PYF_Project_LearnerNotebook_FullCode_complete.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"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cEtNUxNAJY_M"
      },
      "source": [
        "### Let us start by importing the required libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "u6PlpJAPJY_N"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AdracY0HJY_O"
      },
      "source": [
        "### Understanding the structure of the data"
      ]
    },
    {
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        "outputId": "20f8550e-95fc-48a5-be91-d5fae4a9fc33"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "GF01IZDbJY_O",
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          "height": 206
        },
        "outputId": "fe1705c4-ee3a-47cc-a87f-1d5a3222ac34"
      },
      "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              30.75         Weekend  Not given                     25   \n",
              "1              12.08         Weekend  Not given                     25   \n",
              "2              12.23         Weekday          5                     23   \n",
              "3              29.20         Weekend          3                     25   \n",
              "4              11.59         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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          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "path = '/content/drive/MyDrive/Colab Notebooks/foodhub_order.csv'\n",
        "df = pd.read_csv(path)\n",
        "df = df.astype({'restaurant_name': 'category', 'cuisine_type': 'category', 'day_of_the_week': 'category'})\n",
        "df.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 an 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]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Yvz5i-PhJY_Q",
        "outputId": "d9a66233-a2dc-4815-cb59-0327cc4d0152",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1898, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UsTNy3unJY_R"
      },
      "source": [
        "#### Observations:\n",
        "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? (The info() function can be used) [0.5 mark]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Kp70Z0BNJY_R",
        "outputId": "31b8f8ac-d02a-41a7-e752-8376deb5e937",
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      "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   category\n",
            " 3   cuisine_type           1898 non-null   category\n",
            " 4   cost_of_the_order      1898 non-null   float64 \n",
            " 5   day_of_the_week        1898 non-null   category\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: category(3), float64(1), int64(4), object(1)\n",
            "memory usage: 102.7+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WJes5GP2JY_S"
      },
      "source": [
        "#### Observations:  \n",
        "Integer data types in columns 0,1,7, and 8. Object/string data type in columns 2,3,5, and 6. Float data type in column 4.\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]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Zth-RR1PJY_S",
        "outputId": "82fcbabf-abe4-44e1-d9c9-5e6a6f44de5a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "df.isna().sum().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jDHT3vD1JY_S"
      },
      "source": [
        "#### Observations:\n",
        "There are no values missing from 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",
        "outputId": "8ba06abc-9772-4cb7-9884-24923226c7ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           order_id    customer_id  cost_of_the_order  food_preparation_time  \\\n",
              "count  1.898000e+03    1898.000000        1898.000000            1898.000000   \n",
              "mean   1.477496e+06  171168.478398          16.498851              27.371970   \n",
              "std    5.480497e+02  113698.139743           7.483812               4.632481   \n",
              "min    1.476547e+06    1311.000000           4.470000              20.000000   \n",
              "25%    1.477021e+06   77787.750000          12.080000              23.000000   \n",
              "50%    1.477496e+06  128600.000000          14.140000              27.000000   \n",
              "75%    1.477970e+06  270525.000000          22.297500              31.000000   \n",
              "max    1.478444e+06  405334.000000          35.410000              35.000000   \n",
              "\n",
              "       delivery_time  \n",
              "count    1898.000000  \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  "
            ],
            "text/html": [
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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>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>1.898000e+03</td>\n",
              "      <td>1898.000000</td>\n",
              "      <td>1898.000000</td>\n",
              "      <td>1898.000000</td>\n",
              "      <td>1898.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.477496e+06</td>\n",
              "      <td>171168.478398</td>\n",
              "      <td>16.498851</td>\n",
              "      <td>27.371970</td>\n",
              "      <td>24.161749</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.480497e+02</td>\n",
              "      <td>113698.139743</td>\n",
              "      <td>7.483812</td>\n",
              "      <td>4.632481</td>\n",
              "      <td>4.972637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.476547e+06</td>\n",
              "      <td>1311.000000</td>\n",
              "      <td>4.470000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>15.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.477021e+06</td>\n",
              "      <td>77787.750000</td>\n",
              "      <td>12.080000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.477496e+06</td>\n",
              "      <td>128600.000000</td>\n",
              "      <td>14.140000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>25.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.477970e+06</td>\n",
              "      <td>270525.000000</td>\n",
              "      <td>22.297500</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>28.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.478444e+06</td>\n",
              "      <td>405334.000000</td>\n",
              "      <td>35.410000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>33.000000</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-a2960576-8a4f-4bdd-a100-d92ece46647b 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-a2960576-8a4f-4bdd-a100-d92ece46647b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        element.appendChild(docLink);\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "  }\n",
              "\n",
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              "\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\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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              "    90% {\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",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
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              "        document.querySelector('#df-c746702b-5be2-42ea-98f5-7b6f8c61ff35 button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9YbSOR5wJY_T"
      },
      "source": [
        "#### Observations:\n",
        "Once an order is placed the minimum time until the food is ready is 20 minutes, the average time is 27.37 minues, and the maxiumum time is 35 minutes.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cw3l58z_JY_T"
      },
      "source": [
        "### **Question 5:** How many orders are not rated? [1 mark]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EQ9Nz270qCHO",
        "outputId": "6dc1bbf7-3d82-462f-95fa-33ddec84f45b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Not given    736\n",
              "5            588\n",
              "4            386\n",
              "3            188\n",
              "Name: rating, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "df['rating'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5E0O_oviJY_T"
      },
      "source": [
        "#### Observations:\n",
        "736 orders were not rated.\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": "markdown",
      "source": [
        "#### Order ID"
      ],
      "metadata": {
        "id": "Kk5JcWwjWSSE"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "rU5ZqISjJY_U",
        "outputId": "c5498f1a-3fd5-4bc0-d6d5-e6e143617a74",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1898"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "df['order_id'].nunique()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Obeservations: <br>\n",
        "There are 1898 unique entries, each corresponding to an order made by a customer. Since this is equal to the total number of rows, there are no duplicate entries.\n"
      ],
      "metadata": {
        "id": "6z-yVnCSf8H6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Customer ID\n"
      ],
      "metadata": {
        "id": "sK4QdatgWbTe"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hnLnJLJ7qCHP",
        "outputId": "46739ca2-f0cf-4500-d0ff-259b5da17cad",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1200"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "df['customer_id'].nunique()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Obeservations: <br>\n",
        "Out of these 1898 unique entries, there are only 1200 unique customers. This indicates some customers have placed more than one order."
      ],
      "metadata": {
        "id": "TZXZKFAcgVOx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Restaurant Name"
      ],
      "metadata": {
        "id": "dFpxOitPWjKu"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jtg_FACJqCHP",
        "outputId": "6ea7005f-1f0e-4ffd-832c-bce75eb7210a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "178"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "df['restaurant_name'].nunique()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Obeservations: <br>\n",
        "The 1898 orders have been fufilled by 178 unique restaurants. This indicates many resturants have had more than one order placed."
      ],
      "metadata": {
        "id": "Om-8NSSIgjNS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Cuisine Type"
      ],
      "metadata": {
        "id": "bnK4hLhIWmBi"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "IX-6S1O-qCHP",
        "outputId": "b752ab3e-f58a-4b3c-96b7-f695513acde7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "df['cuisine_type'].nunique()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Obeservations: <br>\n",
        "Of these 178 unique restaurants, there are only 14 unique categories of cuisine that they offer."
      ],
      "metadata": {
        "id": "mx0waVQfg6VU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,7))\n",
        "sns.countplot(data=df, x='cuisine_type');\n",
        "plt.xticks(rotation=90);\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "fAxUUA8ty_S9",
        "outputId": "a2af0e10-58f5-4ac1-a5fd-ea7c33672c35"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Obeservations: <br>\n",
        "Looking at the count plot of the 14 unique cuisines, shows that Japanese, American, Italian, and Chinese are the most orderd cuisine by large margins."
      ],
      "metadata": {
        "id": "t2whmojyhLKD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Order Cost"
      ],
      "metadata": {
        "id": "5xv1M7CsW_Wv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,7))\n",
        "sns.histplot(data=df, x='cost_of_the_order', kde=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "id": "iPpb0yE0zTc6",
        "outputId": "81ac53c0-f082-438a-bf5d-4257821952cb"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Obeservations: <br>\n",
        "The histogram with density curve shows that most frequently orders are around\n",
        "12 dollars. Most of the orders are close to this amount, but orders costing more than 25 dollars are causing the distribution to be right skewed."
      ],
      "metadata": {
        "id": "7pzjvG8yhs1v"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "HmwtW7uJqCHQ",
        "outputId": "72753498-d4e9-4a01-d825-f3c949e1d0d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(15,7))\n",
        "sns.boxplot(data=df, x='cost_of_the_order')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Obeservations: <br>\n",
        "The box plot of the order costs also shows the data is right skewed with a median order cost of about 14 dollars. The minimum order cost is 5 dollars, the max is 35 dollars. The 1st quartile is about 12 dollars and the third quartile is about 22 dollars."
      ],
      "metadata": {
        "id": "DgJAyC2ojJBn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Day of the Week"
      ],
      "metadata": {
        "id": "gB2WZeDDXDoc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['day_of_the_week'].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bZQl7HhVzq_6",
        "outputId": "330e64cf-1163-4e72-f994-4fc8246c3d21"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Obeservations: <br>\n",
        "There are 2 unique days of the week recorded. The data taken is only split by weekend and weekdays, not individual days."
