numeric(df2['rating'])\n",
        "\n",
        "# confirming numeric conversion by pulling stat summary (originally wasn't available without selecting include = 'all')\n",
        "df2['rating'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FTcpgZAMImyB",
        "outputId": "b3401fc2-8574-46f7-896d-52f53363e89c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    1162.000000\n",
              "mean        4.344234\n",
              "std         0.741478\n",
              "min         3.000000\n",
              "25%         4.000000\n",
              "50%         5.000000\n",
              "75%         5.000000\n",
              "max         5.000000\n",
              "Name: rating, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "woNEC7zkgGJh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# assessing which restaurants meet the > 50 ratings criteria -- since 'Not given' was removed, I can count the companies and know that they each have a corresponding rating\n",
        "df2['restaurant_name'].groupby(df2['restaurant_name']).value_counts().nlargest(10) > 50"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZwBXCyxGYugM",
        "outputId": "a68c9a46-51af-45d8-9452-82b985b9fcea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "restaurant_name            restaurant_name          \n",
              "Shake Shack                Shake Shack                   True\n",
              "The Meatball Shop          The Meatball Shop             True\n",
              "Blue Ribbon Sushi          Blue Ribbon Sushi             True\n",
              "Blue Ribbon Fried Chicken  Blue Ribbon Fried Chicken     True\n",
              "RedFarm Broadway           RedFarm Broadway             False\n",
              "Parm                       Parm                         False\n",
              "RedFarm Hudson             RedFarm Hudson               False\n",
              "TAO                        TAO                          False\n",
              "Rubirosa                   Rubirosa                     False\n",
              "Han Dynasty                Han Dynasty                  False\n",
              "Name: restaurant_name, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# appyling ratings average > 4 rule to the restaurants with over 50 ratings\n",
        "promo = pd.DataFrame(df2['rating'].groupby(df2['restaurant_name']).mean())"
      ],
      "metadata": {
        "id": "RB5eyn2eTTgq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# selecting restaurants that meet 50+ ratings a 4+ score\n",
        "promogroup = promo.loc[['Shake Shack', 'The Meatball Shop', 'Blue Ribbon Sushi', 'Blue Ribbon Fried Chicken']]\n",
        "\n",
        "promogroup"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "dSx1ry4spIKA",
        "outputId": "c8d26f47-81bf-40d5-f7ad-f756892fbefc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             rating\n",
              "restaurant_name                    \n",
              "Shake Shack                4.278195\n",
              "The Meatball Shop          4.511905\n",
              "Blue Ribbon Sushi          4.219178\n",
              "Blue Ribbon Fried Chicken  4.328125"
            ],
            "text/html": [
              "\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
              "<div id=\"df-7149d799-e8ec-4a6c-b2c3-f0175306f358\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7149d799-e8ec-4a6c-b2c3-f0175306f358')\"\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-7149d799-e8ec-4a6c-b2c3-f0175306f358 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "10uryUMhJY_V"
      },
      "source": [
        "#### Observations: Restaurants meeting the > 50 ratings + an average rating over 4 starts criteria are: The Meatball Shop, Shake Shack, Blue Ribbon Sushi, and Blue Ribbon Fried Chicken.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1gvWBkRUJY_V"
      },
      "source": [
        "### **Question 14:** The company charges the restaurant 25% on the orders having cost greater than 20 dollars and 15% on the orders having cost greater than 5 dollars. Find the net revenue generated by the company across all orders."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rEjbnTNEJY_W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "262d966b-8bc1-467a-b757-d31537e0fba1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    1162.000000\n",
              "mean       16.760766\n",
              "std         7.572578\n",
              "min         4.470000\n",
              "25%        12.130000\n",
              "50%        14.600000\n",
              "75%        22.750000\n",
              "max        35.410000\n",
              "Name: cost_of_the_order, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "# converting cost to float value to create calculated field\n",
        "df2['cost_of_the_order'] = pd.to_numeric(df2['cost_of_the_order'])\n",
        "\n",
        "df2['cost_of_the_order'].describe()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# adding an empty column for revenue calculation\n",
        "df2['total_cost'] = ''\n",
        "\n",
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "wl_zfuFLJAFj",
