foodhub_code.ipynb

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      "source": [
        "# Project Python Foundations: FoodHub Data Analysis\n",
        "\n",
        "**Marks: 60**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NqikjkWwJY_L"
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      "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"
      ]
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    {
      "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"
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      "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\n",
        "\n",
        "# to restrict the float value to 3 decimal places\n",
        "pd.set_option('display.float_format', lambda x: '%.3f' % x)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AdracY0HJY_O"
      },
      "source": [
        "### Understanding the structure of the data"
      ]
    },
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          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "# This code mounts the google drive so we can read in the CSV file\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
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        "id": "GF01IZDbJY_O",
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        "outputId": "be038b0a-470e-4035-efd2-13fc22657c3b"
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          "data": {
            "text/plain": [
              "   order_id  customer_id            restaurant_name cuisine_type  \\\n",
              "0   1477147       337525                    Hangawi       Korean   \n",
              "1   1477685       358141  Blue Ribbon Sushi Izakaya     Japanese   \n",
              "2   1477070        66393                Cafe Habana      Mexican   \n",
              "3   1477334       106968  Blue Ribbon Fried Chicken     American   \n",
              "4   1478249        76942           Dirty Bird to Go     American   \n",
              "\n",
              "   cost_of_the_order day_of_the_week     rating  food_preparation_time  \\\n",
              "0             30.750         Weekend  Not given                     25   \n",
              "1             12.080         Weekend  Not given                     25   \n",
              "2             12.230         Weekday          5                     23   \n",
              "3             29.200         Weekend          3                     25   \n",
              "4             11.590         Weekday          4                     25   \n",
              "\n",
              "   delivery_time  \n",
              "0             20  \n",
              "1             23  \n",
              "2             28  \n",
              "3             15  \n",
              "4             24  "
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              "      <th>order_id</th>\n",
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              "      <td>29.200</td>\n",
              "      <td>Weekend</td>\n",
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      ],
      "source": [
        "# read the data\n",
        "fd_data = pd.read_csv('/content/drive/My Drive/DSBA/Project 1/foodhub_order.csv')\n",
        "# returns the first 5 rows\n",
        "fd_data.head()"
      ]
    },
    {
      "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? [0.5 mark]"
      ]
    },
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      "execution_count": null,
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 1898 rows and 9 columns.\n"
          ]
        }
      ],
      "source": [
        "print(\"There are\", fd_data.shape[0], 'rows and', fd_data.shape[1], \"columns.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UsTNy3unJY_R"
      },
      "source": [
        "#### Observations: There are 1898 rows and 9 columns in the data\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jRXakVrUJY_R"
      },
      "source": [
        "### **Question 2:** What are the datatypes of the different columns in the dataset? (The info() function can be used) [0.5 mark]"
      ]
    },
    {
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          "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": [
        "# Use info() to print a concise summary of the DataFrame\n",
        "fd_data.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WJes5GP2JY_S"
      },
      "source": [
        "#### Observations: There are 9 collumns present in the dataframe with 4 object columns represented by categorical variables and 5 numerical columns represented by floats and integer values\n"
      ]
    },
    {
      "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]"
      ]
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            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Count, Percentage]\n",
              "Index: []"
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "pd.DataFrame({'Count':fd_data.isnull().sum()[fd_data.isnull().sum()>0],'Percentage':(fd_data.isnull().sum()[fd_data.isnull().sum()>0]/fd_data.shape[0])*100})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jDHT3vD1JY_S"
      },
      "source": [
        "#### Observations: There are no missing values in the data \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? [2 marks]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WprQBgOSJY_T",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "a313cb46-3ee0-40a7-bc4a-dbbebc4033f9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This is the Statistical Summary of the Data\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         order_id  customer_id  cost_of_the_order  food_preparation_time  \\\n",
              "count    1898.000     1898.000           1898.000               1898.000   \n",
              "mean  1477495.500   171168.478             16.499                 27.372   \n",
              "std       548.050   113698.140              7.484                  4.632   \n",
              "min   1476547.000     1311.000              4.470                 20.000   \n",
              "25%   1477021.250    77787.750             12.080                 23.000   \n",
              "50%   1477495.500   128600.000             14.140                 27.000   \n",
              "75%   1477969.750   270525.000             22.297                 31.000   \n",
              "max   1478444.000   405334.000             35.410                 35.000   \n",
              "\n",
              "       delivery_time  \n",
              "count       1898.000  \n",
              "mean          24.162  \n",
              "std            4.973  \n",
              "min           15.000  \n",
              "25%           20.000  \n",
              "50%           25.000  \n",
              "75%           28.000  \n",
              "max           33.000  "
            ],
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              "\n",
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              "<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>cost_of_the_order</th>\n",