      ],
      "metadata": {
        "id": "JvASfgvCkUhn"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "igN9gQPNqCHQ",
        "outputId": "fb9f4ef1-01f9-404a-d4f1-ff721f3af877",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(15,7))\n",
        "sns.countplot(data=df, x='day_of_the_week')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Obeservations: <br>\n",
        "Looking at the countplot of the days of the week that orders were made shows that a significant portion of the orders are placed on the weekend, close to 1400, whereas orders placed on weekdays are only nearing 600 orders. This is a significant amount more orders on the weekend especially considering that the weekend is only 2 days, but the weekday accounts for 5 days."
      ],
      "metadata": {
        "id": "KOmFPQzRkgJ-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Rating"
      ],
      "metadata": {
        "id": "bvzbBfSFXNDg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['rating'].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pIwjkf0pz2Eh",
        "outputId": "10aba871-eb17-4d54-edc5-d13d13120f0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Obeservations: <br>\n",
        "\n",
        "There are only 4 unique rating counts given by customers."
      ],
      "metadata": {
        "id": "CYu1Ho-4k_Rh"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "rKKfuTTtqCHQ",
        "outputId": "59ff8a4f-4798-4a39-916d-78363cd24e9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(15,7))\n",
        "sns.countplot(data=df, x='rating')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Obeservations: <br>\n",
        "The count plot of the ratings given by customers shows that no ratings of 1 or 2 were given and most orders ratings are 'Not given'. Of the orders that are given, the most frequent rating is 5, followed by 4, then 3. This shows a trend that as customers are less satisfied they are less likely to give a rating."
      ],
      "metadata": {
        "id": "oGijT6AmlGUa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Preparation Time"
      ],
      "metadata": {
        "id": "2j2DnChlXQ4T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,7))\n",
        "sns.histplot(data=df, x='food_preparation_time', kde=True, bins=30)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "id": "KzfgKFxsz8HJ",
        "outputId": "2211ac15-9906-45d1-b550-e6fc70da283e"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 1 Axes>"
            ],
            "image/png": 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TYWXo2HEexo8DAAAhER2sSdK5556rc889t8XthmHozjvv1J133nkcq0Ik8QeCemnVTs17/1sVV9ZJkoZnJ+v6/zxRp/fryokv0EEYhhEeQF1xTbftChTow8Vzdfec/woHJV5/IDxDann95WjltfX3Nf6Dlhu2h9bX+UOXqFq9jC3t4j9peZGkop0tvx+Feic5jENnaHQ6QmNrOevH2Gr++YduMNV09khfnVs9rn5el71eKOOtdxUImPIHTfmDQfkCx2mwMEkxbqeinKb27tmlbmnpiouLOyj4coSDrxYDMieTXHQWbqdD3RKilJXolnf3RmXGmsrKPHxCZ5qm6gJB1fqC4bCttq4heAs2CeAah3OB+kuNQ2HcYatSxrR5+v3be6W335HbaSgl1hO6xYV6vDb0eEw8+D7Gfcilw1EuZ5t+ZgAAwD4RH6wBLTFNU0s3Fmnuvzdq297QDJ+9u8TqpokDNXFwOr+AAZ1clMupjKQYZSTFtPq5tb6AysPBm1/VdQd6ZFXX+VVVF2iy3NBDq8YXUElZhdZ/s1mpPfrIcLnlD4R+cfcHg00uaTQVGoMqEF46Fgy5ErqozBuUFGxxL7fTUFT9+FUN44BFN1pu6MkT5XKEw824qFAvvfioAz364qNciotyhgepj6vf5nQYWrNmjUaMOEeXzH9NWf2zjtH7RWdlGEb9uHVOJcUc3cQHpmnKFzAPDdwagri6A+vKK6u0b98+xaZ0U11A8gWs94aVQjMnJ0SHLktPqP9vpeGy1fBkLfX/rTXMphx+7HZqV6FXnowTVVZnKL66Tk5HKJxvuHcYDcF9+zgXCgZDoX/QbPi+NMPrGr4/A42XA82v31TgVcwJI7Wn2lBNUYVMUwcm7zBNmY3GFjxkm9l0sg+z0bbG2yVJ5oFv7fC3tyk1nh6kutKlbhf8UXM/KVHSulUy659jmgf2ajhcwx9YGiYocRiGHI6GCUuM0GXiLW4/8MeZxpOdOByGnPVtorCgQkljL9GmMocKdpTIISM8lmPoyvCG5QOTphg6dNKU0PpG+zRsk9HkuY4W9m1YrgtKhidGXr8prz8QrpNzdwDtFcEa2qWdJdW6/Y0NWrqpSJKUGufRdeP665ejsxm4GMAP1hAedU9s/bACa9as0YgbJ+gX819TVv8Tm2xr+EWxIWQLDRx/6C94DQPKNzw+Wo1/AXIYUvHO7fq/e2bpHy/9XScPGiSXI/TLi7N+HKqo+t5gLr430QkZhiGPy5DH5VDiEcK4XVs26MG7LlNubq5yBg9RSXWd9lfVaX91nUqq6lTeMHts/RhyFY2XG882W3+JqtcflLfSq+LDd5M7rIxfP6j3CyQV7Djsfs76ECbcM7bR44ZtTXrAHpRtHCnq8HldSp82T9e/s1dRy5eHQ7GAaSoQqL+v/z7z109YE6gPwhp6DLal7j+/XSuKJRVbu+S87TgUe+IYrdrjlfYU2VyLlPzjX2lDmaSyfXaXIsmj7Otf0S9eK5BeWxJe29BTu3Eo6DBU/++WQ05Ho+0Oo8m+oXUKP69x2Hxw8Ox0KPy4vHS/upxzvVbvc2qTvzAcNNb/L/TfRv3jZtfV1200eU7Dvs2va3ys0PND60orHIofOknvbKvSJl9efUh5ILg88G+8wqG52+movxnyOB1yuw5arh9Cwe101G835HY6ImJICqAjIVhDu1LnD+qZT77TI0u3qNYXlNtp6IrTT9DVZ/ZV4lGM0QQAdnI4DHkchjwtjRzfxgJFpnxF29Urya1+3eOPy2sCHV2Mx6kenhj1SG59b9hA0FRlk7AtdAl6dZ1ftfUTudQ0uow1vOw7MKFLrS+gkvIqbc/bpbjUNAUNhwJBs8VJPkIBl3TsesY6FJXeTzvK/FJZZZsf3e0MBSIuh6P+3jhw72y63ltbo03ffKOM3v0UHRvbfE8u49DeVY0vxW+xB5hxcJgSetQkmmi0vbQoX+///XH98Y+3qFev7FC4YhwaykgHerIFG/2Rxaz/w8uhPeoalpvZv2Fdo+AyGDRVULRX/3z1VZ182tmKSUwOH8s0Vd+T7sAxTFMK6sC2xq8d7nHX+Pk6aJ+GWhrt2/iYh2Oakj90oB/cbloj/uRx2lElqar8uL7uoVzqMuEaPZlbLuWuO+av5nE6FO121I+J2TDTemi2dW91hbqcc72+LHFq+5a9oaEa3E5Fu0L3DT3YG2a3djn4Ax06N4I1tBsrv9unWxav15ai0Enb6D6p+vMFg9Wve4LNlQEAAByZ02EoKdatpNgf9sfA0KXVEzVr/mvK6j9IUtPAI9AoYGkStjTTI/ZwPcbMFhYary/es0OL5v9J8x97TAMH9D/Q06g+7GroKeRyNO2J1LgnUXhbo+c0rGv15zJ7ln45/zVl9e/Zque2tV01u1X51Ts6u+/dGj4829Za1qxZo6eumq8RPxunrP7WZvttK6ZpaueWbzTvuov1yaef6eQhQw4KAhUOBBuHgw2X+za02WB9T8jG6xrvG+412WjfQ9eZysvbqQfnPawfT/61ErqkhUPBhkZuNgoEzfr/M9UQMiq84eB1Zv2DJuvqryE2w+ubXlpcXVGub9eu0FlnnaXEpOT65zW9NPngcNMfCI0v6QvfTNX5D1oOhGbZPlhdIDQLd3lt85O9xJ88Tt9VSqosPeLP1eUwwsNIxHic9UNEhEK6uChneMiIOE9oHFV6y6GjIVhDxKvy+nX32xu1cGWeJKlLnEd//GmOLhjWgy9lAAAANR0v63ie4Lv3m6rdvkZD06M0vG/X4/jKaI8aegUq4FOM22H7FSdrYvbr9i9e04Bpv1JWn1Rba9m1pUSfLZ6rmxpNxNRWzPog0dcoiKvzh8aTrKkfN7a6Uc/YzVu3674HH9bY83+tmOQuqvMHQ7OF188m3jBzuLc+sPMHTfm9oRnDVXX4WpwOQ3H1s6k3TPSSGBMac7Jhshd6wKG9IVhDRPtie4n+3ytfKa+kWpL0i1OzddPEAUqO9dhcGQAAAABEPsMw5HIacjmlGB15VuI1RpFu+eJVnTRtqrL6d2txP9MM9ZCr9Qfl9QVU6w+qus6vam9AVXWhSZ6qGi17/aEJR8pr/Sqv9Su/rJlaJSXGuJUc41ZyrFspsR75aww541IO9PIDIgzBGiJSrS+gv7y7Wc98sl2mKfVIjtF9/3WKTuvHX0IBAAAAwG6GYYTGXHM7paOYkdkfCKqqrn5G9fpJXcrrx5osr/GprMYnf9BUWf3jHSUNz3Qra/r/atrrhTpp9QoNSEvQiekJGpyZqJyMREW7jxwWAscSwRoizrpdZbr+5bXaWj+W2kUjs3TLuSfZ3lUcAAAAAGCNy+lQUoxDSTFuKenQ7aZpqrouoNJqn/ZX14Xvi0orVVEXVGWdU19sL9EX28OJm5wOQ/27x+uUrCSd3CNJQ3umKCcjgRnPcVwRrCFimKapBZ99r7vf3ihfwFS3hCjdc+HJGpeTZndpAAAAAIBjyDCM0CQHUS71SDkw8/KuLRv00HWX6OUlH8mR0kObCyu0Mb9CG3aXaV9VnTYVVGhTQYVeXr1LkhTrcWpoz2SN7JWi4fU3OmngWCJYQ0Qoq/bpxle/0jsbCiVJEwal6Z4LT1FKHGOpAQAAAEBnZvrrdEKKW8OHZx1YZ5rKL6vVut1lWr+7TF/vKtOavP2qqPXrs2379Nm2fZIkhyGdkpWs0/t11dh+XTSiV4qiXFw+irZDsAbbrd1ZqukvrtGu/TVyOw398ZwcTRvbmxk/AQAAAADNMgxDmckxykyO0YRB6ZKkYNDUlqJKrd5Rotzv92v1jv3KK6nW2p2lWruzVI99sFXRbodG9U7VmQO6a3xOmrK7xNr8TtDeEazBNqZp6tlPv9fctzfKHzSVnRqrx345TKdkJdtdGgAAAACgnXE4DA1IT9CA9ARNHd1LkrS7tEafbi3WZ1uL9em2fdpb4dXHW4r18ZZi3fnWN+rfPV7jctI0Lqe7hmenyOmggwdah2ANtqj1BTT7tXVa9OVuSdI5J6frnimncO07AAAAAKDN9EiO0UUje+qikT1lmqa2FlVq+bd79f7GQq36fr+2FFVqS1Glnli+Td0SonTO4HSdOyRTI7JT5CBkw1EgWMNxV1BWq6v+d7W+3lUmpyN06edlp3HpJwAAAADg2DEMQ/3TEtQ/LUH//eMTVFbj0/Jv92rpxkJ9sKlIeyu8en7FDj2/YocykqJ1zskZOn9opk7ukcTvq2gRwRqOqzV5+/Xb/83V3gqvkmPd+usvh2tsv652lwUAAAAA6GSSYtz62ZBM/WxIpur8QX26tVhvfr1H720oVH5Zrf72yXb97ZPtGpCWoJ+PzFJfZ8DukhGBCNZw3CzbXq2nXv1cdYGgBqQl6Olfj2SgSAAAAACA7Twuh84c2F1nDuyuWl9AH327V29+na93NxRoc2GF7vrXRjkNqevk2cqvMZRpmnLQiw0iWMNxYJpS8k+m6bFVZZKkCYPS9OBFQxUXRfMDAAAAAESWaLdTZw9K19mD0lVW49ObX+3RK6t36qtdZYobcJo+2yut/+x7DemZrEEZiYpyO+0uGTZy2F0AOrZA0NSqfU4ljfm5JOnacf31+NQRhGoAAAAAgIiXFOPWr37US69PP10PTeiq8lWL5XaYKq/16+MtxXrmk+1atqlI+yq9dpcKmxCs4Zjx+gJavHa3dlY7ZQb8mj4qSbP+80RmVgEAAAAAtDu9ktzav+wZnZPp07iB3dUl3iN/0NS63WX6v5V5Wrx2t3aX1thdJo4zug3hmKio9en1tXu0r6pOLsPU7n/eobN+8YzdZQEAAAAA8IO4HNLgHkkalJmo3aU1WruzVN/trdKOfdXasa9amcnRGtU7Vb1SY5lNtBMgWEObK6706vW1e1Tp9SvO49SPUmv17Pdf2l0WAAAAAABtxjAMZaXEKislVqXVdcrdsV8b8yu0p7RWr6/do24JUTq1d6r6dosjYOvAuBQUbaqgrFb/zN2lSq9fqbEeXTSyp5I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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Obeservations: <br>\n",