        "outputId": "40ef0897-f8ac-4d15-e294-095362d49030"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3802\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\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   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\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/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'total_fees'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-63-3b7eeb5c8368>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# adding an empty column for revenue calculation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'total_cost'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'total_fees'\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      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mdf2\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/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3805\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3807\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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   3808\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3809\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\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;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3804\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3805\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'total_fees'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# applying calculation for total revenue\n",
        "def revenue(df2):\n",
        "    if df2['cost_of_the_order'] > 20:\n",
        "        return (df2['cost_of_the_order'] + (df2['cost_of_the_order'] * 0.25))\n",
        "    elif df2['cost_of_the_order'] > 5 & (df2['cost_of_the_order'] <= 20):\n",
        "        return (df2['cost_of_the_order'] + (df2['cost_of_the_order'] * 0.15))\n",
        "    elif df2['cost_of_the_order'] <= 5:\n",
        "        return df2['cost_of_the_order']\n",
        "\n",
        "df2['total_cost'] = df2.apply(revenue, axis = 1)\n",
        "\n",
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "ibEWmDEEt2l3",
        "outputId": "44a057e1-6541-4430-89a2-c6a573785cf0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      order_id  customer_id                        restaurant_name  \\\n",
              "2      1477070        66393                            Cafe Habana   \n",
              "3      1477334       106968              Blue Ribbon Fried Chicken   \n",
              "4      1478249        76942                       Dirty Bird to Go   \n",
              "5      1477224       147468                       Tamarind TriBeCa   \n",
              "7      1477859        89574                              Barbounia   \n",
              "...        ...          ...                                    ...   \n",
              "1889   1478190        94152                       RedFarm Broadway   \n",
              "1890   1477316       164776                                    TAO   \n",
              "1893   1476701       292602  Chipotle Mexican Grill $1.99 Delivery   \n",
              "1894   1477421       397537                              The Smile   \n",
              "1896   1477513        64151                      Jack's Wife Freda   \n",
              "\n",
              "       cuisine_type  cost_of_the_order day_of_the_week  rating  \\\n",
              "2           Mexican              12.23         Weekday       5   \n",
              "3          American              29.20         Weekend       3   \n",
              "4          American              11.59         Weekday       4   \n",
              "5            Indian              25.22         Weekday       3   \n",
              "7     Mediterranean               5.97         Weekday       3   \n",
              "...             ...                ...             ...     ...   \n",
              "1889        Chinese               8.68         Weekday       3   \n",
              "1890       Japanese              15.67         Weekend       5   \n",
              "1893        Mexican              22.31         Weekend       5   \n",
              "1894       American              12.18         Weekend       5   \n",
              "1896  Mediterranean              12.18         Weekday       5   \n",
              "\n",
              "      food_preparation_time  delivery_time  total_cost  \n",
              "2                        23             28     14.0645  \n",
              "3                        25             15     36.5000  \n",
              "4                        25             24     13.3285  \n",
              "5                        20             24     31.5250  \n",
              "7                        33             30      6.8655  \n",
              "...                     ...            ...         ...  \n",
              "1889                     33             30      9.9820  \n",
              "1890                     20             22     18.0205  \n",
              "1893                     31             17     27.8875  \n",
              "1894                     31             19     14.0070  \n",
              "1896                     23             31     14.0070  \n",
              "\n",
              "[1162 rows x 10 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-957a0d02-8abb-4f01-bf19-a264b718dca7\" 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>order_id</th>\n",
              "      <th>customer_id</th>\n",
              "      <th>restaurant_name</th>\n",
              "      <th>cuisine_type</th>\n",
              "      <th>cost_of_the_order</th>\n",
              "      <th>day_of_the_week</th>\n",
              "      <th>rating</th>\n",
              "      <th>food_preparation_time</th>\n",
              "      <th>delivery_time</th>\n",
              "      <th>total_cost</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1477070</td>\n",
              "      <td>66393</td>\n",
              "      <td>Cafe Habana</td>\n",
              "      <td>Mexican</td>\n",
              "      <td>12.23</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>5</td>\n",