              "      <th>food_preparation_time</th>\n",
              "      <th>delivery_time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1898.000</td>\n",
              "      <td>1898.000</td>\n",
              "      <td>1898.000</td>\n",
              "      <td>1898.000</td>\n",
              "      <td>1898.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1477495.500</td>\n",
              "      <td>171168.478</td>\n",
              "      <td>16.499</td>\n",
              "      <td>27.372</td>\n",
              "      <td>24.162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>548.050</td>\n",
              "      <td>113698.140</td>\n",
              "      <td>7.484</td>\n",
              "      <td>4.632</td>\n",
              "      <td>4.973</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1476547.000</td>\n",
              "      <td>1311.000</td>\n",
              "      <td>4.470</td>\n",
              "      <td>20.000</td>\n",
              "      <td>15.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1477021.250</td>\n",
              "      <td>77787.750</td>\n",
              "      <td>12.080</td>\n",
              "      <td>23.000</td>\n",
              "      <td>20.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1477495.500</td>\n",
              "      <td>128600.000</td>\n",
              "      <td>14.140</td>\n",
              "      <td>27.000</td>\n",
              "      <td>25.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1477969.750</td>\n",
              "      <td>270525.000</td>\n",
              "      <td>22.297</td>\n",
              "      <td>31.000</td>\n",
              "      <td>28.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1478444.000</td>\n",
              "      <td>405334.000</td>\n",
              "      <td>35.410</td>\n",
              "      <td>35.000</td>\n",
              "      <td>33.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c312b200-df20-49cc-a69b-f240735ae8cd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "print('This is the Statistical Summary of the Data')\n",
        "fd_data.describe()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Minimum Preparation Time =', fd_data['food_preparation_time'].min(),'mins')\n",
        "print('Average Preparation Time =', fd_data['food_preparation_time'].mean(),'mins')\n",
        "print('Max Preparation Time =', fd_data['food_preparation_time'].max(),'mins')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ruWqvyQaL3HS",
        "outputId": "43259bcf-0fbe-406d-f75c-d8d60448c990"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Minimum Preparation Time = 20 mins\n",
            "Average Preparation Time = 27.371970495258168 mins\n",
            "Max Preparation Time = 35 mins\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9YbSOR5wJY_T"
      },
      "source": [
        "#### Observations: The statistical summary of the data cuts across 5 integer or float columns. The 'Rating'column is considered as an object and as such is not defined here\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cw3l58z_JY_T"
      },
      "source": [
        "### **Question 5:** How many orders are not rated? [1 mark]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fd_data['rating'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VCmt_qoUL-Dc",
        "outputId": "a2e38a5c-4e5b-4c7b-d3a0-f687ecc8edcc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Not given', '5', '3', '4'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hEzDUL9wgksi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "70c7b5ce-438c-4064-c4a6-9811b89d11c2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 736 orders not rated\n"
          ]
        }
      ],
      "source": [
        "print('There are', fd_data['rating'].value_counts()['Not given'],'orders not rated')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5E0O_oviJY_T"
      },
      "source": [
        "#### Observations: Though there are no missing values under the rating column, 736 orders have not been rated which could be considered missing in some terms\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",
      "execution_count": null,
      "metadata": {
        "id": "rU5ZqISjJY_U",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "outputId": "cc58d41b-3bc7-4be8-c2e6-122a717f910e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.countplot(data=fd_data,x='cuisine_type',palette='inferno')\n",
        "plt.xticks(rotation=90)\n",
        "plt.title('Histogram of the Count of Cuisine Types')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(data=fd_data, x='cost_of_the_order', kde=True);\n",
        "plt.title('Distribution of Cost of the Order')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "4GWbPw9WMgXc",
        "outputId": "c622e71e-01e1-41a0-babd-0b39285842aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Distribution of Cost of the Order')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(data=fd_data, x='day_of_the_week');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "BELJBuFmMyc8",
        "outputId": "83e54f13-7387-4ea5-e92d-b8d18cf3d2f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEICAYAAACuxNj9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAW6ElEQVR4nO3de7SddX3n8fdHIiiiBOVIMQmGaqpFOt5OAUEdRroQqBps8basgFLTjmh1vKJOS8dLq6MtarWMURDoUFCpDmmlIoMoKhdJkKugRBSSiBINoGtYlol+54/nl2H3eJLnJJy9z0nO+7XWXud5vr/ffvZvJzv5nOeyf0+qCkmStuRBMz0ASdLsZ1hIknoZFpKkXoaFJKmXYSFJ6mVYSJJ6zRvWhpOcDjwPuLOq9p/Q9ibgg8BYVf0kSYAPA0cB9wLHV9XVre9xwH9tT31PVZ3Z99p77rlnLV68eNreiyTNBatWrfpJVY1N1ja0sADOAD4KnDVYTLIIOBy4faB8JLCkPQ4ETgUOTPJI4GRgHChgVZIVVXXXll548eLFrFy5cprehiTNDUlu21zb0A5DVdWlwIZJmk4B3kr3n/8mS4GzqnMFMD/J3sBzgYuqakMLiIuAI4Y1ZknS5EZ6ziLJUmBdVV07oWkBsGZgfW2rba4+2baXJVmZZOX69euncdSSpJGFRZJdgXcAfzGM7VfV8qoar6rxsbFJD7lJkrbRKPcsHgfsC1yb5AfAQuDqJL8BrAMWDfRd2Gqbq0uSRmhkYVFV11fVo6tqcVUtpjuk9LSq+hGwAjg2nYOAe6rqDuBC4PAkeyTZg+7E+IWjGrMkqTO0sEhyDnA58IQka5OcsIXuFwC3AquBTwCvAaiqDcC7gava412tJkkaoeyIU5SPj4+Xl85K0tZJsqqqxidr8xvckqRehoUkqdcwv8G93VqwaB9+uHZNf0dpGzxm4SLWrbm9v6M0ixgWk/jh2jW85OOXzfQwtIP69J8cPNNDkLaah6EkSb0MC0lSL8NCktTLsJAk9TIsJEm9DAtJUi/DQpLUy7CQJPUyLCRJvQwLSVIvw0KS1MuwkCT1MiwkSb0MC0lSL8NCktTLsJAk9TIsJEm9DAtJUq+hhUWS05PcmeSGgdoHktyc5Lokn08yf6Dt7UlWJ/lOkucO1I9otdVJThrWeCVJmzfMPYszgCMm1C4C9q+q/wB8F3g7QJL9gJcCT2rP+fskOyXZCfgYcCSwH/Cy1leSNEJDC4uquhTYMKH2para2FavABa25aXAuVX1b1X1fWA1cEB7rK6qW6vqPuDc1leSNEIzec7iVcC/tuUFwJqBtrWttrn6r0myLMnKJCvXr18/hOFK0tw1I2GR5J3ARuDs6dpmVS2vqvGqGh8bG5uuzUqSgHmjfsEkxwPPAw6rqmrldcCigW4LW40t1CVJIzLSPYskRwBvBV5QVfcONK0AXppklyT7AkuAbwJXAUuS7JtkZ7qT4CtGOWZJ0hD3LJKcAxwK7JlkLXAy3dVPuwAXJQG4oqr+tKpuTPIZ4Nt0h6dOrKpftu28FrgQ2Ak4vapuHNaYJUmTG1pYVNXLJimftoX+7wXeO0n9AuCCaRyaJGkr+Q1uSVIvw0KS1MuwkCT1MiwkSb0MC0lSL8NCktTLsJAk9TIsJEm9DAtJUi/DQpLUy7CQJPUyLCRJvQwLSVIvw0KS1MuwkCT1MiwkSb0MC0lSL8NCktTLsJAk9TIsJEm9DAtJUi/DQpLUa2hhkeT0JHcmuWGg9sgkFyW5pf3co9WT5CNJVie5LsnTBp5zXOt/S5LjhjVeSdLmDXPP4gzgiAm1k4CLq2oJcHFbBzgSWNIey4BToQsX4GTgQOAA