        "The histogram of the food preparation time shows that the food preparation time is pretty evenly distributed between 20 and 35 minutes."
      ],
      "metadata": {
        "id": "zu7S6LwUl29_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "4Q_-ROS5qCHQ",
        "outputId": "2ade1b04-67be-4619-9dcc-68fb07428198",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(15,7))\n",
        "sns.boxplot(data=df, x='food_preparation_time')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Obeservations: <br>\n",
        "Looking at the box plot of the food preparation time, shows the min is 20 minutes, the max is 35 minutes, the median is 27 minutes, the Q1 is 23 minutes, and the Q3 is 31 minutes."
      ],
      "metadata": {
        "id": "8NNhpy120Cul"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Delivery Time"
      ],
      "metadata": {
        "id": "QkUso7WXXUQu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,7))\n",
        "sns.histplot(data=df, x='delivery_time', kde=True, bins=15)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "id": "qJ5f3-yp0FJs",
        "outputId": "2ce6f544-151f-4aa5-ce9c-5e2dd5d5ef46"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Obeservations: <br>\n",
        "The histogram of the delivery time shows the distibution is left skewed with the majority of deliveries taking around 23 to 30 minutes."
      ],
      "metadata": {
        "id": "IUKZ9p8OnVDJ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "8MpvkcghqCHQ",
        "outputId": "afe049bb-032e-4112-8dd8-27825eb7e64d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(15,7))\n",
        "sns.boxplot(data=df, x='delivery_time')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Obeservations: <br>\n",
        "The box plot of the delivery time shows that the min is 15 minutes, the max is 33 minutes, the median is 25 minutes, Q1 is 20 minutes and Q3 is about 28 minutes."
      ],
      "metadata": {
        "id": "1qbo03xHnuSQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Total time"
      ],
      "metadata": {
        "id": "lsOshA-FsVTX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,7))\n",
        "sns.histplot(data=df, x='total_time', kde=True, bins=15)\n",
        "plt.show()\n",
        "\n",
        "plt.figure(figsize=(15,7))\n",
        "sns.boxplot(data=df, x='total_time')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "kJernmQ-sdno",
        "outputId": "53e7b365-0b72-4140-960b-d4718c5629cd"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Obeservations: <br>\n",
        "The min total order time is 35 minutes, the max is 70 minutes and the median is about 52 minutes. This is the prep time plus the delivery time."
      ],
      "metadata": {
        "id": "humXG90ntNKO"
      }
    },
    {
      "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",
        "outputId": "4e27120b-598b-485e-aeb1-35188e70e6af",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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",
              "Name: restaurant_name, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "df['restaurant_name'].value_counts().head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Obeservations:\n",
        "The top 5 restaurants in terms of numbers of orders received are Shake Shack, The Meatball Shop, Blue Ribbon Sushi, Blue Ribbon Fried Chicken, and Parm.\n"
      ],
      "metadata": {
        "id": "TRb6IdcGs3-X"
      }
    },
    {
      "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",
        "outputId": "ec603f72-8e53-4681-e367-f41d7154c900",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "day_of_the_week  cuisine_type  \n",
              "Weekday          American          169\n",
              "                 Japanese          135\n",
              "                 Italian            91\n",
              "                 Chinese            52\n",
              "                 Indian             24\n",
              "                 Mexican            24\n",
              "                 Middle Eastern     17\n",
              "                 Mediterranean      14\n",
              "                 Southern            6\n",
              "                 French              5\n",
              "                 Thai                4\n",
              "                 Vietnamese          3\n",
              "                 Korean              2\n",
              "                 Spanish             1\n",
              "Weekend          American          415\n",
              "                 Japanese          335\n",
              "                 Italian           207\n",
              "                 Chinese           163\n",
              "                 Mexican            53\n",
              "                 Indian             49\n",
              "                 Mediterranean      32\n",
              "                 Middle Eastern     32\n",
              "                 Thai               15\n",
              "                 French             13\n",
              "                 Korean             11\n",
              "                 Southern           11\n",
              "                 Spanish            11\n",
              "                 Vietnamese          4\n",
              "Name: cuisine_type, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "df.groupby(['day_of_the_week'])['cuisine_type'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bpQ270TUJY_U"
      },
      "source": [
        "#### Observations:\n",
        "The most popular cuisine on weekends 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",
        "outputId": "076aaf0a-896c-4461-e150-1ed945c93e4d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "29.24130663856691"
            ]
          },
          "execution_count": 250,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "((df[(df['cost_of_the_order'] > 20)].shape[0]) / df.shape[0]) * 100"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "77KNAC5zJY_U"
      },
      "source": [
        "#### Observations:\n",
        "29.24% of the orders cost more than 20 dollars."
      ]
    },
    {
      "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",
        "outputId": "1f5cf90b-dede-44c1-e02f-1e8a9cb05ff4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "24.161749209694417"
            ]
          },
          "execution_count": 130,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['delivery_time'].mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F35SRV-HJY_V"
      },
      "source": [
        "#### Observations:\n",
        "The mean order delivery time is 24.2 minutes."
      ]
    },
    {
      "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",
        "outputId": "82714c37-9ccc-41c0-82c6-6f8a1c4be51a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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": 75
        }
      ],
      "source": [
        "df['customer_id'].value_counts().head(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9tWVwfX6JY_V"
      },
      "source": [
        "#### Observations:\n",
        "The top 3 most frequent customers are customer id's 52832, 47440, and 83287."