              "      <td>23</td>\n",
              "      <td>28</td>\n",
              "      <td>14.0645</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1477334</td>\n",
              "      <td>106968</td>\n",
              "      <td>Blue Ribbon Fried Chicken</td>\n",
              "      <td>American</td>\n",
              "      <td>29.20</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>3</td>\n",
              "      <td>25</td>\n",
              "      <td>15</td>\n",
              "      <td>36.5000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1478249</td>\n",
              "      <td>76942</td>\n",
              "      <td>Dirty Bird to Go</td>\n",
              "      <td>American</td>\n",
              "      <td>11.59</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>4</td>\n",
              "      <td>25</td>\n",
              "      <td>24</td>\n",
              "      <td>13.3285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1477224</td>\n",
              "      <td>147468</td>\n",
              "      <td>Tamarind TriBeCa</td>\n",
              "      <td>Indian</td>\n",
              "      <td>25.22</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>3</td>\n",
              "      <td>20</td>\n",
              "      <td>24</td>\n",
              "      <td>31.5250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1477859</td>\n",
              "      <td>89574</td>\n",
              "      <td>Barbounia</td>\n",
              "      <td>Mediterranean</td>\n",
              "      <td>5.97</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>3</td>\n",
              "      <td>33</td>\n",
              "      <td>30</td>\n",
              "      <td>6.8655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1889</th>\n",
              "      <td>1478190</td>\n",
              "      <td>94152</td>\n",
              "      <td>RedFarm Broadway</td>\n",
              "      <td>Chinese</td>\n",
              "      <td>8.68</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>3</td>\n",
              "      <td>33</td>\n",
              "      <td>30</td>\n",
              "      <td>9.9820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1890</th>\n",
              "      <td>1477316</td>\n",
              "      <td>164776</td>\n",
              "      <td>TAO</td>\n",
              "      <td>Japanese</td>\n",
              "      <td>15.67</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>5</td>\n",
              "      <td>20</td>\n",
              "      <td>22</td>\n",
              "      <td>18.0205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1893</th>\n",
              "      <td>1476701</td>\n",
              "      <td>292602</td>\n",
              "      <td>Chipotle Mexican Grill $1.99 Delivery</td>\n",
              "      <td>Mexican</td>\n",
              "      <td>22.31</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>5</td>\n",
              "      <td>31</td>\n",
              "      <td>17</td>\n",
              "      <td>27.8875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1894</th>\n",
              "      <td>1477421</td>\n",
              "      <td>397537</td>\n",
              "      <td>The Smile</td>\n",
              "      <td>American</td>\n",
              "      <td>12.18</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>5</td>\n",
              "      <td>31</td>\n",
              "      <td>19</td>\n",
              "      <td>14.0070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1896</th>\n",
              "      <td>1477513</td>\n",
              "      <td>64151</td>\n",
              "      <td>Jack's Wife Freda</td>\n",
              "      <td>Mediterranean</td>\n",
              "      <td>12.18</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>5</td>\n",
              "      <td>23</td>\n",
              "      <td>31</td>\n",
              "      <td>14.0070</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1162 rows × 10 columns</p>\n",
              "</div>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    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",
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              "    20% {\n",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\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",
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              "        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",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# calculating total pre-fee revenue\n",
        "df2['cost_of_the_order'].sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g2kaLhrR1hvr",
        "outputId": "38166fab-b3b2-4139-97e0-71e13345a1dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19476.01"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# calcuating total fee revenue\n",
        "df2['total_cost'].sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DONZYZPY-JQz",
        "outputId": "74062e62-9f47-44d0-e1b7-83a104aed86f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "23346.5875"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# creating column to include net revenue\n",
        "df2['net_revenue'] = df2.apply(lambda row: row['total_cost'] - row['cost_of_the_order'], axis = 1)\n",
        "\n",
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "78PZoN44-L51",
        "outputId": "2bb359e3-5da4-476c-c4af-dcbbcd3756e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      order_id  customer_id                        restaurant_name  \\\n",
              "2      1477070        66393                            Cafe Habana   \n",