4ORNASNJGp2hhUVVXQpsmFBeCpzZls8Ejh6on1WdK4D5SfYGngtcVFUbquou4CJ+PYAkSUM26nMWe1XVHW35R8BebXkBsGag39pW21z91yRZlmRlkpXr16+f3lFL0hw3Yye4q6qAmsbtLa+q8aoaHxsbm67NSpIYfVj8uB1eov28s9XXAYsG+i1stc3VJUkjNOqwWAFsuqLpOOD8gfqx7aqog4B72uGqC4HDk+zRTmwf3mqSpBGaN6wNJzkHOBTYM8lauqua3gd8JskJwG3Ai1v3C4CjgNXAvcArAapqQ5J3A1e1fu+qqoknzSVJQza0sKiql22m6bBJ+hZw4ma2czpw+jQOTZK0lfwGtySpl2EhSeplWEiSehkWkqRehoUkqZdhIUnqZVhIknoZFpKkXoaFJKmXYSFJ6mVYSJJ6GRaSpF6GhSSpl2EhSeplWEiSehkWkqRehoUkqZdhIUnqZVhIknoZFpKkXoaFJKnXjIRFkv+S5MYkNyQ5J8lDkuyb5Mokq5N8OsnOre8ubX11a188E2OWpLls5GGRZAHwZ8B4Ve0P7AS8FHg/cEpVPR64CzihPeUE4K5WP6X1kySN0EwdhpoHPDTJPGBX4A7gOcB5rf1M4Oi2vLSt09oPS5IRjlWS5ryRh0VVrQM+CNxOFxL3AKuAu6tqY+u2FljQlhcAa9pzN7b+j5q43STLkqxMsnL9+vXDfROSNMfMxGGoPej2FvYFHgM8DDjigW63qpZX1XhVjY+NjT3QzUmSBkwpLJIcMpXaFP0e8P2qWl9V/xf4HHAIML8dlgJYCKxry+uARe015wG7Az/dxteWJG2Dqe5Z/N0Ua1NxO3BQkl3buYfDgG8DlwDHtD7HAee35RVtndb+5aqqbXxtSdI2mLelxiTPAA4GxpK8caDpEXRXMW21qroyyXnA1cBG4FvAcuALwLlJ3tNqp7WnnAb8Q5LVwAa6K6ckSSO0xbAAdgZ2a/0ePlD/GffvBWy1qjoZOHlC+VbggEn6/gJ40ba+liTpgdtiWFTVV4GvJjmjqm4b0ZgkSbNM357FJrskWQ4sHnxOVT1nGIOSJM0uUw2LzwL/A/gk8MvhDUeSNBtNNSw2VtWpQx2JJGnWmuqls/+c5DVJ9k7yyE2PoY5MkjRrTHXPYtP3HN4yUCvgN6d3OJKk2WhKYVFV+w57IJKk2WtKYZHk2MnqVXXW9A5HkjQbTfUw1O8OLD+EboqOqwHDQpLmgKkehnrd4HqS+cC5QxmRJGnW2dYpyv8P3RTjkqQ5YKrnLP6Z7uon6CYQ/G3gM8MalCRpdpnqOYsPDixvBG6rqrVDGI8kaRaa0mGoNqHgzXQzz+4B3DfMQUmSZpep3invxcA36aYKfzFwZZJtnqJckrR9mephqHcCv1tVdwIkGQP+N3DesAYmSZo9pno11IM2BUXz0614riRpOzfVPYsvJrkQOKetvwS4YDhDkiTNNn334H48sFdVvSXJHwDPbE2XA2cPe3CSpNmhb8/iQ8DbAarqc8DnAJL8Tmt7/lBHJ0maFfrOO+xVVddPLLba4qGMSJI06/SFxfwttD10OgciSZq9+sJiZZJXTywm+WNg1ba+aJL5Sc5LcnOSm5I8o91976Ikt7Sfe7S+SfKRJKuTXJfkadv6upKkbdN3zuINwOeTvJz7w2Ec2Bl44QN43Q8DX6yqY5LsDOwKvAO4uKrel+Qk4CTgbcCRwJL2OBA4tf2UJI3IFsOiqn4MHJzkPwH7t/IXqurL2/qCSXYHng0c317jPuC+JEuBQ1u3M4Gv0IXFUuCsqirgirZXsndV3bGtY5AkbZ2p3s/iEuCSaXrNfYH1wKeSPJluj+X1dCfTNwXAj4C92vICYM3A89e22r8LiyTLgGUA++yzzzQNVZIEM/Mt7HnA04BTq+qpdPfGOGmwQ9uLqEmeu1lVtbyqxqtqfGxsbNoGK0mambBYC6ytqivb+nl04fHjJHsDtJ+bphdZBywaeP7CVpMkjcjIw6KqfgSsSfKEVjoM+DawAjiu1Y4Dzm/LK4Bj21VRBwH3eL5CkkZrqnNDTbfXAWe3K6FuBV5JF1yfSXICcBvdVOjQzUF1FLAauLf1lSSN0IyERVVdQ3cJ7kSHTdK3gBOHPihJ0mY5zbgkqZdhIUnqNVPnLKS560HzSDLTo9AO6jELF7Fuze3Tvl3DQhq1X23kJR+/bKZHoR3Up//k4KFs18NQkqRehoUkqZdhIUnqZVhIknoZFpKkXoaFJKmXYSFJ6mVYSJJ6GRaSpF6GhSSpl2EhSeplWEiSehkWkqRehoUkqZdhIUnqZVhIknoZFpKkXjMWFkl2SvKtJP/S1vdNcmWS1Uk+nWTnVt+lra9u7YtnasySNFfN5J7F64GbBtbfD5xSVY8H7gJOaPUTgLta/ZTWT5I0QjMSFkkWAr8PfLKtB3gOcF7rciZwdFte2tZp7YfFu91L0kjN1J7Fh4C3Ar9q648C7q6qjW19LbCgLS8A1gC09ntaf0nSiIw8LJI8D7izqlZN83aXJVmZZOX69eunc9OSNOfNxJ7FIcALkvwAOJfu8NOHgflJ5rU+C4F1bXkdsAigte8O/HTiRqtqeVWNV9X42NjYcN+BJM0xIw+Lqnp7VS2sqsXAS4EvV9XLgUuAY1q344Dz2/KKtk5r/3JV1QiHLElz3mz6nsXbgDcmWU13TuK0Vj8NeFSrvxE4aYbGJ0lz1rz+LsNTVV8BvtKWbwUOmKTPL4AXjXRgkqR/ZzbtWUiSZinDQpLUy7CQJPUyLCRJvQwLSVIvw0KS1MuwkCT1MiwkSb0MC0lSL8NCktTLsJAk9TIsJEm9DAtJUi/DQpLUy7CQJPUyLCRJvQwLSVIvw0KS1MuwkCT1MiwkSb0MC0lSL8NCktRr5GGRZFGSS5J8O8mNSV7f6o9MclGSW9rPPVo9ST6SZHWS65I8bdRjlqS5bib2LDYCb6qq/YCDgBOT7AecBFxcVUuAi9s6wJHAkvZYBpw6+iFL0tw28rCoqjuq6uq2/HPgJmABsBQ4s3U7Ezi6LS8FzqrOFcD8JHuPeNiSNKfN6DmLJIuBpwJXAntV1R2t6UfAXm15AbBm4GlrW23itpYlWZlk5fr164c2Zkmai2YsLJLsBvwT8Iaq+tlgW1UVUFuzvapaXlXjVTU+NjY2jSOVJM1IWCR5MF1QnF1Vn2vlH286vNR+3tnq64BFA09f2GqSpBGZiauhApwG3FRVfzvQtAI4ri0fB5w/UD+2XRV1EHDPwOEqSdIIzJuB1zwEeAVwfZJrWu0dwPuAzyQ5AbgNeHFruwA4ClgN3Au8crTDlSSNPCyq6utANtN82CT9CzhxqIOSJG2R3+CWJPUyLCRJvQwLSVIvw0KS1MuwkCT1MiwkSb0MC0lSL8NCktTLsJAk9TIsJEm9DAtJUi/DQpLUy7CQJPUyLCRJvQwLSVIvw0KS1MuwkCT1MiwkSb0MC0lSL8NCktTLsJAk9TIsJEm9tpuwSHJEku8kWZ3kpJkejyTNJdtFWCTZCfgYcCSwH/CyJPvN7Kgkae7YLsICOABYXVW3VtV9wLnA0hkekyTNGamqmR5DryTHAEdU1R+39VcAB1bVawf6LAOWtdUnAN8Z+UDnrj2Bn8z0ILTD8vM1Oo+tqrHJGuaNeiTDUlXLgeUzPY65KMnKqhqf6XFox+Tna3bYXg5DrQMWDawvbDVJ0ghsL2FxFbAkyb5JdgZeCqyY4TFJ0pyxXRyGqqqNSV4LXAjsBJxeVTfO8LB0Pw//aZj8fM0C28UJbknSzNpeDkNJkmaQYSFJ6mVYzBFJTknyhoH1C5N8cmD9b5K8cSu3eUb7Dsx0jnNxkhumc5savpn6fCU5NMm/bP2ItbUMi7njG8DBAEkeRPdFpycNtB8MXDYD49KOwc/XDs6wmDsuA57Rlp8E3AD8PMkeSXYBfhuoJF9Nsqr9Zrg3QJLHJfliq38tyRMnbjzJu9tvgjsleUuSq5Jcl+S/tfbFSW5K8okkNyb5UpKHtranJ7k2ybXAiSP4s9D0G+Xn64gkNye5GviDgT4HJLk8ybeSXJbkCa1+aZKnDPT7epInD+1PYgdlWMwRVfVDYGOSfeh+y7scuJLuH/g4cBNwCnBMVT0dOB14b3v6cuB1rf5m4O8Ht53kA8AY8ErgMGAJ3XxeTwGenuTZresS4GNV9STgbuAPW/1Tbfv+A95OjfDz9WDgE8DzgacDvzHQ9WbgWVX1VOAvgL9q9dOA49u2fgt4SFVdO13vfa7YLr5noWlzGd0/5IOBvwUWtOV76L4RfzhwURLovs9yR5LdWp/PtjrALgPb/HPgyqpaBpDk8Ladb7X23ehC4nbg+1V1TauvAhYnmQ/Mr6pLW/0f6GYX1vZnFJ+vJ9J9jm5p6/+T++eE2x04M8kSoOiCBeCzwJ8neQvwKuCMaX3Xc4RhMbdsOq78O3SHCdYAbwJ+BnwFWFBVzxh8QpJHAHdX1VOY3FV0ew+PrKoNQIC/rqqPT9jOYuDfBkq/BB76AN+PZpdRfL625N3AJVX1wvZ5+wpAVd2b5CK6mapfTLdHoq3kYai55TLgecCGqvpl+8c3n+5QwTnAWJJnACR5cJInVdXPgO8neVGrZ8Lx3i8C7wO+kOThdN+yf1X7jZEkC5I8enMDqqq7gbuTPLOVXj6db1gjNYrP1810e6SPa+0vG+i7O/fPGXf8hLF9EvgIcFVV3TU9b3duMSzmluvprlK5YkLtnqq6EzgGeH870XwN7eoWuv/AT2j1G5lwL5Gq+izdceQVwNeAfwQuT3I9cB7w8J5xvRL4WJJr6PZMtH0axecrdIedvtBOcN850PW/A3+d5FtMOGpSVavo9nA+NQ3vc05yug9JO7wkj6E7LPXEqvrVDA9nu+SehaQdWpJj6a7MeqdBse3cs5Ak9XLPQpLUy7CQJPUyLCRJvQwLSVIvw0I7rCR/meTNQ36NJya5pk1e97hJ2ucnec3A+qycUjvJ8Uk+OtPj0OxlWEgPzNHAeVX11Kr63iTt84HXTFKXtiuGhXYoSd6Z5LtJvg5smqL61W3K9GuT/FOSXZM8PMn3kzy49XnE4Pok231KkivatOufb1NvHwW8AfjPSS7ZzJDeBzyu7X18oNV2S3Jem2b77LQZ9NJN1f5rU3hPMpZHJ1nVlp+cpNpsryT5Xnt/Y+29XtUeh7T2hyU5Pck3297Q0km2//vppvrec2p/6poTqsqHjx3iQTdB3PXArsAjgNV0U14/aqDPe+imw4Zu6oej2/Iy4G+2sO3rgP/Ylt8FfKgt/yXw5i08bzFww8D6oXSzsC6k+2XtcuCZdDOkXgaMtX4vAU7fwnZvbO/xtXST7b0ceCxweWv/R+CZbXkf4Ka2/FfAH7Xl+cB3gYfRzaX0UeCFdFO27DHTf58+ZtfDWWe1I3kW8PmquhcgyYpW3z/Je+j+c9yNbrJD6CaXeyvwv+jmp3r1ZBtNsjvdNOpfbaUz6aa93lbfrKq1bdvX0AXK3cD+TJjCewvbuAw4BHg2XQAcQTdv0tda++8B+w1M+/2INrnj4cALBs7lPIQuTACeQ3fvicOrm+BP+v8MC80FZ9DtQVyb5Hi63+6pqm+ku4PfocBOVTWqe39PnKp9Ht1/9DfWhCm8t+BSunB8LHA+8Da6ezh8obU/CDioqn4x+KR2yOsPq+o7E+oHAt8DfhP4LWDl1rwh7fg8Z6EdyaXA0Uke2qazfn6rP5zuRjsP5tenQD+L7pDNZmcjrap7gLuSPKuVXgF8dXP9J/g5/bPuAnyHSabw3kL/rwF/BNxS3XxHG4CjgK+39i8Br9vUOfffVvRC4HUD50meOrDN2+juXnhWz2trDjIstMOoqquBTwPXAv9Kdywf2t3W6G7Oc/OEp50N7EF3v4UtOQ74QJLr6G4X+64pjumnwDeS3DBwgnuyfvex+Sm8J+v/A7q9kU13GPw63U2ENt2r4c+A8XZC/tvAn7b6u+nOj1yX5Ma2Prjdm+kC9bOTXQqsucuJBDWnJTkGWFpVr5jpsUizmecsNGcl+Tu6+30fNdNjkWY79yykAUk+RneV0aAPV9UW77CW5FHAxZM0HdYORY10PNJ0MywkSb08wS1J6mVYSJJ6GRaSpF6GhSSp1/8DBQBMDfMpTOMAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(data=fd_data, x='food_preparation_time', bins=5);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "YU3YWVCAM0oh",