      ]
    },
    {
      "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": "markdown",
      "source": [
        "#### Correlation among Numerical Variables"
      ],
      "metadata": {
        "id": "9F1xKrw4Za8N"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m1gIZH-kqCHZ",
        "outputId": "6de60099-a8ab-4823-cf50-05ad9a6d744a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       order_id  customer_id  cost_of_the_order  \\\n",
              "order_id               1.000000    -0.013960           0.021171   \n",
              "customer_id           -0.013960     1.000000          -0.001322   \n",
              "cost_of_the_order      0.021171    -0.001322           1.000000   \n",
              "food_preparation_time -0.007026    -0.030330           0.041527   \n",
              "delivery_time          0.009690    -0.009885          -0.029949   \n",
              "\n",
              "                       food_preparation_time  delivery_time  \n",
              "order_id                           -0.007026       0.009690  \n",
              "customer_id                        -0.030330      -0.009885  \n",
              "cost_of_the_order                   0.041527      -0.029949  \n",
              "food_preparation_time               1.000000       0.011094  \n",
              "delivery_time                       0.011094       1.000000  "
            ],
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "df.corr(numeric_only=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Obeservations: <br>\n",
        "Looking at the correlation table of the numerical variables does not show any significant correlation."
      ],
      "metadata": {
        "id": "zsyHn1lhQVRK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Cuisine Type"
      ],
      "metadata": {
        "id": "r9_4K9l5ZlWD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,7))\n",
        "sns.boxplot(data=df, x=\"cuisine_type\", y=\"cost_of_the_order\")\n",
        "plt.xticks(rotation = 45)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 688
        },
        "id": "g7abyyxYQwpm",
        "outputId": "3350c55f-5f4f-4c66-d81e-f7654de0b054"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Obeservations: <br>\n",
        "Looking at the four most popular cuisine types by order count, American, Japanese, Italian, and Chinese, they all have nearly identical box plots. This could be worth looking into to increase sales in other cuisine types. The minumum order for all cuisine types is around 5 dollars and the max is around 35 dollars.\n"
      ],
      "metadata": {
        "id": "fmv_x-_aS_3o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,7))\n",
        "sns.boxplot(data=df, x= 'cuisine_type', y='food_preparation_time')\n",
        "plt.xticks(rotation = 45)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 688
        },
        "id": "wIUfdL7VTCzH",
        "outputId": "d781cddc-2bda-48e2-aaf4-2130f1fe8f82"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Obeservations: <br>\n",
        "The minimim prep time is around 20 minutes, and the max is around 35 minutes. The four most popular cuisine types again look very similar. The median time for all orders stays around 25-28 minutes."
      ],
      "metadata": {
        "id": "DfR-XfIvTDcG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Day of the Week"
      ],
      "metadata": {
        "id": "F4gA06uaZ3KD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,7))\n",
        "sns.boxplot(data=df, x='day_of_the_week', y='delivery_time')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "id": "mN_xZtwQTQ6S",
        "outputId": "5ed13729-473e-474b-8fcb-5a1cb9736ad2"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Obeservations: <br>\n",
        "The deliveries on the weekdays generally take much longer than on the weekends, although the variance of delivery time is smaller. The minimum delivery on the weekdays is longer than the median on the weekends. On weekends the variance of for delivery time is larger, but the minumum time is 15 minutes which is much lower than the weekdays. Comparing the median delivery times between weekends and weekdays shows that weekday deliveries take around 7 minutes longer than weekends."
      ],
      "metadata": {
        "id": "bb9pTzdGTRKq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Rating"
      ],
      "metadata": {
        "id": "GOhqMqXmaAX_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15, 7))\n",
        "sns.pointplot(x = 'rating', y = 'delivery_time', data = df)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "id": "Th8mFlFtTdxJ",
        "outputId": "eee7a9c9-a7bd-4021-ebec-c40ab2f52798"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Obeservations: <br>\n",
        "One might expect that rating would increase with lower delivery times, but that does not appear to be the case from the point plot comparing rating vs delivery time. Even though ratings of 3 have the highest central tendancy, ratings of 5 are typically taking longer than those with ratings of 4."
      ],
      "metadata": {
        "id": "MHFrhlP5Td6n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15, 7))\n",
        "sns.pointplot(data=df, x='rating', y='food_preparation_time')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        },
        "id": "Zq7XA00rThQe",
        "outputId": "1e5da410-5bb8-438b-8534-049515a2ba97"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Obeservations: <br>\n",