              "3      1477334       106968              Blue Ribbon Fried Chicken   \n",
              "4      1478249        76942                       Dirty Bird to Go   \n",
              "5      1477224       147468                       Tamarind TriBeCa   \n",
              "7      1477859        89574                              Barbounia   \n",
              "...        ...          ...                                    ...   \n",
              "1889   1478190        94152                       RedFarm Broadway   \n",
              "1890   1477316       164776                                    TAO   \n",
              "1893   1476701       292602  Chipotle Mexican Grill $1.99 Delivery   \n",
              "1894   1477421       397537                              The Smile   \n",
              "1896   1477513        64151                      Jack's Wife Freda   \n",
              "\n",
              "       cuisine_type  cost_of_the_order day_of_the_week  rating  \\\n",
              "2           Mexican              12.23         Weekday       5   \n",
              "3          American              29.20         Weekend       3   \n",
              "4          American              11.59         Weekday       4   \n",
              "5            Indian              25.22         Weekday       3   \n",
              "7     Mediterranean               5.97         Weekday       3   \n",
              "...             ...                ...             ...     ...   \n",
              "1889        Chinese               8.68         Weekday       3   \n",
              "1890       Japanese              15.67         Weekend       5   \n",
              "1893        Mexican              22.31         Weekend       5   \n",
              "1894       American              12.18         Weekend       5   \n",
              "1896  Mediterranean              12.18         Weekday       5   \n",
              "\n",
              "      food_preparation_time  delivery_time  total_cost  net_revenue  \n",
              "2                        23             28     14.0645       1.8345  \n",
              "3                        25             15     36.5000       7.3000  \n",
              "4                        25             24     13.3285       1.7385  \n",
              "5                        20             24     31.5250       6.3050  \n",
              "7                        33             30      6.8655       0.8955  \n",
              "...                     ...            ...         ...          ...  \n",
              "1889                     33             30      9.9820       1.3020  \n",
              "1890                     20             22     18.0205       2.3505  \n",
              "1893                     31             17     27.8875       5.5775  \n",
              "1894                     31             19     14.0070       1.8270  \n",
              "1896                     23             31     14.0070       1.8270  \n",
              "\n",
              "[1162 rows x 11 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8ab93087-e872-434e-849e-221e250e85cc\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>order_id</th>\n",
              "      <th>customer_id</th>\n",
              "      <th>restaurant_name</th>\n",
              "      <th>cuisine_type</th>\n",
              "      <th>cost_of_the_order</th>\n",
              "      <th>day_of_the_week</th>\n",
              "      <th>rating</th>\n",
              "      <th>food_preparation_time</th>\n",
              "      <th>delivery_time</th>\n",
              "      <th>total_cost</th>\n",
              "      <th>net_revenue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1477070</td>\n",
              "      <td>66393</td>\n",
              "      <td>Cafe Habana</td>\n",
              "      <td>Mexican</td>\n",
              "      <td>12.23</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>5</td>\n",
              "      <td>23</td>\n",
              "      <td>28</td>\n",
              "      <td>14.0645</td>\n",
              "      <td>1.8345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1477334</td>\n",
              "      <td>106968</td>\n",
              "      <td>Blue Ribbon Fried Chicken</td>\n",
              "      <td>American</td>\n",
              "      <td>29.20</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>3</td>\n",
              "      <td>25</td>\n",
              "      <td>15</td>\n",
              "      <td>36.5000</td>\n",
              "      <td>7.3000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1478249</td>\n",
              "      <td>76942</td>\n",
              "      <td>Dirty Bird to Go</td>\n",
              "      <td>American</td>\n",
              "      <td>11.59</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>4</td>\n",
              "      <td>25</td>\n",
              "      <td>24</td>\n",
              "      <td>13.3285</td>\n",
              "      <td>1.7385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1477224</td>\n",
              "      <td>147468</td>\n",
              "      <td>Tamarind TriBeCa</td>\n",
              "      <td>Indian</td>\n",
              "      <td>25.22</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>3</td>\n",
              "      <td>20</td>\n",
              "      <td>24</td>\n",
              "      <td>31.5250</td>\n",
              "      <td>6.3050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1477859</td>\n",
              "      <td>89574</td>\n",
              "      <td>Barbounia</td>\n",
              "      <td>Mediterranean</td>\n",