        "outputId": "137cd0cf-2edf-4cea-c36f-29ffd46ccfb0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEHCAYAAABBW1qbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAUdElEQVR4nO3de7RkZX3m8e8jDajxAkiHIN1Oo5DJZGUiOu0NHcfAJIPGBCcj3hjFDA6Jl3jLqBjXipg1f6BmJMGZaFjiCjosBdEENMaIgpeo4HQjFxEdOwbsBpT2hhpHXcBv/tjveSnb0+ccu7tqV/f5ftaqdfZ+9659njqrq5+qXbv2TlUhSRLAPcYOIEmaH5aCJKmzFCRJnaUgSeosBUlSt2bsALvj0EMPrQ0bNowdQ5L2Kps3b/5GVa1dbNleXQobNmxg06ZNY8eQpL1Kkpt2tszdR5KkzlKQJHWWgiSpsxQkSZ2lIEnqLAVJUmcpSJI6S0GS1FkKkqRur/5GsyQBHLH+QdyybevYMWbqgevWc/PWr+7x7VoKkvZ6t2zbytP/8tNjx5ipC37v2Kls191HkqTOUpAkdZaCJKmzFCRJnaUgSeosBUlSZylIkjpLQZLUWQqSpM5SkCR1loIkqbMUJEmdpSBJ6iwFSVJnKUiSOktBktRZCpKkzlKQJHWWgiSpsxQkSZ2lIEnqLAVJUjf1UkiyX5LPJflAmz8yyZVJtiS5IMkBbfzANr+lLd8w7WySpJ80i3cKLwFumJh/PXBWVR0FfBs4tY2fCny7jZ/V1pMkzdBUSyHJOuA3gbe1+QDHARe1Vc4DntKmT2zztOXHt/UlSTMy7XcKfwa8ErirzT8A+E5V3dHmtwFHtOkjgK0Abfntbf2fkOS0JJuSbNq+ffs0s0vSqjO1UkjyZOC2qtq8J7dbVedU1caq2rh27do9uWlJWvXWTHHbjwV+O8mTgHsC9wP+HDgoyZr2bmAdcHNb/2ZgPbAtyRrg/sA3p5hPkrSDqb1TqKpXV9W6qtoAPAO4rKpOBi4HntpWOwW4uE1f0uZpyy+rqppWPknSTxvjewqvAl6eZAvDZwbntvFzgQe08ZcDp4+QTZJWtWnuPuqq6mPAx9r0V4BHLrLOD4GTZpFHkrQ4v9EsSeosBUlSZylIkjpLQZLUWQqSpG4mRx/NoyPWP4hbtm0dO8ZMPXDdem7e+tWxY0iaY6u2FG7ZtpWn/+Wnx44xUxf83rFjR5A059x9JEnqLAVJUmcpSJI6S0GS1FkKkqTOUpAkdav2kFStDn4fRfrZWAqryT3WkGTsFDPn91GklbMUVpO77vA/SElL8jMFSVJnKUiSOktBktT5mYK0r1mlBxRoz7AUpH2NBxRoN7j7SJLUWQqSpM5SkCR1loIkqbMUJEmdpSBJ6iwFSVJnKUiSOktBktRZCpKkzlKQJHWWgiSpsxQkSZ2lIEnqLAVJUje1UkhyzySfTXJNkuuTvK6NH5nkyiRbklyQ5IA2fmCb39KWb5hWNknS4qb5TuFHwHFV9VDgGOCEJI8GXg+cVVVHAd8GTm3rnwp8u42f1daTJM3Q1EqhBt9vs/u3WwHHARe18fOAp7TpE9s8bfnx8ZqCkjRTU/1MIcl+Sa4GbgMuBf4R+E5V3dFW2QYc0aaPALYCtOW3Aw9YZJunJdmUZNP27dunGV+SVp2plkJV3VlVxwDrgEcCv7QHtnlOVW2sqo1r167d7YySpLvN5OijqvoOcDnwGOCgJGvaonXAzW36ZmA9QFt+f+Cbs8gnSRpM8+ijtUkOatP3An4duIGhHJ7aVjsFuLhNX9Lmacsvq6qaVj5J0k9bs/wqu+xw4Lwk+zGUz4VV9YEkXwDeneS/A58Dzm3rnwu8M8kW4FvAM6aYTZK0iKmVQlVdCzxskfGvMHy+sOP4D4GTppVHkrQ8v9EsSeosBUlSZylIkjpLQZLUWQqSpM5SkCR1loIkqbMUJEndikohyWNXMiZJ2rut9J3Cm1c4Jknaiy15moskjwGOBdYmefnEovsB+00zmCRp9pY799EBwH3aevedGP8ud5/pVJK0j1iyFKrq48DHk/xVVd00o0ySpJGs9CypByY5B9gweZ+qOm4aoSRJ41hpKbwHeCvwNuDO6cWRJI1ppaVwR1W9ZapJJEmjW+khqe9P8oIkhyc5ZOE21WSSpJlb6TuFhWsnv2JirIAH79k4kqQxragUqurIaQeRJI1vRaWQ5DmLjVfVO/ZsHEnSmFa6++gRE9P3BI4HrgIsBUnah6x099EfTM4nOQh491QSSZJGs6unzv5nwM8ZJGkfs9LPFN7PcLQRDCfC+1fAhdMKJUkax0o/U/jTiek7gJuqatsU8kiSRrSi3UftxHhfZDhT6sHAj6cZSpI0jpVeee1pwGeBk4CnAVcm8dTZkrSPWenuo9cAj6iq2wCSrAU+Alw0rWCSpNlb6dFH91gohOabP8N9JUl7iZW+U/hQkr8H3tXmnw58cDqRJEljWe4azUcBh1XVK5L8DvC4tugzwPnTDidJmq3l3in8GfBqgKp6H/A+gCT/ui37rammkyTN1HKfCxxWVdftONjGNkwlkSRpNMuVwkFLLLvXngwiSRrfcqWwKcl/3XEwyfOAzdOJJEkay3KfKbwU+OskJ3N3CWwEDgD+4zSDSZJmb8l3ClX19ao6FngdcGO7va6qHlNVX1vqvknWJ7k8yReSXJ/kJW38kCSXJvly+3lwG0+Ss5NsSXJtkofviQcoSVq5lV5P4XLg8p9x23cAf1hVVyW5L7A5yaXAc4GPVtWZSU4HTgdeBTwROLrdHgW8pf2UJM3I1L6VXFW3VtVVbfp7wA3AEcCJwHlttfOAp7TpE4F31OAK4KAkh08rnyTpp83kVBVJNgAPA65kOMz11rboa8BhbfoIYOvE3ba1sR23dVqSTUk2bd++fWqZJWk1mnopJLkP8F7gpVX13cllVVXcffGeFamqc6pqY1VtXLt27R5MKkmaaikk2Z+hEM5v34gG+PrCbqH2c+FEezcD6yfuvq6NSZJmZGqlkCTAucANVfWmiUWXAKe06VOAiyfGn9OOQno0cPvEbiZJ0gys9Cypu+KxwLOB65Jc3cb+CDgTuDDJqcBNDBftgeGsq08CtgA/AH53itkkSYuYWilU1T8A2cni4xdZv4AXTiuPJGl5XihHktRZCpKkzlKQJHWWgiSpsxQkSZ2lIEnqLAVJUmcpSJI6S0GS1FkKkqTOUpAkdZaCJKmzFCRJnaUgSeosBUlSZylIkjpLQZLUWQqSpM5SkCR1loIkqbMUJEmdpSBJ6iwFSVJnKUiSOktBktRZCpKkzlKQJHWWgiSpsxQkSZ2lIEnqLAVJUmcpSJI6S0GS1FkKkqTOUpAkdZaCJKmbWikkeXuS25J8fmLskCSXJvly+3lwG0+Ss5NsSXJtkodPK5ckaeem+U7hr4ATdhg7HfhoVR0NfLTNAzwROLrdTgPeMsVckqSdmFopVNUngG/tMHwicF6bPg94ysT4O2pwBXBQksOnlU2StLhZf6ZwWFXd2qa/BhzWpo8Atk6st62N/ZQkpyXZlGTT9u3bp5dUklah0T5orqoCahfud05VbayqjWvXrp1CMklavWZdCl9f2C3Uft7Wxm8G1k+st66NSZJmaNalcAlwSps+Bbh4Yvw57SikRwO3T+xmkiTNyJppbTjJu4AnAIcm2Qa8FjgTuDDJqcBNwNPa6h8EngRsAX4A/O60ckmSdm5qpVBVz9zJouMXWbeAF04riyRpZfxGsySpsxQkSZ2lIEnqLAVJUmcpSJI6S0GS1FkKkqTOUpAkdZaCJKmzFCRJnaUgSeosBUlSZylIkjpLQZLUWQqSpM5SkCR1loIkqbMUJEmdpSBJ6iwFSVJnKUiSOktBktRZCpKkzlKQJHWWgiSpsxQkSZ2lIEnqLAVJUmcpSJI6S0GS1FkKkqTOUpAkdZaCJKmzFCRJnaUgSeosBUlSN1elkOSEJF9KsiXJ6WPnkaTVZm5KIcl+wP8Cngj8MvDMJL88bipJWl3mphSARwJbquorVfVj4N3AiSNnkqRVJVU1dgYAkjwVOKGqntfmnw08qqpetMN6pwGntdl/CXxpF3/locA3dvG+szLvGec9H5hxT5j3fDD/Gect37+oqrWLLVgz6yS7q6rOAc7Z3e0k2VRVG/dApKmZ94zzng/MuCfMez6Y/4zznm/SPO0+uhlYPzG/ro1JkmZknkrh/wBHJzkyyQHAM4BLRs4kSavK3Ow+qqo7krwI+HtgP+DtVXX9FH/lbu+CmoF5zzjv+cCMe8K854P5zzjv+bq5+aBZkjS+edp9JEkamaUgSepWRSkkWZ/k8iRfSHJ9kpe08UOSXJrky+3nwXOW741Jvpjk2iR/neSgMfItlXFi+R8mqSSHzlu+JH/Q/o7XJ3nDGPmWypjkmCRXJLk6yaYkjxwx4z2TfDbJNS3j69r4kUmubKeguaAdDDJP+c5vp8j5fJK3J9l/jHxLZZxYfnaS74+Vb1lVtc/fgMOBh7fp+wL/l+FUGm8ATm/jpwOvn7N8vwGsaeOvHyvfUhnb/HqGAwRuAg6dp3zArwEfAQ5sy35+3v6GwIeBJ7bxJwEfGzFjgPu06f2BK4FHAxcCz2jjbwWeP2f5ntSWBXjXWPmWytjmNwLvBL4/Vr7lbqvinUJV3VpVV7Xp7wE3AEcwnEbjvLbaecBT5ilfVX24qu5oq13B8N2NUSzxNwQ4C3glMNpRC0vkez5wZlX9qC27bQ4zFnC/ttr9gVvGSQg1WHgVu3+7FXAccFEbH/O5smi+qvpgW1bAZxn3ubJoxnZ+tzcyPFfm1qoohUlJNgAPY2jvw6rq1rboa8BhI8Xqdsg36b8AfzfrPIuZzJjkRODmqrpm1FATdvgb/iLwb9uuj48necSY2RbskPGlwBuTbAX+FHj1eMmGk1MmuRq4DbgU+EfgOxMvULZx9wuC0fNV1ZUTy/YHng18aKx8LcdiGV8EXDLxf85cWlWlkOQ+wHuBl1bVdyeXtVcYox6fu7N8SV4D3AGcP1a2iSw9I0OmPwL+eNRQExb5G64BDmHYxfAK4MIkGTHiYhmfD7ysqtYDLwPOHTNfVd1ZVccwvNp+JPBLY+bZ0Y75kvzKxOK/AD5RVZ8cJ91gkYyPB04C3jxmrpVYNaXQXkG8Fzi/qt7Xhr+e5PC2/HCGVp+nfCR5LvBk4ORWXKNZJONDgCOBa5LcyPAEuCrJL8xJPhhe1b6vvaX/LHAXw8nJRrGTjKcAC9PvYfiPeHRV9R3gcuAxwEFJFr7sOhenoJnIdwJAktcCa4GXj5lr0kTGXwOOAra058q9k2wZM9vOrIpSaK8MzwVuqKo3TSy6hOEJSft58ayzwc7zJTmBYf/jb1fVD8bINpHlpzJW1XVV9fNVtaGqNjD8B/zwqvraPORr/obhCUmSXwQOYKSzVS6R8Rbg37Xp44AvzzrbgiRrF45yS3Iv4NcZPvu4HHhqW23M58pi+b6Y5HnAfwCeWVV3jZFtmYybq+oXJp4rP6iqo8bMuTOr4hvNSR4HfBK4juGVIgy7Pa5kOKriQQxHzjytqr41R/nOBg4EvtnGrqiq3591Pth5xqr64MQ6NwIbq2rm/+ku8Tf8CPB24Bjgx8B/q6rLZp1vmYzfBf6cYVfXD4EXVNXmkTL+KsMHyfsxvGi8sKr+JMmDGa5xcgjwOeA/L3x4Pyf57mB4Dn+vrfq+qvqTWedbKuMO63y/qu4zRr7lrIpSkCStzKrYfSRJWhlLQZLUWQqSpM5SkCR1loIkqbMUJEmdpaDRJHlxkhuS7NbpO5LcmJFO2b07kmxI8qyJ+Y1Jzt6D2z8oyQsm5h+Y5KKl7iP5PQWNJskXgX9fVdt2czs3sptfmkuyX1XduTs5drLdNRMnkttx2RMYvkz35D39e9v2NwAfqKpfWWZVqfOdgkaR5K3Ag4G/y3CBnr/JcDGhK9o3QhcugrTY+AOSfLhdwORtDOev39nv2ZDhAjvnt3clFyW5d1t2Y5LXJ7kKOCnJbyT5TJKrkrynnbhuYb03JLkuw8VTjmrjv9XOvvq5JB9JclgbPyPJO5N8Cnhny/DJtt2rkhzb4p3JcAbXq5O8LMkTknxgmcd+RoaLyHwsyVeSvHiJP/OZwEPa9t/Ycny+bee5bfuXtsf3oiQvb4/liiSHtPUekuRDSTa3xzBXJ8fTFOzqhRi8edvdG3Ajw8np3gy8to0dB1zdpnc2fjbwx236NxnObrvoxX2ADW35Y9v82xlenS/8/le26UOBTwA/1+ZfNfE7bgRe06afw/DqG+Bg7n63/Tzgf7TpM4DNwL3a/L2Be7bpo4FNbfoJC9vacX6Jx34G8GmG058cynAKlP2XeOyfX2weeC6wheFiP2uB24Hfb8vOYjiDK8BHgaPb9KOAy8b+d+NtureFsx5KY3oc8J8Aquqy9k7gfkuMPx74nTb+t0m+vcz2t1bVp9r0/wZezHDdAoAL2s9HM1wF7VPDees4APjMxDbeNfHzrDa9Drggwxl2DwD+aWL9S6rq/7Xp/YH/meQY4E6GazwsZ2ePHeBvazjv0I+S3MZwHZBd2QV3eQ0X+/lektuB97fx64Bfbe+UjgXek7vPNn7gLvwe7UUsBa0GO35wNjn/z+1nGC6G8swVbGNh+s3Am6rqkvb5wBmLbBeGayR8HXgowy7bH644+eImT0R3J7v+PJ7czl0T83e1bd6D4eI6x+zi9rUX8jMFzYNPAidD//D1GzVcfGZn458AntXGn8iwG2cpD0rymDb9LOAfFlnnCuCxE58X/FyGU20vePrEz4V3EPfn7usKnMLO3R+4tYZTOj+b4eyZMJzR8747uc/OHvvPYqntL6v9vn9KclLLkSQP3dXtae9gKWgenAH8myTXMnw4esoy468DHp/keobdSF9dZvtfAl6Y5AaGAnnLjitU1XaG/ezvar/vM/zkFccObuMvYXjlv5DvPUk2s/Q1Gv4COCXJNW2bC+8irgXuTHJNkpftcJ8zWPyxr1hVfZNhd9jnk7zxZ71/czJwast+PcN1zbUP85BU7dP2xGGZe+KQV2lv4TsFSVLnOwXtE5I8gOHwyR0d33aj7LNW82PXnmcpSJI6dx9JkjpLQZLUWQqSpM5SkCR1/x/zdHsssCm5JgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(data=fd_data, x='delivery_time', bins=5);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "G2-uDChtM4pe",
        "outputId": "fc2deed3-aca9-4b21-9395-b13a28459185"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K9CqA5H_JY_U"
      },
      "source": [
        "### **Question 7**: Which are the top 5 restaurants in terms of the number of orders received? [1 mark]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PaJZ3wlOJY_U",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5cda22b2-1b48-46ec-b8d7-d354e022b578"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Shake Shack                  219\n",
