NEO Hotel operates a well-known in-house restaurant, which has historically been a key feature for guests and walk-in customers. Positioned as a premium dining option, the restaurant offers a diverse menu that includes local specialties, international cuisines, and a range of beverages. Despite its strategic location within the hotel, the restaurant has recently faced a significant downturn in sales. This decline has been attributed to several factors, including increased competition from nearby dining establishments and changing customer preferences.
Management has recognized the need for data-driven insights to turn around the restaurant’s performance. Currently, the restaurant lacks clear visibility into customer ordering patterns, which makes it difficult to design effective promotions or meal combos. Ultimately, the goal is to improve customer satisfaction, increase repeat visits, and boost overall sales revenue. In addition to targeted promotions, the insights gained from this analysis could inform staff training on upselling techniques and enhance the restaurant’s marketing campaigns. This holistic approach will help the restaurant regain its competitive edge and solidify its reputation as a top dining destination.
Recognizing the importance of data-driven decision-making, the management has formed an analytics task force to address the restaurant’s challenges. The management has invited you to join the task force as a Data Analyst, recalling that you have previously completed an Applied Data Analytics module as a Hospitality and Tourism Management undergraduate.
Your task is to develop a rule-based Association Rule Mining machine learning model. This model can uncover interesting relations between food items ordered by guests in the restaurant to create AI-driven recipes to foster culinary innovation by suggesting unique dishes and flavor combinations, enhancing the overall dining experience for guests.
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The structured data required for this analysis was extracted from the company’s Enterprise Data Warehouse (EDW) system. The IT department, leveraging SQL-based queries, extracted relevant order and transaction data from the data mart designated for restaurant operations. The following data dictionary was also provided as part of the Metadata given by the IT department.
| Column Name | Description | Data Type | Acceptable Values |
|---|---|---|---|
| TRANS_ID | Unique identifier for each transaction | int64 | Numeric IDs (e.g., 1001, 1002) |
| CUST_ID | Unique identifier for the customer | int64 | Numeric IDs corresponding to customers (e.g., 1, 2, 3) |
| FoodItems | Items ordered in the transaction | object | Text values listing only the following food items extracted for analysis: Cheeseburger, Coffee, French Fries, Fruit Platter, Garlic Bread, Ice Cream Sundae, Lemon Iced Tea, Spaghetti Bolognese, Steak Sandwich, Veggie Pizza |
| Column Name | Description | Data Type | Acceptable Values |
|---|---|---|---|
| CUST_ID | Unique identifier for the customer | int64 | Numeric IDs corresponding to customers (e.g., 1, 2, 3) |
| Gender | Gender of the customer | object | Categorical values: “Male”, “Female” |
| Age | Age of the customer | int64 | Integer values for age (e.g., 42, 54) |
| Home Type | Type of residence of the customer | object | Categorised into two main groups: “Private” and “HDB” |
| Member | Whether the customer is a member (Yes/No) | object | Categorical values: “Yes”, “No” |
Download the given Excel dataset and follow the CRISP-DM framework closely to complete each stage. You may follow the suggested steps in each stage as shown below.
4.1.1. Come up with a relevant Business Problem, Business Objective, and Data Mining Goals for the given background.
4.2.1. Import the dataset and perform the necessary table joins.
4.2.2. Perform Visual and Non-visual data exploration to check for any data quality issues.
4.3.1. Perform data cleansing to fix any identified data quality issue(s).
4.3.2. Perform data transformation to ensure data is structured in a format suitable for the Association Rule Learner model.
4.4.1. Build the Association Rule Learner model using the cleansed data.
4.4.2. Adjust the settings of the Association Rule Learner model such that there are enough interesting and actionable rules displayed.
4.5.1. Evaluate each rule generated in the final model.
4.5.2. Identify at least three interesting rules that are actionable.
4.6.1. Devise a marketing strategy using the identified interesting rules in the evaluation stage.
4.6.2. Your marketing strategy should be tailored towards solving the business problem identified in the ‘Business Understanding’ stage.
Below is a table of marking rubrics based on a 3-point grading scale (‘Poor’, ‘Good’, ‘Excellent’) for each of the six CRISP-DM stages, along with presentation skills and marketing strategy:
| Stage | Weightage (%) | Poor | Good | Excellent |
|---|---|---|---|---|
| Business Understanding | 10 | Limited understanding of the problem and objectives | Clear problem definition and objectives | Comprehensive understanding with well-defined goals |
| Data Understanding | 20 | Minimal exploration with incomplete insights | Sufficient exploration with most data quality issues identified | Thorough exploration with all data quality issues identified |
| Data Preparation | 20 | Inadequate handling of data quality issues | Adequate handling of most data quality issues | Excellent handling with clean, well-prepared data |
| Modeling | 10 | Incorrect or poorly implemented model without any interesting rules | Correct model with interesting rules displayed using some parameters | Highly accurate model with interesting rules displayed using all parameters |
| Evaluation | 10 | Poor evaluation with little interpretation | Adequate evaluation with some interpretation | Thorough evaluation with clear interpretation and actionable insights |
| Deployment | 20 | No or weak recommendations | Basic actionable recommendations | Well-thought-out and highly actionable recommendations |
| Presentation Skills | 10 | Poor communication and lack of clarity | Clear communication and explanation covering most aspects of the project | Excellent communication and explanation covering all aspects |
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