              "      <td>5.97</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>3</td>\n",
              "      <td>33</td>\n",
              "      <td>30</td>\n",
              "      <td>6.8655</td>\n",
              "      <td>0.8955</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1889</th>\n",
              "      <td>1478190</td>\n",
              "      <td>94152</td>\n",
              "      <td>RedFarm Broadway</td>\n",
              "      <td>Chinese</td>\n",
              "      <td>8.68</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>3</td>\n",
              "      <td>33</td>\n",
              "      <td>30</td>\n",
              "      <td>9.9820</td>\n",
              "      <td>1.3020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1890</th>\n",
              "      <td>1477316</td>\n",
              "      <td>164776</td>\n",
              "      <td>TAO</td>\n",
              "      <td>Japanese</td>\n",
              "      <td>15.67</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>5</td>\n",
              "      <td>20</td>\n",
              "      <td>22</td>\n",
              "      <td>18.0205</td>\n",
              "      <td>2.3505</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1893</th>\n",
              "      <td>1476701</td>\n",
              "      <td>292602</td>\n",
              "      <td>Chipotle Mexican Grill $1.99 Delivery</td>\n",
              "      <td>Mexican</td>\n",
              "      <td>22.31</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>5</td>\n",
              "      <td>31</td>\n",
              "      <td>17</td>\n",
              "      <td>27.8875</td>\n",
              "      <td>5.5775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1894</th>\n",
              "      <td>1477421</td>\n",
              "      <td>397537</td>\n",
              "      <td>The Smile</td>\n",
              "      <td>American</td>\n",
              "      <td>12.18</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>5</td>\n",
              "      <td>31</td>\n",
              "      <td>19</td>\n",
              "      <td>14.0070</td>\n",
              "      <td>1.8270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1896</th>\n",
              "      <td>1477513</td>\n",
              "      <td>64151</td>\n",
              "      <td>Jack's Wife Freda</td>\n",
              "      <td>Mediterranean</td>\n",
              "      <td>12.18</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>5</td>\n",
              "      <td>23</td>\n",
              "      <td>31</td>\n",
              "      <td>14.0070</td>\n",
              "      <td>1.8270</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1162 rows × 11 columns</p>\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# calcuating net revenue\n",
        "df2['net_revenue'].sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cFfOBVRF-oDa",
        "outputId": "a5a425ed-a871-48ac-acff-7bbc27fe032f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3870.5774999999994"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XHCEJgH0JY_W"
      },
      "source": [
        "**Observations**: The total costs from orders before the surcharge is $19,476.01.\n",
        "\n",
        "- Total revenue is $23,346.59.\n",
        "\n",
        "- Net revenue is $3,870.58.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pRlVW-oeJY_W"
      },
      "source": [
        "### **Question 15:** The company wants to analyze the total time required to deliver the food. What percentage of orders take more than 60 minutes to get delivered from the time the order is placed?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zxoTrxfLJY_W",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "outputId": "9430fb82-4bb3-4038-a40d-6c028369f992"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      order_id  customer_id                        restaurant_name  \\\n",
              "2      1477070        66393                            Cafe Habana   \n",
              "3      1477334       106968              Blue Ribbon Fried Chicken   \n",
              "4      1478249        76942                       Dirty Bird to Go   \n",
              "5      1477224       147468                       Tamarind TriBeCa   \n",
              "7      1477859        89574                              Barbounia   \n",
              "...        ...          ...                                    ...   \n",
              "1889   1478190        94152                       RedFarm Broadway   \n",
              "1890   1477316       164776                                    TAO   \n",
              "1893   1476701       292602  Chipotle Mexican Grill $1.99 Delivery   \n",
              "1894   1477421       397537                              The Smile   \n",
              "1896   1477513        64151                      Jack's Wife Freda   \n",
              "\n",
              "       cuisine_type  cost_of_the_order day_of_the_week  rating  \\\n",
              "2           Mexican              12.23         Weekday       5   \n",
              "3          American              29.20         Weekend       3   \n",
              "4          American              11.59         Weekday       4   \n",
              "5            Indian              25.22         Weekday       3   \n",
              "7     Mediterranean               5.97         Weekday       3   \n",
              "...             ...                ...             ...     ...   \n",
              "1889        Chinese               8.68         Weekday       3   \n",