              "The Meatball Shop            132\n",
              "Blue Ribbon Sushi            119\n",
              "Blue Ribbon Fried Chicken     96\n",
              "Parm                          68\n",
              "                            ... \n",
              "Sushi Choshi                   1\n",
              "Dos Caminos Soho               1\n",
              "La Follia                      1\n",
              "Philippe Chow                  1\n",
              "'wichcraft                     1\n",
              "Name: restaurant_name, Length: 178, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "fd_data['restaurant_name'].unique()\n",
        "fd_data['restaurant_name'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tnjhWgUfJY_U"
      },
      "source": [
        "#### Observations: The top 5 Restaurants in terms of number of orders received are\n",
        "\n",
        "\n",
        "*   Shake Shack\n",
        "*   The Meatball Shop\n",
        "*   Blue Ribbon Sushi\n",
        "*   Blue Ribbon Fried Chicken\n",
        "*   Parm\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gWPMqGq0JY_U"
      },
      "source": [
        "### **Question 8**: Which is the most popular cuisine on weekends? [1 mark]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c-Xnus3VJY_U",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "outputId": "8d5aa1a3-e2d5-4092-c92e-4b122609e9ce"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The most popular cuisine on the weekends going by the Countplot below is American\n"
          ]
        }
      ],
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "sns.countplot(data=fd_data, x='cuisine_type', hue='day_of_the_week', palette='inferno')\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()\n",
        "print('The most popular cuisine on the weekends going by the Countplot below is American')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bpQ270TUJY_U"
      },
      "source": [
        "#### Observations:\n",
        "The most popular cuisine on the weekends going by the Countplot below is American"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "88xgTqkxJY_U"
      },
      "source": [
        "### **Question 9**: What percentage of the orders cost more than 20 dollars? [2 marks]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OTpHyTdzJY_U",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "outputId": "9c8e62da-22fa-4dc7-cf4b-9a6c152ff174"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   order_id  customer_id            restaurant_name cuisine_type  \\\n",
              "0   1477147       337525                    Hangawi       Korean   \n",
              "1   1477685       358141  Blue Ribbon Sushi Izakaya     Japanese   \n",
              "2   1477070        66393                Cafe Habana      Mexican   \n",
              "3   1477334       106968  Blue Ribbon Fried Chicken     American   \n",
              "4   1478249        76942           Dirty Bird to Go     American   \n",
              "\n",
              "   cost_of_the_order day_of_the_week  rating  food_preparation_time  \\\n",
              "0             30.750         Weekend     NaN                     25   \n",
              "1             12.080         Weekend     NaN                     25   \n",
              "2             12.230         Weekday   5.000                     23   \n",
              "3             29.200         Weekend   3.000                     25   \n",
              "4             11.590         Weekday   4.000                     25   \n",
              "\n",
              "   delivery_time  Revenue_line_one  Total_time Total_time_rating  Cost_type  \n",
              "0             20             7.688          45        Under Hour   Over $20  \n",
              "1             23             1.812          48        Under Hour  Under $20  \n",
              "2             28             1.835          51        Under Hour  Under $20  \n",
              "3             15             7.300          40        Under Hour   Over $20  \n",
              "4             24             1.738          49        Under Hour  Under $20  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-eff8496e-ea56-4527-a3fd-037cc71d27e3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>Revenue_line_one</th>\n",
              "      <th>Total_time</th>\n",
              "      <th>Total_time_rating</th>\n",
              "      <th>Cost_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1477147</td>\n",
              "      <td>337525</td>\n",
              "      <td>Hangawi</td>\n",
              "      <td>Korean</td>\n",
              "      <td>30.750</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25</td>\n",
              "      <td>20</td>\n",
              "      <td>7.688</td>\n",
              "      <td>45</td>\n",
              "      <td>Under Hour</td>\n",
              "      <td>Over $20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1477685</td>\n",
              "      <td>358141</td>\n",
              "      <td>Blue Ribbon Sushi Izakaya</td>\n",
              "      <td>Japanese</td>\n",
              "      <td>12.080</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25</td>\n",
              "      <td>23</td>\n",
              "      <td>1.812</td>\n",
              "      <td>48</td>\n",
              "      <td>Under Hour</td>\n",
              "      <td>Under $20</td>\n",
              "    </tr>\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>12.230</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>5.000</td>\n",
              "      <td>23</td>\n",
              "      <td>28</td>\n",
              "      <td>1.835</td>\n",
              "      <td>51</td>\n",
              "      <td>Under Hour</td>\n",
              "      <td>Under $20</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>29.200</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>3.000</td>\n",
              "      <td>25</td>\n",
              "      <td>15</td>\n",
              "      <td>7.300</td>\n",
              "      <td>40</td>\n",
              "      <td>Under Hour</td>\n",
              "      <td>Over $20</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>11.590</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>4.000</td>\n",
              "      <td>25</td>\n",
              "      <td>24</td>\n",
              "      <td>1.738</td>\n",
              "      <td>49</td>\n",
              "      <td>Under Hour</td>\n",
              "      <td>Under $20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eff8496e-ea56-4527-a3fd-037cc71d27e3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-eff8496e-ea56-4527-a3fd-037cc71d27e3 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-eff8496e-ea56-4527-a3fd-037cc71d27e3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "#fd_data['Cost_Type'] = fd_data['food_preparation_time'] + fd_data['delivery_time']\n",
        "def costing(i):\n",