        "Similarly, one might expect that a quicker prep time would yield higher ratings, but the point plot above does not indicate that that is the case. Although there is some fluctation between ratings and their prep times, the fluctuation is less than a minute between all the ratings."
      ],
      "metadata": {
        "id": "94y2O9YhTheo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15, 7))\n",
        "sns.pointplot(data=df, x='rating', y='total_time')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "id": "8AckHv9siT2T",
        "outputId": "10ef9f63-ac70-4d7c-ae3e-73956f487075"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Obeservations: <br>\n",
        "Looking further into the ratings compared to the total time from order placed to order recieved, the results appear to be somewhat inconclusive. Ratings of 3, which are the lowest rating recorded, are only less than a minute longer than ratings of 5 when comparing their central tendency. Even comparing the max total time of ratings of 3 to the min of ratings of 5, the difference is only 2 minutes. It is unlikely that this would change a users rating that significantly. Also it is worth noting that ratings of 4 ususlly take less time on total time than those with ratings of 5."
      ],
      "metadata": {
        "id": "1ho-QLCPmcmF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15, 7))\n",
        "sns.pointplot(data=df, x='rating', y='cost_of_the_order')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "id": "wVciOCrITmfD",
        "outputId": "99da4daa-b19c-453c-e334-05b95b016f2f"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 1 Axes>"
            ],
            "image/png": 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8qtlnWAAAypn8ArN2JacX6TXHMy/8+0FAERDaAAB2sSs5XS8u3KvdRzJs1mpVddUL/RprQHNftomiTHJzdtTIjg01pE19fbn5sGavjteps7lWx6w+eEKrD55QZOM6eiIyROF1Pe00LQAAZVd+gVl/HDqtRXtS9Ht0qk7/4+/bf+Pm7FhCk6GiIrQBAG6oM2dz9ebvBzV/a5IsFus1B5M0ooO/xvUKkQe3W0c5UMnFUfd1DtDd7epr3sbD+mBtvNLPWZ+9uXx/mpbvT1PvJl4a1ytEjbw97DQtAABlQ36BWZsTTuvX3cWLa5dqF8C1U2EsQhsA4IYwmy36dluy3lhyQGfO2W4Tbd2guiYPCFeYL5EB5U9lFyc91C1Q/7mpvuZtTNSHaw8p80K+1TG/Rx/X79HH1S/CR0/0DFawF1umAQC46GJcW7QnRb/vTbU5U7w46teorK7BtQ2YDvgboQ0AUOL2Hs3QCwv2audlrplRs4qLJvRtrNta1JWDA9tEUb65uzlrbI9gDe/gr0/XJ+iTdQnKyrEObot2p2jxnhT1b+arx3oGK7B2VTtNCwCAfRU1rrk4OahrSG35Va+kTzckXvVYRweTpgwM59+fMByhDQBQYjLO5em/Sw/qi82HL7tN9D83NdCTvULlWZltoqhYPNyc9URkiO7t4K+P1yXosw0JOptbULhusfx117Rfdh3ToBb19FjPIDWoWcWOEwMAcGMUOa45OqhraG31a+qjno3ryP3/Lj/Son51PfvDbp275O/Xi9xdnfTfu5qpawhns8F4hDYAgOHMZot+2HFEr/924LL/OGruV02vDAzn4u+o8KpVdtFTvUM1qlNDfbj2kOZtTNT5vL9/IDBbpB92HNGCnUd1R8t6GtsjSH41KttxYgAAjJdfYNaWhNP6tQhxrUtIbd0SYR3XLnVrM191Dq6lrtNWF97d3tXJQROiGum2VvW4HjBKDKENAGCofccyNXHhXm07fMZmrXplZz0X1Uh3tvLjNH3gEjWquOi5qEa6r3NDzVkdr//9cVg5+ebC9QKzRd9sS9aPfx7RXa399Ej3IPlWq2THiQEAuD4X49qiPSlaYlBc+6dqlV1U1dWpMLTVquqqezs2NGR+4EoIbQAAQ2ReyNP0pTH6fFOizP/YJmoySUPb1tfTN4eqehUX+wwIlAG1qrrqhVvCNKZLgN5fHa+vNicpt+Dv4JZXYNGXm5P03bYjGtrWTw93D5KXh5sdJwYA4NoVN671i/BWz8ZenIWGMoHQBgC4LhaLRQt2HtWriw7oZHaOzXpEPU9NGRCuZn7VbvxwQBlVx8NNk/o30QNdA/T+qnjN35qkvIK/C3ZugVnzNh3W/K3JGtaugR7qFqja7q52nBgAgMsrMFu0+dCpv665Fp2qk9nENZRvhDYAQLEdTM3Siwv3akvCaZs1z0rOeqZPqIa0qS9HtokCxeLjWUlTBobrga4BmrUqTt9tO6L8S04Zzck369MNCfpqy2GNaO+vMV0CVLMqwQ0AYF8FZos2J5zSot1FiWu11C/Ch7iGMo/QBgAosuycfM1YFqPPNiaq4J/7RCUNbu2nZ/qE8gM/YJB61Str6m0ReqhrkN5bGasf/zxq9b+9C3lmfbD2kP73x2Hd28Ff93cOYJs2AOCGIq4BfyG0ASXgjtkblZJxQZLk4+mm7x/qYOeJAGNYLBb9sjtFry7ap+OZtttEw3w8NGVguFo1qG6H6YDyr37Nypp2ZzM93D1I766I1cKdR62uiXgut0Dvr47X55sOa1SnhhrdqaE8K/GDCwCgZFyMa4v/75pr1xrX+jb1UWQYcQ3lU5FDm8ViUXJysurUqSM3Ny6+C1xOSsYFHU0/b+8xAEPFpWVp4sJobYw/ZbPm7uakp3uHali7BmwTBW6AhrWq6O3BzfVI90C9syJOv+4+JsslwS07J1/vrojVZxsSdH/nAI3s6H9Nd2cDAODfWMe145e9Ru+liGuoaIoV2oKCghQdHa3g4OCSmAkAUIqczcnXuytj9cm6BKtrQ110e8t6ei6qERdiB+wgqI673hvaQmO7B2nG8hj9tjfVaj3rQr6mL4vRp/8X3O7t4K8qrmxoAAAUTXHiWufgv7aFEtdQ0RT5X1oODg4KDg7WqVOnCG0AUI5ZLBb9tjdVU37dV7gV+lKNvN01eUC42jasYYfpAFwq1Ntds//TStHHMjRjeayW7TtutZ5+Lk/Tfj+oT9Yn6MGuAbrnJn9VcnG007QAgLKgwGzRloTTWrTnGHENKIJi/Urz9ddf19NPP63Zs2crPDzc6JkAAHZ26ES2Xvo5WutiT9qsVXV10vheIRrevoGcHB3sMB2AK2ni66mPhrfWniMZent5jFYeSLNaP302V68tPqAP1ybooW6BGtauvtycCW4AgL9cjGuL96Tot72p/xrXnB1N6hJcu3BbKNcFBYoZ2oYPH65z586pWbNmcnFxUaVKlazWT58+bchwAIAb63xugWauitWHaw8pr8B2m+jA5r76f30bq44H1+gESrOm9Tz16b1t9GfSGb29PFZrY05YrZ/MztGUX/fpgzXxeqR7kIa09ZOrE8ENACoi4hpgrGKFthkzZhg8BgDAniwWi5buO67Jv+y77I08gutU1eQB4WofWNMO0wEorhb1q+vzUW21NfG03l4WY3Mzk7SsHL30c7TmrInX2B5BurOVn1ycOFMVAMq74sS1zsG11Y+4BvyrYoW2ESNGGD0HAMBODp86q0k/R2vVwRM2a1VcHPVEZIju7egvZ7aJAmVWG/8a+ur+m7Qp/pTeXhajLYnWuw9SMi7o+Z/26v1V8XqsZ5Bua1mP/80DQDlTYLZoa+JpLdpNXANKUrFvOxUfH6/PPvtM8fHxeuedd1SnTh399ttvql+/vpo0aWLkjACAEnAhr0Dvr47XnDXxys0326zfEuGjF/qFyduTbaJAedE+sKZuCrhJG+JOafqyg9qRlG61fjT9vJ79YY/eXx2vx3oEa0BzX67FCABl2MW4dvHMtRNZ1xbX+jb1US/iGlAsxQpta9asUVRUlDp27Ki1a9fq1VdfVZ06dbRr1y598skn+v77742eEwBgoBX7j2vSL9FKPm27TTSgdhVN7h+uTsG17DAZgJJmMpnUKbiWOgbV1JqYE3p7WYx2HcmwOubwqXN68rtdmrUqTo9HBuuWCF85OpjsNDEAoCiIa4B9FSu0Pffcc3rllVc0fvx4ubu7Fz7fo0cPzZw507DhAADGSj59Ti//Eq3l+9Ns1io5O+qxnsEa3akh12gCKgCTyaRuoXXUNaS2Vh5I0/RlMYo+lml1zKGTZ/X4/J2auTJOT0SGKCrcWw4ENwAodYoT1zoF1VK/CF/1auwlz8rENcAoxQpte/bs0VdffWXzfJ06dXTy5MnrHgoAYKwLeQX6cO0hzVoVp5zLbBONCvfWC7eEqW61Spd5NYDyzGQyqWdjL/VoVEe/Rx/XjOUxOpCaZXVMbFq2Hvlqhxp5u+uJyBD1buIlk4ngBgD2VGC2aFviaS0irgGlSrFCW7Vq1ZSSkqKGDRtaPf/nn3+qbt26hgwGADDG6oNpmvRztBJPnbNZ869ZWZP6N1G30Dp2mAxAaWIymdQn3Fs3h3npt72pent5jOLSsq2OOZCapQe/2K4mvh4a3ytEPRrVIbgBwA1U3LjWt6mPbg7zJq4BN0CxQtuQIUP07LPP6rvvvpPJZJLZbNaGDRv01FNPafjw4UbPCAAohqPp5zXll31aEp1qs+bq5KCx3YM0pmuAXJ0c7TAdgNLKwcGkfhE+6hPurV93H9M7y2N16ORZq2Oij2Vq9LxtalbPU+N6hahrSG2CGwCUkItxbfGeFC2+hrjm5GBS52DiGmAvxQptr732mh555BH5+fmpoKBAYWFhKigo0N13360XXnjB6BkBAEWQm2/Wx+sP6b0VcTqfV2Cz3ivMSxNvCZNfjcp2mA5AWeHoYNKA5nXVr6mPFu48pndWxCrptPWZsbuOZOjez7aqZf1qGt8rVB2DahLcAMAAl8a13/