              "1890       Japanese              15.67         Weekend       5   \n",
              "1893        Mexican              22.31         Weekend       5   \n",
              "1894       American              12.18         Weekend       5   \n",
              "1896  Mediterranean              12.18         Weekday       5   \n",
              "\n",
              "      food_preparation_time  delivery_time  total_cost  net_revenue  \\\n",
              "2                        23             28     14.0645       1.8345   \n",
              "3                        25             15     36.5000       7.3000   \n",
              "4                        25             24     13.3285       1.7385   \n",
              "5                        20             24     31.5250       6.3050   \n",
              "7                        33             30      6.8655       0.8955   \n",
              "...                     ...            ...         ...          ...   \n",
              "1889                     33             30      9.9820       1.3020   \n",
              "1890                     20             22     18.0205       2.3505   \n",
              "1893                     31             17     27.8875       5.5775   \n",
              "1894                     31             19     14.0070       1.8270   \n",
              "1896                     23             31     14.0070       1.8270   \n",
              "\n",
              "      total_delivery_time  \n",
              "2                      51  \n",
              "3                      40  \n",
              "4                      49  \n",
              "5                      44  \n",
              "7                      63  \n",
              "...                   ...  \n",
              "1889                   63  \n",
              "1890                   42  \n",
              "1893                   48  \n",
              "1894                   50  \n",
              "1896                   54  \n",
              "\n",
              "[1162 rows x 12 columns]"
            ],
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              "\n",
              "  <div id=\"df-c53c851f-16d6-4ae9-ba89-5fab9241342a\" class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>order_id</th>\n",
              "      <th>customer_id</th>\n",
              "      <th>restaurant_name</th>\n",
              "      <th>cuisine_type</th>\n",
              "      <th>cost_of_the_order</th>\n",
              "      <th>day_of_the_week</th>\n",
              "      <th>rating</th>\n",
              "      <th>food_preparation_time</th>\n",
              "      <th>delivery_time</th>\n",
              "      <th>total_cost</th>\n",
              "      <th>net_revenue</th>\n",
              "      <th>total_delivery_time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1477070</td>\n",
              "      <td>66393</td>\n",
              "      <td>Cafe Habana</td>\n",
              "      <td>Mexican</td>\n",
              "      <td>12.23</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>5</td>\n",
              "      <td>23</td>\n",
              "      <td>28</td>\n",
              "      <td>14.0645</td>\n",
              "      <td>1.8345</td>\n",
              "      <td>51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1477334</td>\n",
              "      <td>106968</td>\n",
              "      <td>Blue Ribbon Fried Chicken</td>\n",
              "      <td>American</td>\n",
              "      <td>29.20</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>3</td>\n",
              "      <td>25</td>\n",
              "      <td>15</td>\n",
              "      <td>36.5000</td>\n",
              "      <td>7.3000</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1478249</td>\n",
              "      <td>76942</td>\n",
              "      <td>Dirty Bird to Go</td>\n",
              "      <td>American</td>\n",
              "      <td>11.59</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>4</td>\n",
              "      <td>25</td>\n",
              "      <td>24</td>\n",
              "      <td>13.3285</td>\n",
              "      <td>1.7385</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1477224</td>\n",
              "      <td>147468</td>\n",
              "      <td>Tamarind TriBeCa</td>\n",
              "      <td>Indian</td>\n",
              "      <td>25.22</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>3</td>\n",
              "      <td>20</td>\n",
              "      <td>24</td>\n",
              "      <td>31.5250</td>\n",
              "      <td>6.3050</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1477859</td>\n",
              "      <td>89574</td>\n",
              "      <td>Barbounia</td>\n",
              "      <td>Mediterranean</td>\n",
              "      <td>5.97</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>3</td>\n",
              "      <td>33</td>\n",
              "      <td>30</td>\n",
              "      <td>6.8655</td>\n",
              "      <td>0.8955</td>\n",
              "      <td>63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1889</th>\n",
              "      <td>1478190</td>\n",
              "      <td>94152</td>\n",
              "      <td>RedFarm Broadway</td>\n",
              "      <td>Chinese</td>\n",
              "      <td>8.68</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>3</td>\n",
              "      <td>33</td>\n",
              "      <td>30</td>\n",
              "      <td>9.9820</td>\n",
              "      <td>1.3020</td>\n",
              "      <td>63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1890</th>\n",