        "  if i>20:\n",
        "    return 'Over $20'\n",
        "  elif 0<i<20:\n",
        "    return'Under $20'\n",
        "  else:\n",
        "    'N/A'\n",
        "fd_data['Cost_type'] = fd_data['cost_of_the_order'].apply(costing)\n",
        "fd_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fd_data['Cost_type'].value_counts(normalize=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c_QD8VpPjBD1",
        "outputId": "a8da987c-9b92-4b59-b1df-7be9c82810b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Under $20   0.708\n",
              "Over $20    0.292\n",
              "Name: Cost_type, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "77KNAC5zJY_U"
      },
      "source": [
        "#### Observations: The percentage of orders over $20 is 29.2% or 555 orders\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HFyHn7hsJY_V"
      },
      "source": [
        "### **Question 10**: What is the mean order delivery time? [1 mark]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cyFL13hNJY_V",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "15d43a08-a137-45e4-f274-c824f4624c1f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Order Delivery Time = 24.161749209694417 mins\n"
          ]
        }
      ],
      "source": [
        "print('Mean Order Delivery Time =', fd_data['delivery_time'].mean(),'mins')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F35SRV-HJY_V"
      },
      "source": [
        "#### Observations: The mean order delivery time is 24.16mins\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. [1 mark]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Hz8-DE4Zge0j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "031d49f6-5df0-4272-80c3-6b8f405afe72"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "52832    13\n",
              "47440    10\n",
              "83287     9\n",
              "Name: customer_id, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "fd_data['customer_id'].unique()\n",
        "fd_data['customer_id'].value_counts().head(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9tWVwfX6JY_V"
      },
      "source": [
        "#### Observations: The top 3 customers as highlighted above are \n",
        "\n",
        "\n",
        "*   Customer_id 52832 - 13 orders\n",
        "*   Customer_id 47440 - 10 orders\n",
        "*   Customer_id 83287 - 9 orders\n",
        "\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. (It is a good idea to explore relations between numerical variables as well as relations between numerical and categorical variables) [10 marks]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3qt4MCNTJY_V",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "outputId": "12687d3b-cca8-47ae-ea42-d1fac2a3d210"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.heatmap(data=fd_data[['cost_of_the_order','rating','food_preparation_time','delivery_time']].corr(), annot=True, cmap='YlGnBu');"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(data=fd_data[['cost_of_the_order','food_preparation_time','delivery_time']]);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "id": "cGtjpmbgOLmu",
        "outputId": "2bd53396-eb82-48d5-8893-012272e55657"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 540x540 with 12 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,7))\n",
        "sns.boxplot(data=fd_data, x='cuisine_type', y='cost_of_the_order');\n",
        "plt.title('Order Cost vs Cuisine Type')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "0KpTBXckORtn",
        "outputId": "5790bb19-75ac-4ea8-aa30-1df4543c2b4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Order Cost vs Cuisine Type')"
            ]
          },
          "metadata": {},
          "execution_count": 69
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,5))\n",
        "g = sns.FacetGrid(data=fd_data, col=\"cuisine_type\", col_wrap=7)\n",
        "g.map(sns.histplot, \"cost_of_the_order\");\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "0aHaTLeoOWnR",
        "outputId": "82fc8060-a6d1-4847-a3f0-d7636b56daa2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1512x432 with 14 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,5))\n",
        "g = sns.FacetGrid(data=fd_data, col=\"cuisine_type\", col_wrap=7)\n",
        "g.map(sns.histplot, \"delivery_time\");\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "R2kz0b1dObpV",
        "outputId": "af5a808f-0309-4250-f7d7-8dbda9802aac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1512x432 with 14 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,5))\n",
        "g = sns.FacetGrid(data=fd_data, col=\"cuisine_type\", col_wrap=7)\n",
        "g.map(sns.histplot, \"food_preparation_time\");\n",
        "plt.title('Food Preparation Time vs Cuisine Type')\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "kWbPAc9iOfJv",
        "outputId": "5c2827f7-4f19-431e-a86c-0e5d2f41e08b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1512x432 with 14 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,7))\n",
        "plt.title('Barplot:Most Popular Ratings')\n",
        "plt.ylim(0,180)\n",
        "sns.countplot(data=fd_data, x='cuisine_type',hue='rating')\n",
        "plt.xticks(rotation=90)\n",
        "plt.xlabel('Cuisine Type');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "o7uIw2OVOkrC",
        "outputId": "2e725ef3-e3cc-4b5b-a31a-0bf9f8ec1ddf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "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. [3 marks]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0M_QDvbYJY_V",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b371c014-6265-45d7-c192-4363ec1f5b17"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "restaurant_name\n",
              "'wichcraft         5.000\n",
              "12 Chairs          4.500\n",
              "5 Napkin Burger    4.000\n",
              "67 Burger          5.000\n",
              "Alidoro              NaN\n",
              "                    ... \n",
              "Zero Otto Nove     4.000\n",
              "brgr               3.000\n",
              "da Umberto         5.000\n",
              "ilili Restaurant   4.154\n",
              "indikitch          4.500\n",
              "Name: rating, Length: 178, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "fd_data['rating'] = fd_data['rating'].replace(['Not given'],np.nan)\n",
        "fd_data['rating'] = fd_data['rating'].astype(float)\n",
        "fd_data_rating = fd_data.groupby(['restaurant_name'])['rating'].mean()\n",
        "fd_data_rating"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('The Mean of Shake Shack orders is equal to', fd_data_rating['Shake Shack'])\n",
        "print('The Mean of The Meatball Shop orders is equal to', fd_data_rating['The Meatball Shop'])\n",
        "print('The Mean of Blue Ribbon Sushi orders is equal to', fd_data_rating['Blue Ribbon Sushi'])\n",
        "print('The Mean of Blue Ribbon Fried Chicken orders is equal to', fd_data_rating['Blue Ribbon Fried Chicken'])\n",