amKu0a4lqn4FrqR1wD7K5Yoc3FxUUfffSRXnzxRe3du1fZ2dlq0aKFgoODjZ4PAFAE62NPauLPe3XoxFmbNb8alfRy/ybq0cjLDpMBKKucHB10e6t66t/cVz/tOKp3VsTqaLr1HYt3JKXrP59sVtuGNTS+V4huCqhpp2kBoOwymy3adviMFu0+RlwDyrBihbaL6tevr/r16xs1CwCgmFIyzuuVRfu1aHeKzZqLk4Me6hqoh7oFys2ZbaIAisfZ0UF3tfHTwBZ19d32ZM1cGaeUjAtWx2xJOK0hH/6hDoE1Nb5XiFr717DTtABQNhQ3rv21LdRL1Sq73KBJAVyraw5t48ePv+Y3nT59erGGAQAUTV6BWZ+uT9A7K2J1Ltd2m2j30Nqa1L+JGtSsYofpAJRHLk4OGtauge5oVU/fbP0ruP3zB8ON8ae0MX6TOgfX0vheIWpRv7qdpgWA0udiXFu8J0WL96QQ14By5ppD259//mn1eMeOHcrPz1doaKgkKSYmRo6OjmrVqpWxEwIALmtj/Em9tDBasf+4K6Ak1a1WSS/dGqZeYV5cLwlAiXB1ctTw9v66q7WfvtycpNmr43QyO9fqmHWxJ7Uu9qR6NKqjcZEhalrP007TAoB9XRrXftubouOZ/x7XOgbVUr8I4hpQ1lxzaFu1alXhn6dPny53d3fNmzdP1av/9RvKM2fOaOTIkercubPxUwIACqVlXtAri/br513HbNZcHB00pkuAHukepEoubBMFUPLcnB01ulNDDW3rpy/+OKw5aw7p9Fnr4LbyQJpWHkhTrzAvPREZrCa+BDcA5R9xDaiYinWNtrfeektLly4tjGySVL16db3yyiu6+eab9eSTTxo2IADgL/kFZs3bdFhvL4tRdk6+zXrn4Fp6uX8TBdSuaofpAFR0lV2cNKZLoIa1a6B5mxL14dpDSj+XZ3XMsn3HtWzfcUWFe+uJyBCFervbaVoAKBlms0Xbk85o0e4ixrWmPrq5CXENKA+KFdoyMzN14sQJm+dPnDihrKys6x4KAGBtS8JpTVy4VwdSbb/H+ni6aeItYeoT7s02UQB2V8XVSQ93C9I9NzXQZxsS9dG6Q8q6YP3Lgd/2pmpJdKpuifDV4z2DFVSHXxAAKLuIawAuVazQNmjQII0cOVJvvfWW2rZtK0navHmznn76ad12222GDggAFdmJrBxN/W2/ftxx1GbNycGk+zoH6LGeQarscl03kQYAw7m7OeuxnsEa0cFfn6xP0KfrE6zOxrVYpF92HdOi3cc0oHldPdYzWA1rceMWAGVDceJah6BauoW4BpR7xfrJbM6cOXrqqad09913Ky/vry0BTk5OGj16tKZNm2bogABQEeUXmPXl5iT9d+lBmzNBJKlDYE1NHtBEQXXYdgWgdPOs5KzxvUI0soO/Plp3SHM3JlrdJdlskX7686h+3nVMt7Woq0d7BKt+zcp2nBgALo+4BuBaFDm0FRQUaNu2bXr11Vc1bdo0xcfHS5ICAwNVpQq/hQSA67X98Bm9uGCv9qVk2qx5ebjqhX5huiXCh22iAMqU6lVc9EyfRhrdqaE+XHtI8zYl6kKeuXC9wGzRd9uP6Kc/j+rO1vX0SPcg1atOcANgX2azRTuSzujX3SlasjdVqZkXrnr8xbjWr6m3bg7zVvUqxDWgoilyaHN0dNTNN9+s/fv3q2HDhoqIiCiJuQCgwjmVnaM3lhzQt9uO2Kw5Opg0qqO/Ho8MUVVXtokCKLtqVnXVhL6NNbpzQ81ZfUhfbD6s3Py/g1u+2aKvtyTr++1HNKRNfT3cPVA+npXsODGAiuZiXFu0J0W/7fn3uOZYeM014hqAYm4dDQ8P16FDh9SwYcPr+uRr167VtGnTtH37dqWkpOinn37SwIEDrY7Zv3+/nn32Wa1Zs0b5+fkKCwvTDz/8oPr161/2PefOnauRI0daPefq6qoLF67+zREA7KXAbNFXW5I0bckBZV5mm2i7hjU0eUA4d+cDUK7UcXfTxFvD9EDXAL2/Kk5fb0lWbsHfwS2vwKL//XFY32xL1t1t6+vhboGq4+Fmx4kBlGfFiWsdAmvqlggf4hoAK8UKba+88oqeeuopTZkyRa1atbLZMurh4XFN73P27Fk1a9ZMo0aNuuxNFOLj49WpUyeNHj1aL7/8sjw8PBQdHS03t6v/I8vDw0MHDx4sfMz2KgCl1a7kdL24cK92H8mwWatV1VUv9GusAc19+T4GoNzy8nDTywPC9UDXQM1aFadvtyUrr8BSuJ6bb9bcjYn6ekuS7rmpgR7sFqhaVV3tODGA8oK4BqAkFCu09e3bV5LUv39/qx/+LBaLTCaTCgoKrvRSK1FRUYqKirri+vPPP6++ffvqzTffLHwuMDDwX9/XZDLJ29v7mmYAAHs4czZXb/5+UPO3JslisV5zMEkjOvhrXK8Qebg522dAALjBfKtV0quDmurBroGauTJO3+84ogLz398gc/LN+nh9gr7cnKQRHfw1pkuAavBDLoAiMpst+jP5r2uuFSWu9Wvqo5ubePN9B8C/KlZoW7VqldFz2DCbzVq0aJGeeeYZ9e7dW3/++acaNmyoCRMm2Gwv/afs7Gw1aNBAZrNZLVu21GuvvaYmTZqU+MwA8G/MZou+3ZasN5Yc0JlzeTbrrRtU1+QB4QrzvbYzgwGgvPGrUVlv3BGhh7sH6t0VcfrpzyO6pLfpfF6B5qyJ1/82JWpkx4a6r3ND7uQH4KqIawBupGKFtq5duxo9h420tDRlZ2fr9ddf1yuvvKI33nhDS5Ys0W233aZVq1ZdcYbQ0FB9+umnioiIUEZGhv773/+qQ4cOio6OVr169S77mpycHOXk/H1r5sxM2zv9AcD12nMkQy8u3Kudyek2azWruGhC38a6rUVdOTiwTRQAGtSsorfuaqZHugfqnRWx+nnXMaszgM/mFmjmqjjN25io0Z0balSnhpwFDKDQxbi2aHeqFu9JIa4BuGGKfeu69PR0ffLJJ9q/f78kqUmTJho1apQ8PT0NGcxs/utiuAMGDNC4ceMkSc2bN9fGjRs1Z86cK4a29u3bq3379oWPO3TooMaNG+uDDz7QlClTLvuaqVOn6uWXXzZkbgD4p4xzefrv0oP6YvPhy24T/c9NDfTkzaHyrMQPiADwTwG1q+qdIS00tnuQZqyI1aLdKVbrWTn5mrE8Vp+uT9CYLgG6t2ND7s4MVFCXxrXf9qYoJYO4BuDGK9a/QrZt26bevXurUqVKatu2rSRp+vTpevXVV7V06VK1bNnyugerVauWnJycFBYWZvV848aNtX79+mt+H2dnZ7Vo0UJxcXFXPGbChAkaP3584ePMzEz5+fkVfWgAuITZbNEPO47o9d8O6NTZXJv15n7V9MrAcIXXNeYXFABQngV7uWvW3S31aI9MzVgWqyXRqVbrmRfy9d+lMfpkfYLGdAnUiA4NVNmF4AaUd8WNa32b+qg3cQ1ACSjWvz7GjRun/v3766OPPpKT019vkZ+fr/vuu09PPPGE1q5de92Dubi4qE2bNlZ3D5WkmJgYNWjQ4Jrfp6CgQHv27Cm8gcPluLq6ytWVu1cBME70sQxNXBit7YfP2KxVr+ys56Ia6c5WfmwTBYAiauTtoTn3tNLeoxmasTxGy/enWa2fOZenN5Yc0MfrDumhboEa1q6BKrk42mlaACXhr7iWrkW7U4hrAEqdYp/RdmlkkyQnJyc988wzat269TW/T3Z2ttWZZgkJCdq5c6dq1Kih+vXr6+mnn9bgwYPVpUsXde/eXUuWLNEvv/yi1atXF75m+PDhqlu3rqZOnSpJmjx5sm666SYFBQUpPT1d06ZN0+HDh3XfffcV50sFgCLJvJCn6Utj9PmmRKuLd0uSySQNbVtfz/QO5cLdAHCdwut66uMRbbQrOV1vL4/R6oMnrNZPnc3VK4v264O1h/Rwt0ANbVtfbs4EN6CsuhjXFu9J0eI9xDUApVexQpuHh4eSkpLUqFEjq+eTk5Pl7u5+ze+zbds2de/evfDxxe2bI0aM0Ny5czVo0CDNmTNHU6dO1WOPPabQ0FD98MMP6tSpU+FrkpKS5ODgUPj4zJkzuv/++5Wamqrq1aurVatW2rhxo80WVAAwksVi0YKdR/XqogM6mZ1jsx5Rz1NTBoSrmV+1Gz8cAJRjzfyqae7Ittp++IxmLI/RutiTVusnsnL08i/79MGaQ3qkR5Dual1Prk4EN6AsKE5cax9QU/0iiGsA7KdYoW3w4MEaPXp04R09JWnDhg16+umnNXTo0Gt+n27dusnyzyuD/8OoUaM0atSoK65fenabJL399tt6++23r3kGALheB1Oz9OLCvdqScNpmzbOSs57pE6ohberLkW2iAFBiWjWorv+NbqfNh05p+rIYbf7H9+TUzAt6ccFezVkdr7E9gnRHq3pydnS4wrsBsJdL49pve1J07Brj2l9nrnmpZlUuCQTAvooV2v773//KZDJp+PDhys/Pl/TXTQceeughvf7664YOCAClVdaFPL2zPFafbUxUwT/3iUoa3NpPz0Y14repAHADtQuoqW8eaK+N8Sc1fWmMtv3jWplH089rwo979P7qOD3aI1i3tagrJ4IbYFcWyyXXXLuGuOZgkjoE1iKuASiVihXaXFxc9M4772jq1KmKj4+XJAUGBqpy5cpWxx05ckS+vr5WWzsBoKyzWCz6ZXeKXvl1n9KybLeJNvH10JSB4WpZv7odpgMASH/9EN7+wZpaH3dSby2N0c7kdKv15NPn9cz3u/X+qjg9Hhms/s3qcuYxcANdjGuLd/+1LZS4BqC8uK57nleuXFlNmza94npYWJh27typgICA6/k0AFBqxB7P0sSF0dp06JTNmrubk57uHaph7RrwwxoAlAImk0mdg2urU1AtrT54QtOXxWjP0QyrYxJPndO4b3Zp5so4PR4Zolua+nBHaKCEFCeutQ+sqX5NfYlrAMqM6wpt/+bfrr8GAGXF2Zx8vbsyVp+sS1D+ZbaJ3t6ynp6LaqTa7vwDEABKG5PJpO6N6qhbaG0t35+m6ctitD8l0+qY+BNn9djXf2rmylg9ERmiPk28CW6AAS6Na7/tTdXR9PNXPf5iXLt4t9BaxDUAZUyJhjYAKOssFot+25uqKb/uu+ydrhp5u2vKwHC18a9hh+kAAEVhMpnUK8xLPRvV0e/RqXp7eYxijmdbHRNzPFsPf7lDjX08NC4yWL3CvGQyEdyAorBYLNp58ZprxDUAFQyhDQCu4NCJbL30c7TWxZ60Wavq6qTxvUI0vH0DLqINAGWMg4NJUf/3A/2iPSmasTxG8SfOWh2zPyVTY/63XU3rempcr2B1D61DcAOugrgGAH8htAHAP5zPLdDMVbH6cO0h5RXYbhMd2NxX/69vY9XxcLPDdAAAozg4mHRrM1/1beqjX3Yd0zsrYpVw0jq47TmaoVFzt6m5XzWN7xWizsG1CG7A/7kY1xbvSdHiPdcW124KqKl+EcQ1AOVXiYY2/hECoCyxWCxauu+4Jv+y77L/UAzxqqrJA8J1U0BNO0wHACgpjg4mDWxRV7dE+OinP4/q3ZWxSj5t/ffAzuR0Df90i1o3qK7xvULUIaiWnaYF7Ku4ca1vUx/1CSeuASj/uBkCAEg6fOqsXvo5WqsPnrBZq+LiqCciQ3RvR385s00UAMotJ0cH3dnaTwNb1NUP24/ovZVxNhFh2+EzuvvjzbopoIbGRYaoHb98QQVAXAOAa3ddoS0uLk7x8fHq0qWLKlWqJIvFYnUW2759++Tr63vdQwJASbmQV6D3V8drzpp45eabbdZvifDRC/3C5O3JNlEAqCicHR00pG19DWpZV99uO6JZK+OUmml9Q5w/Dp3W4A//UKegWhrXK0StGlS307RAybBYLNp1JEOLdh8jrgFAERQrtJ06dUqDBw/WypUrZTKZFBsbq4CAAI0ePVrVq1fXW2+9JUny8/MzdFgAMNKK/cc16Zdom+1BkhRYu4omDwhXR7YGAUCF5erkqHtuaqA7W9XT/C1JmrU6XieycqyOWR93UuvjTqprSG2N6xWi5n7V7DMsYICLcW3xnhQt2p1yTXGtXcO/r7lW2524BgDFCm3jxo2Tk5OTkpKS1Lhx48LnBw8erPHjxxeGNgAojZJPn9PLv0Rr+f40m7VKzo56rGewRndqKBcntokCACQ3Z0fd27GhhrStry/+OKzZq+N16myu1TFrYk5oTcwJRTauoyciQxRe19NO0wJFU9y41jfCR32IawBgo1ihbenSpfr9999Vr149q+eDg4N1+PBhQwYDAKNdyCvQh2sPadaqOOVcZptoVLi3XrglTHWrVbLDdACA0s7N2VH3dQ7Q3e3q6/NNh/XBmnidOZdndczy/Wlavj9NvZt46YnIEDX28bDTtMCVEdcAoOQUK7SdPXtWlStXtnn+9OnTcnXlmy6A0mf1wTRN+jlaiafO2aw1rFVFk/o3UdeQ2naYDABQ1lR2cdKDXQP1n5saaN7GRH249pAyzlsHt9+jj+v36OPq19RHT0QGK9jL3U7TAn+xWCzafSRDi/akaPGeFB05Q1wDgJJQrNDWuXNnff7555oyZYokyWQyyWw2680331T37t0NHRAArsfR9POa8ss+LYlOtVlzc3bQ2O5Bur9LgFydHO0wHVByfC65gYcPN/MASkRVVyc90j1I97RvoE/XJ+iTdQnKysm3OmbRnhQt3pui/s189VjPYAXWrmqnaVERXYxri/ekaNE1xrW2DWuoX4QvcQ0AiqlYoe3NN99Uz549tW3bNuXm5uqZZ55RdHS0Tp8+rQ0bNhg9IwAUWW6+WR+tO6T3VsbqQp7tNtFeYV6aeEuY/GrYnp0LlAffP9TB3iMAFYaHm7OeiAzRyA4N9fH6Q/p0fYLO5hYUrlss0sKdx/TLrmMa2KKuHusRLP9aVew4McqzosY1k0lq17CG+jX1Ue9wb9Vx55czAHA9ihXawsPDFRMTo5kzZ8rd3V3Z2dm67bbb9Mgjj8jHx8foGQGgSNbHntTEn/fq0ImzNmv1a1TWpP5h6tHIyw6TAQDKM8/Kznry5lCN7NhQH649pHkbE3U+7+/gZrZIP+44qoU7j+mOlvU0tkcQv/CBIYhrAFB6FCu0SZKnp6eef/55I2cBgOuSknFer/y6X4v2pNisuTg56OFugXqwa6DcnNkmCgAoOTWquOi5qEa6r3NDfbAmXp9vOmx1E54Cs0XfbEvWDzuO6K42fhrbPUi+3IgHRWSxWLTnaIYW7SauAUBpUuzQlp6eri1btigtLU1ms/W2rOHDh1/3YABwrXLzzfpsQ4LeWRGrc5ds1bmoe2htTerfRA1qsk0HAHDj1Krqquf7hen+zgGavSZeX25OUu4lwS3fbNFXm5P0/bYjGtLWT490D5KXB/EDV1acuNbWv4ZuiSCuAcCNUqzQ9ssvv2jYsGHKzs6Wh4eHTCZT4ZrJZCK0AbhhNsaf1MSF0YpLy7ZZq1utkl66NUy9wrysvk8BAHAj1fFw00u3NtEDXQI1a1Wc5m9NUl6BpXA9t8Cszzcd1vytyfpPuwZ6sFsAQQSFCuPa/90tNPn0tcW1fhE+6kNcA4Abrlih7cknn9SoUaP02muvqXJlrisB4MZLy7ygVxbt18+7jtmsuTg66IGuAXq4W5AqubBNFABQOnh7umnKwHA92C1QM1fG6bttyco3XxLc8s36dEOCvtpyWMPb++uBLgGqWZW7PlZE1xXXmnirDmdGAoDdFCu0HT16VI899hiRDcANl19g1tyNiZqxPFbZOfk2652Da+nl/k0UULuqHaYDAODf1a1WSVNva6qHuwXqvZWx+mHHURVcEtwu5Jn14dpD+uKPw7q3g7/u7xyg6lVc7DgxbgSLxaK9RzP1655jxDUAKMOKFdp69+6tbdu2KSAgwOh5AOCKtiSc1sSFe3UgNctmzcfTTRNvCVOfcG+2iQIAygS/GpX15h3N9FC3IL23IlYLdh7VJb1N53IL9P7qv26mMKqjv0Z3DpBnJWf7DQzDFSeutfm/a64R1wCgdLrm0Pbzzz8X/rlfv356+umntW/fPjVt2lTOztZ/4ffv39+4CQFUeCeycjT1t/36ccdRmzVnR5Pu6xygR3sEqbJLse/vAgCA3TSsVUXTBzfXw92D9O6KWP2y+5gslwS37Jx8vbsyTp9tTNT9nQM0sqO/3N0IbmXVxbh2cVto0ulzVz3+Ylzr19RHUeHENQAo7a75p9KBAwfaPDd58mSb50wmkwoKbO/6BwBFlV9g1hd/HNZbS2OUdZltoh0Ca2rygHAF1WGbKACg7AuqU1XvDm2hsT2CNGN5jBbvSbVaz7qQr+nLYvTJ+gSN6RKgezv4q4orv2QqC4hrAFBxXPPfzGaz+d8PAgCDbD98Wi8uiNa+lEybNS8PV73QL0y3RPiwTRQAUO6EeLnr/WGttO9YpmYsj9HSfcet1jPO52na7wf1yfoEPdAlQMPb+3Pzn1KIuAYAFVOxfgX2+eefa/DgwXJ1tb4LUm5urubPn6/hw4cbMhyAiudUdo5e/+2Avtt+xGbNycGkUZ0a6rGewarKb/ABAOVcmK+HPhzeWnuOZGjG8hitOJBmtX76bK6m/nZAH61L0EPdAjWsXX25ORPc7MlisSj6WKZ+3V2EuNbg/25oEO4tL+IaAJR5xfpJdeTIkerTp4/q1Klj9XxWVpZGjhxJaANQZAVmi77akqRpSw4o84LtNtF2DWtoysBwhXi522E6AADsp2k9T31ybxv9mXRGby+P1dqYE1brJ7NzNOXXffpgTbwe6R6kIW395OpEcLtRLsa1RXtStGj3tce1vk29FdXUh7gGAOVMsUKbxWK57HatI0eOyNPT87qHAlCx7ExO14sL9mrP0Qybtdrurnq+b2MNaO7LNlEAQIXWon51fT6qrbYlntbby2O0Ie6U1XpaVo5e+jlac9bEa2yPIN3Zyk8uTg52mrZ8I64BAK6kSKGtRYsWMplMMplM6tmzp5yc/n55QUGBEhIS1KdPH8OHBFA+nTmbqzd/P6D5W5Ot7q4mSY4OJo1o768negXLgzurAQBQqLV/DX15303649ApTV8Woy0Jp63WUzIu6Pmf9ur9VfF6rGeQbmtZT86OBLfrdWlcW7wnRYdPEdcAALaKFNou3nl0586d6t27t6pW/ftOfy4uLvL399ftt99u6IAAyh+z2aJvtiXrjSUHlH4uz2a9dYPqmjwgXGG+HnaYDgCAsuGmgJr6ZsxN2hh/Sm8tPagdSelW60fTz+vZH/Zo1qp4PdYzWAOb+8qJ4FYkxYlrrRtU/+uGBsQ1AKiQihTaXnrpJUmSv7+/Bg8eLDe3q//F8fXXX6t///6qUqVK8ScEUK7sOZKhFxbu1a7kdJu1mlVcNKFvY93esi7bRAEAuAYmk0kdg2qpQ2BNrY09qelLD2rXEetLMSSdPqenvtul91fF6fHIYN0S4StHB/6evZKLcW3xnhQtKkJc69vUR1HhPvL2JK4BQEVWrGu0jRgx4pqOe+CBB9SuXTsFBAQU59MAKEcyzuXpv0sP6ovNh222iTqYpHtuaqDxN4fKsxLbRAEAKCqTyaSuIbXVJbiWVh5I0/RlMYo+lml1zKGTZ/X4/J16b2WcnogMVt9wHzkQ3CQVPa5JUht/4hoAwFaxQtu1svzzp2kAFY7ZbNH3O47o9d8O6PTZXJv1FvWracqAcIXX5UYqAABcL5PJpJ6NvdSjUR0t3Xdcby+L0YHULKtj4tKyNfarPxXqFadxvYLVu4l3hTyT/NK4tnhPihKJawAAA5RoaANQsUUfy9DEhdHafviMzVr1ys56LqqR7mzlx2/TAQAwmMlkUu8m3urV2Eu/7U3VjOUxik3Ltjrm4PEsPfjFDjXx9dC4yBD1bFyn3Ac3i8WifSmZWrT72uNa6wbV1S+CuAYAuDaENgCGy7yQp+lLY/T5pkSZ/3Fiq8kk3d22vp7uHapqlV3sMyAAABWEg4NJ/SJ81CfcW7/uPqZ3lsfq0MmzVsdEH8vUfZ9vU0Q9T43rFaJuIbXLVXArblzr29RHUU295eNZ6QZMCQAoLwhtAAxjsVj0059H9driAzqZnWOzHlHPU1MGhKuZX7UbPxwAABWYo4NJA5rXVb+mPvp51zG9syLW5jpku49kaORnW9WyfjWN7xWqjkE1y2xwuxjXFu9J0aLdxDUAwI1DaANgiAOpmZq4IFpbEk/brHlWctYzfUI1pE197nIGAIAdOTk66LaW9XRrM1/9tOOo3l0ZqyNnzlsdsyMpXf/5ZLPa+tfQuF4hah9Y007TFs2lcW3xnlQl/OPMvcshrgEAjHbNoe3dd9/VmDFj5ObmpqSkJPn5+f3rb7gaNGggZ2fuIAiUZ1kX8vTO8lh9tjFRBf/cJyppcGs/PRvVSDWqsE0UAIDSwtnRQXe18dPAFnX1/fYjmrkyVscyLlgdsyXxtIZ+9Ic6BNbUuF4hauNfw07TXllx4lqrBtXVj7gGACgh1xzaxo8fryFDhsjNzU0NGzZUSkqK6tSpc9XX7N2797oHBFA6WSwW/bzrmF5dtF9pWbbbRJv4emjKwHC1rF/dDtMBAIBr4eLkoLvb1dftrerqm63JmrUqTsczrf9e3xh/ShvjN6lzcC2N6xVi97/bLRaL9qdkadGeY0WKa32b+qgvcQ0AUMKuObT5+vrqhx9+UN++fWWxWHTkyBFduHDhssfWr1/fsAEBlD6xx7M0cWG0Nh06ZbPm7uakp3uHali7BmwTBQCgjHB1ctTw9v66q7WfvtqcpPdXx9tcb3Vd7Emtiz2p7qG1Na5XiCLqVbth812Ma4v3pGjRnpQixbWocG/5ViOuAQBujGsObS+88IIeffRRjR07ViaTSW3atLE5xmKxyGQyqaCgwNAhAZQOZ3Py9e7KWH2yLkH5l9kmenvLeprQt5FqVXW1w3QAAOB6uTk7alSnhhr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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Obeservations: <br>\n",
        "Rating and Order cost appear the be positively correlated. As order cost increases, so does the customers rating. Potentially, much cheaper orders have a quality that is not satisfiying to customers."