              "      <td>1477316</td>\n",
              "      <td>164776</td>\n",
              "      <td>TAO</td>\n",
              "      <td>Japanese</td>\n",
              "      <td>15.67</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>5</td>\n",
              "      <td>20</td>\n",
              "      <td>22</td>\n",
              "      <td>18.0205</td>\n",
              "      <td>2.3505</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1893</th>\n",
              "      <td>1476701</td>\n",
              "      <td>292602</td>\n",
              "      <td>Chipotle Mexican Grill $1.99 Delivery</td>\n",
              "      <td>Mexican</td>\n",
              "      <td>22.31</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>5</td>\n",
              "      <td>31</td>\n",
              "      <td>17</td>\n",
              "      <td>27.8875</td>\n",
              "      <td>5.5775</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1894</th>\n",
              "      <td>1477421</td>\n",
              "      <td>397537</td>\n",
              "      <td>The Smile</td>\n",
              "      <td>American</td>\n",
              "      <td>12.18</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>5</td>\n",
              "      <td>31</td>\n",
              "      <td>19</td>\n",
              "      <td>14.0070</td>\n",
              "      <td>1.8270</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1896</th>\n",
              "      <td>1477513</td>\n",
              "      <td>64151</td>\n",
              "      <td>Jack's Wife Freda</td>\n",
              "      <td>Mediterranean</td>\n",
              "      <td>12.18</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>5</td>\n",
              "      <td>23</td>\n",
              "      <td>31</td>\n",
              "      <td>14.0070</td>\n",
              "      <td>1.8270</td>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1162 rows × 12 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c53c851f-16d6-4ae9-ba89-5fab9241342a')\"\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      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",
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              "\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",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c53c851f-16d6-4ae9-ba89-5fab9241342a 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-c53c851f-16d6-4ae9-ba89-5fab9241342a');\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",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6302e42a-c964-473a-bd1b-7054bc5f3b1c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6302e42a-c964-473a-bd1b-7054bc5f3b1c')\"\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",
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              "    </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",
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              "      border-top-color: var(--fill-color);\n",
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              "    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",
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              "  }\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",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6302e42a-c964-473a-bd1b-7054bc5f3b1c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ],
      "source": [
        "# first, creating column to summarize a combination between prep time and delivery time as a single value\n",
        "df2['total_delivery_time'] = df2.apply(lambda row: row['food_preparation_time'] + row['delivery_time'], axis = 1)\n",
        "\n",
        "df2"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# calculating percentage of deliveries over 60 minutes\n",
        "longer_wait = df2['total_delivery_time'] > 60\n",
        "\n",
        "longer_wait.value_counts(normalize = True) # normalizing to get count of T/F as a percentage"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hc_CLUdlAasI",
        "outputId": "aeff06aa-fd34-4d50-aa5f-ec0c366713b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    0.89759\n",
              "True     0.10241\n",
              "Name: total_delivery_time, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iqzBXn2yJY_W"
      },
      "source": [
        "#### Observations: 10.2% of orders take over 60 minutes to be delivered.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bUd6cYpPJY_W"
      },
      "source": [
        "### **Question 16:** The company wants to analyze the delivery time of the orders on weekdays and weekends. How does the mean delivery time vary during weekdays and weekends? [2 marks]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pIU5xZZ6JY_W",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "c0d50533-4cd7-46f6-a0dc-021c5998ec38"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='rating', ylabel='total_delivery_time'>"
            ]
          },
          "metadata": {},
          "execution_count": 129
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# we can analyze this difference through visualization\n",
        "sns.boxplot(data = df2, x = 'rating', y = 'total_delivery_time', hue = 'day_of_the_week')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8H-ODxbVJY_W"
      },
      "source": [
        "#### Observations:\n",
        "- Weekend delivery times are generally faster than weekday deliveries.\n",
        "- Weekend deliveries recieve higher ratings than weekday deliveries.\n",
        "- Weekday deliveries take more than 60 minutes to complete more often than weekend deliveries.\n",