        "print('The Mean of Parm orders is equal to', fd_data_rating['Parm'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PkCEAkGBd4sa",
        "outputId": "0db8a7df-86c1-4796-e50f-5923445d4239"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Mean of Shake Shack orders is equal to 4.2781954887218046\n",
            "The Mean of The Meatball Shop orders is equal to 4.511904761904762\n",
            "The Mean of Blue Ribbon Sushi orders is equal to 4.219178082191781\n",
            "The Mean of Blue Ribbon Fried Chicken orders is equal to 4.328125\n",
            "The Mean of Parm orders is equal to 4.128205128205129\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "10uryUMhJY_V"
      },
      "source": [
        "#### Observations: Looking at the top 5 restaurants which all have orders ratings over 50 and mean values over 4, the following restaurants are eligible for the promotional offer\n",
        "*   Shake Shack\n",
        "*   The Meatball Shop\n",
        "*   Blue Ribbon Sushi\n",
        "*   Blue Ribbon Fried Chicken\n",
        "*   Parm\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. [3 marks]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rEjbnTNEJY_W"
      },
      "outputs": [],
      "source": [
        "def revenue_a(i):\n",
        "  if i>20:\n",
        "    return i*0.25 \n",
        "  elif 5<i<20:\n",
        "    return i*0.15\n",
        "  else:\n",
        "    0\n",
        "  #This function defines the parameters in the question 25% charge is done on orders over $20 while 15% charge is done on orders greater than $5 but less than $20"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fd_data['Revenue_line_one'] = fd_data['cost_of_the_order'].apply(revenue_a)\n",
        "fd_data.head()\n",
        "#A new column is added with the charges on the cost of the order in line with the revenue percentages"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "8anVXLU1gCmu",
        "outputId": "684b7d14-4e0b-48d2-8e19-a2c49dc154f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   order_id  customer_id            restaurant_name cuisine_type  \\\n",
              "0   1477147       337525                    Hangawi       Korean   \n",
              "1   1477685       358141  Blue Ribbon Sushi Izakaya     Japanese   \n",
              "2   1477070        66393                Cafe Habana      Mexican   \n",
              "3   1477334       106968  Blue Ribbon Fried Chicken     American   \n",
              "4   1478249        76942           Dirty Bird to Go     American   \n",
              "\n",
              "   cost_of_the_order day_of_the_week  rating  food_preparation_time  \\\n",
              "0             30.750         Weekend     NaN                     25   \n",
              "1             12.080         Weekend     NaN                     25   \n",
              "2             12.230         Weekday   5.000                     23   \n",
              "3             29.200         Weekend   3.000                     25   \n",
              "4             11.590         Weekday   4.000                     25   \n",
              "\n",
              "   delivery_time  Revenue_line_one  \n",
              "0             20             7.688  \n",
              "1             23             1.812  \n",
              "2             28             1.835  \n",
              "3             15             7.300  \n",
              "4             24             1.738  "
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            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('The Total cost of all the orders is $',fd_data.cost_of_the_order.sum())\n",
        "print('The Net Revenue across the orders is $',fd_data.Revenue_line_one.sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dLV2e5XUgUhY",
        "outputId": "a721a371-35f5-4e6a-c04f-0c9d25b91a9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Total cost of all the orders is $ 31314.82\n",
            "The Net Revenue across the orders is $ 6166.303\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XHCEJgH0JY_W"
      },
      "source": [
        "#### Observations: The overall revenue generated from the cost of the orders is 7.7% across board. There may be potential to increase some of the fees charged per category of order\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? (The food has to be prepared and then delivered.) [2 marks]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zxoTrxfLJY_W",
        "colab": {
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          "height": 462
        },
        "outputId": "6b0fa1f6-46e5-4e8b-c35a-0e87f3f050bc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   order_id  customer_id            restaurant_name   cuisine_type  \\\n",
              "0   1477147       337525                    Hangawi         Korean   \n",
              "1   1477685       358141  Blue Ribbon Sushi Izakaya       Japanese   \n",
              "2   1477070        66393                Cafe Habana        Mexican   \n",
              "3   1477334       106968  Blue Ribbon Fried Chicken       American   \n",
              "4   1478249        76942           Dirty Bird to Go       American   \n",
              "5   1477224       147468           Tamarind TriBeCa         Indian   \n",
              "6   1477894       157711          The Meatball Shop        Italian   \n",
              "7   1477859        89574                  Barbounia  Mediterranean   \n",
              "8   1477174       121706         Anjappar Chettinad         Indian   \n",
              "9   1477311        39705              Bukhara Grill         Indian   \n",
              "\n",
              "   cost_of_the_order day_of_the_week  rating  food_preparation_time  \\\n",
              "0             30.750         Weekend     NaN                     25   \n",
              "1             12.080         Weekend     NaN                     25   \n",
              "2             12.230         Weekday   5.000                     23   \n",
              "3             29.200         Weekend   3.000                     25   \n",
              "4             11.590         Weekday   4.000                     25   \n",
              "5             25.220         Weekday   3.000                     20   \n",
              "6              6.070         Weekend     NaN                     28   \n",
              "7              5.970         Weekday   3.000                     33   \n",
              "8             16.440         Weekday   5.000                     21   \n",
              "9              7.180         Weekday   5.000                     29   \n",
              "\n",
              "   delivery_time  Revenue_line_one  Total_time Total_time_rating  \n",
              "0             20             7.688          45        Under Hour  \n",
              "1             23             1.812          48        Under Hour  \n",
              "2             28             1.835          51        Under Hour  \n",