      ],
      "metadata": {
        "id": "QWX2IVZlTxNg"
      }
    },
    {
      "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": 29,
      "metadata": {
        "id": "QiaUkcYoqCHa",
        "outputId": "5acc3e4c-eef6-4197-8ca9-3abf27c26486",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             restaurant_name  rating\n",
              "0                Shake Shack     133\n",
              "1          The Meatball Shop      84\n",
              "2          Blue Ribbon Sushi      73\n",
              "3  Blue Ribbon Fried Chicken      64\n",
              "4           RedFarm Broadway      41"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9a5d4e1f-7594-44a9-8800-596f39ffd6d5\" 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>restaurant_name</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Shake Shack</td>\n",
              "      <td>133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>The Meatball Shop</td>\n",
              "      <td>84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Blue Ribbon Sushi</td>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Blue Ribbon Fried Chicken</td>\n",
              "      <td>64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RedFarm Broadway</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9a5d4e1f-7594-44a9-8800-596f39ffd6d5')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9a5d4e1f-7594-44a9-8800-596f39ffd6d5 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-9a5d4e1f-7594-44a9-8800-596f39ffd6d5');\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-f14ff31b-bb1d-45f4-bfd0-737e14821416\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f14ff31b-bb1d-45f4-bfd0-737e14821416')\"\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-f14ff31b-bb1d-45f4-bfd0-737e14821416 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": 29
        }
      ],
      "source": [
        "df_rated = df[df['rating'] != 'Not given'].copy()\n",
        "df_rated['rating'] = df_rated['rating'].astype('int')\n",
        "df_rating_count = df_rated.groupby(['restaurant_name'])['rating'].count().sort_values(ascending = False).reset_index()\n",
        "df_rating_count.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rest_names = df_rating_count[df_rating_count['rating'] > 50]['restaurant_name']\n",
        "df_mean_4 = df_rated[df_rated['restaurant_name'].isin(rest_names)].copy()\n",
        "df_mean_4.groupby(['restaurant_name'])['rating'].mean().sort_values(ascending = False).reset_index().dropna()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "5d1aOdA-unln",
        "outputId": "2553844b-f1f9-442f-961b-419c7de3a24c"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             restaurant_name    rating\n",
              "0          The Meatball Shop  4.511905\n",
              "1  Blue Ribbon Fried Chicken  4.328125\n",
              "2                Shake Shack  4.278195\n",
              "3          Blue Ribbon Sushi  4.219178"
            ],
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              "\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>The Meatball Shop</td>\n",
              "      <td>4.511905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Blue Ribbon Fried Chicken</td>\n",
              "      <td>4.328125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Shake Shack</td>\n",
              "      <td>4.278195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Blue Ribbon Sushi</td>\n",
              "      <td>4.219178</td>\n",
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              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b5110e27-6292-40ea-8c91-e1f33bdf8576 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-b5110e27-6292-40ea-8c91-e1f33bdf8576');\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",
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              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "\n",
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              "    background-color: var(--bg-color);\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    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",
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              "\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-003e34fc-ccc0-4d1f-a3d0-d2f107bf72fd 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": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "10uryUMhJY_V"
      },
      "source": [
        "#### Observations:\n",
        "Of all the restaurants, only four meet the conditions for the promotional offer. The resaurants with both more than 50 ratings and a mean rating greater than 4 are: \"The Meatball Shop\", \"Blue Ribbon Fried Chicken\", \"Shake Shack\", and \"Blue Ribbon Sushi\".\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": 9,
      "metadata": {
        "id": "rEjbnTNEJY_W",
        "outputId": "88485b61-84b5-4091-9ee2-12b8f0eede80",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5428.557499999999"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "def charge_restaurant(x):\n",
        "    if x > 20:\n",
        "        return x*0.20\n",
        "    elif x > 5:\n",
        "        return x*0.15\n",
        "    else:\n",
        "        return x*0\n",
        "\n",
        "df['revenue'] = df['cost_of_the_order'].transform(charge_restaurant)\n",
        "\n",
        "\n",
        "total_revenue = df['revenue'].sum()\n",
        "total_revenue"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XHCEJgH0JY_W"
      },
      "source": [
        "#### Observations:\n",
        "The net revenue generated by the company across all orders is $5,428.56."
      ]
    },
    {
      "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": 32,
      "metadata": {
        "id": "zxoTrxfLJY_W",
        "outputId": "fa964034-1668-4f00-809b-8a1ec22efe95",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10.537407797681771"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "df['total_time'] = df['food_preparation_time'] + df['delivery_time']\n",
        "df[(df['total_time'] > 60) == True].shape[0] / df.shape[0] * 100"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iqzBXn2yJY_W"
      },
      "source": [
        "#### Observations:\n",
        "10.53% of orders take more than 60 minutes to get delivered from the time the order is placed."
      ]
    },
    {
      "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,
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            "text/plain": [
              "day_of_the_week\n",
              "Weekday    28.340037\n",
              "Weekend    22.470022\n",
              "Name: delivery_time, dtype: float64"
            ]
          },
          "execution_count": 166,
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      "source": [
        "df.groupby('day_of_the_week')['delivery_time'].mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8H-ODxbVJY_W"
      },
      "source": [
        "#### Observations:\n",
        "The variance of mean delivery time between weekdays and weekends is about 6 minutes, with weekday deliveries taking longer on average."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "17roJywaJY_W"
      },
      "source": [
        "### Conclusion and Recommendations"
      ]
    },
    {
      "cell_type": "markdown",
      "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]"
      ],
      "metadata": {
        "id": "7IAxXAd6t3uF"
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        "#### Conclusions:\n",
        "*   American, Japanese, Italian, and Chinese cuisine are the significant majority of all orders places.\n",
        "*    Most order costs are around 12 dollars, but more than 25 percent cost more than 20 dollars.\n",
        "*    Total time of order does not appear to be a significant factor affecting customer ratings.\n",
        "*    Orders on the weekend are around 66 percent of all orders placed.\n",
        "*    Less satisfied customers leave ratings less frequently, with no customers leaving ratings of 1 or 2.\n",
        "*   Ratings of 5 seem to be frequently given without consideration of actual order satisfaction.\n",
        "*   Median total deliever time is about 52 minutes across all days of the week.\n",
        "*   The top 5 restaurants in terms of numbers of orders received are Shake Shack, The Meatball Shop, Blue Ribbon Sushi, Blue Ribbon Fried Chicken, and Parm.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Recommendations:\n",
        "\n",
        "*   Focusing on weekend sales will be the biggest increase in profits since the majority of orders are places on the weekend.\n",
        "*   Finding a way to increase weekday sales is also a big opportunity for growth, but customers might not be easily incentivised to do so.\n",
        "*   Increasing the amount of delivery drivers could decrease delivery times on weekdays to improve customer satisfaction and increase weekday orders.\n",
        "*   Attempting to get a rating from customers who did not rate their order is a big opportunity to improve processes for orders that are less than satisfactory because no ratings of 1 or 2 have been given. Offer a one time discount or other incentive to try to get this data from customers.\n",
        "*   Starting a promotion or sales program to increase sales that are near the 20 dollar mark would push customers over the 20 dollar threshold and in turn charge the restaurant 25% instead of 15% that would otherwise be charged.\n",
        "*   Increase the number of restaraunts offered in the American, Japanese, Italian, and Chinese cuisine types to keep customers from getting tired of ordering from these restaurants because these are a significant portion of all sales."
      ],
      "metadata": {
        "id": "xKa-ag7EuOGr"
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