        "- Despite being faster, weekend deliveries are more commonly rated below 4.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "17roJywaJY_W"
      },
      "source": [
        "## **Conclusion and Recommendations**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZdX2Wz4FJY_W"
      },
      "source": [
        "### **Question 17:** What are your conclusions from the analysis? What recommendations would you like to share to help improve the business?"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# one more look at relevant values\n",
        "sns.catplot(data = df2, x = 'total_delivery_time', y = 'cuisine_type', hue = 'cuisine_type', col = 'day_of_the_week', kind = 'violin')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "fwLK9EnMFVyl",
        "outputId": "ae5b38a0-0f18-4bcc-8725-6216446d563a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7884243f3af0>"
            ]
          },
          "metadata": {},
          "execution_count": 132
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1153.12x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# drilling into ratings once more\n",
        "sns.catplot(data = df2, y = 'cuisine_type', hue = 'rating', col = 'day_of_the_week', kind = 'count')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "XQovo2bPH4J_",
        "outputId": "a0b6ce18-69d0-4c0e-995c-39ef456967f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x788426eb40a0>"
            ]
          },
          "metadata": {},
          "execution_count": 115
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1058.75x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# analyzing money spent vs time of the week\n",
        "sns.catplot(data = df2, y = 'cost_of_the_order', hue = 'rating', col = 'day_of_the_week', kind = 'swarm')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "id": "dmLg0_eXJYva",
        "outputId": "c8048c5b-5e38-466c-d699-cc6c71514945"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3544: UserWarning: 12.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
            "  warnings.warn(msg, UserWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3544: UserWarning: 10.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
            "  warnings.warn(msg, UserWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x788424ade080>"
            ]
          },
          "metadata": {},
          "execution_count": 133
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3544: UserWarning: 11.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
            "  warnings.warn(msg, UserWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1058.75x500 with 2 Axes>"
            ],
            "image/png": 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\n"
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        "### **Conclusions**:\n",
        "- There is a preference among customers to order food on weekends.\n",
        "- Customers spend more money on orders on average on the weeknds vs weekdays.\n",
        "- There doesn't appear to be a correlation between the cost of an order and its rating\n",
        "- There doesn't appear to be a correlation between cuisine type and rating.\n",
        "- There doesn't appear to be a correlation between delivery time and rating.\n",
        "- 8/14 cuisine types [Mexican, American, Indian, Mediterranean, Chinese, Japanese, Middle Eastern, Italian] make up for the majority of customer spending.\n",
        "- Delivery times on weekends, though more arrive sooner than 60 minutes, are more unpredictable compared to a weekday.\n"
      ]
    },
    {
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      "metadata": {
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        "### **Recommendations**:\n",
        "- **Investigate why 42% of cuisine types lack order volume:** There is close to 0 activity across weekdays and weekends in 6/14 (over 42%) cuisine types. Is this due to a lack of awareness about what's out there? Is this regional? Is it due to a lack of drivers?\n",
        "- **Seek to drive more weekday engagement:** While customers spend more money on the weekends, the overall volume nearly doubles or triples weekday orders across all cuisine categories. Driving app engagement and understanding why customers are ordering less during the week could become a significant driven of revenue.\n",
        "- **Perform competitor analysis:** In addition to investigating the lack of weekday engagement, it is important to understand the competitive landscape within our markets, as our lack of order volume during the week could be due to greater market share of weekday orders by another delivery company.\n",
        "- **Seek investment outside of traditional channels:** Given how few of our orders take over 60 minutes to deliver, it would be interesting to see how developing some accountability on our part could drive increased volume.\n",
        "Rather than focus on coupons or other marketing spend, we could take advantage of our ~90% less than 60 minute delivery rate by saying that we waive our 15%-20% charge on any orders that take longer than 60 minutes.\n",
        "- **Repricing:** Conversely, we can explore better segmentation within current pricing model to generate more net revenue from our current volume. There is a wide gap between $5 and $20 (15% charge), and an even wider opportunity over $20 (25% charge). I would recommend drilling further into this range:\n",
        "\n",
        "  - $5 < cost < $10 (10%)\n",
        "  - $10 < cost < $15 (15%)\n",
        "  - $15 < cost < $25 (25%)\n",
        "  - $25 < cost < $30 (30%)"
      ]
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