              "3             15             7.300          40        Under Hour  \n",
              "4             24             1.738          49        Under Hour  \n",
              "5             24             6.305          44        Under Hour  \n",
              "6             21             0.910          49        Under Hour  \n",
              "7             30             0.895          63         Over Hour  \n",
              "8             26             2.466          47        Under Hour  \n",
              "9             26             1.077          55        Under Hour  "
            ],
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              "      <th>order_id</th>\n",
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              "      <td>1.835</td>\n",
              "      <td>51</td>\n",
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              "      <td>7.300</td>\n",
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              "      <td>Under Hour</td>\n",
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              "      <td>76942</td>\n",
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              "      <td>24</td>\n",
              "      <td>1.738</td>\n",
              "      <td>49</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1477224</td>\n",
              "      <td>147468</td>\n",
              "      <td>Tamarind TriBeCa</td>\n",
              "      <td>Indian</td>\n",
              "      <td>25.220</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>3.000</td>\n",
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              "      <td>24</td>\n",
              "      <td>6.305</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1477894</td>\n",
              "      <td>157711</td>\n",
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              "      <th>7</th>\n",
              "      <td>1477859</td>\n",
              "      <td>89574</td>\n",
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              "      <td>5.970</td>\n",
              "      <td>Weekday</td>\n",
              "      <td>3.000</td>\n",
              "      <td>33</td>\n",
              "      <td>30</td>\n",
              "      <td>0.895</td>\n",
              "      <td>63</td>\n",
              "      <td>Over Hour</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1477174</td>\n",
              "      <td>121706</td>\n",
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              "      <td>Weekday</td>\n",
              "      <td>5.000</td>\n",
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              "      <td>26</td>\n",
              "      <td>2.466</td>\n",
              "      <td>47</td>\n",
              "      <td>Under Hour</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
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              "      <td>39705</td>\n",
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              "      <td>Weekday</td>\n",
              "      <td>5.000</td>\n",
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              "      <td>26</td>\n",
              "      <td>1.077</td>\n",
              "      <td>55</td>\n",
              "      <td>Under Hour</td>\n",
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              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "fd_data['Total_time'] = fd_data['food_preparation_time'] + fd_data['delivery_time']\n",
        "def timescale(i):\n",
        "  if i>60:\n",
        "    return 'Over Hour'\n",
        "  elif 10<i<60:\n",
        "    return'Under Hour'\n",
        "  else:\n",
        "    'N/A'\n",
        "fd_data['Total_time_rating'] = fd_data['Total_time'].apply(timescale)\n",
        "fd_data.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fd_data['Total_time_rating'].value_counts(normalize=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sX1Knz-bhRto",
        "outputId": "85e65c56-a1fa-4e35-bf57-47910c13354f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Under Hour   0.892\n",
              "Over Hour    0.108\n",
              "Name: Total_time_rating, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iqzBXn2yJY_W"
      },
      "source": [
        "#### Observations: The percentage of orders that take more than 60mins from order to delivery is 10.8% or 200 orders in total. This shows that a majority of orders are delivered under an hour\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/"
        },
        "outputId": "af205eb8-ecaf-40ca-e947-3cfe73ae7e4c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "day_of_the_week\n",
              "Weekday   28.340\n",
              "Weekend   22.470\n",
              "Name: delivery_time, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "fd_data_day = fd_data.groupby(['day_of_the_week'])['delivery_time'].mean()\n",
        "fd_data_day"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8H-ODxbVJY_W"
      },
      "source": [
        "#### Observations: On average, it takes 28mins to deliver an order during the week and 22mins30sec to deliver an order on the weekend.\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fd_data.to_csv('/content/drive/MyDrive/DSBA/Project 1/foodhub_order_update.csv',index=False)"
      ],
      "metadata": {
        "id": "k9DRLiTmsxX7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "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? (You can use cuisine type and feedback ratings to drive your business recommendations.) [6 marks]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VTugVsL4Ddz-"
      },
      "source": [
        "### Conclusions:\n",
        "*  American cuisine is the most popular followed by Japanese and Italian. The bulk of the orders are generated over the weekend. No surprise as these cuisines also receive the best ratings.\n",
        "*   Only a low 29% of orders cost over $20. There could be an opportunity to improve on this.\n",
        "*   Feedback ratings for French, Korean and Vietnamese cuisine are quite low which could suggest a lack of pupularity across the target market.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PWnsI0i_guWJ"
      },
      "source": [
        "### Recommendations:\n",
        "\n",
        "*  Foodhub should drive the adoption of the more popular cuisines such as American, Japanese, Italian to increase order count. They could also look into increasing the amount of stores under these cuisines.\n",
        "*   A focus on the more popular cuisines could lead to a higher percentage of orders over $20 which will increase net revenue\n",
        "*   They could also make the feedback system more attractive to reduce the amount of 'Not Given' feedback instances. Maybe attach some loyalty points to providing some feedback.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tSoucKKbg9Hc"
      },
      "source": [
        "---"
      ]
    }
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