BHB3701 Assignment 1 Brief Trimester 2 AY 2024-25 Pdf

1. Company Background

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.

2. Your Role

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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3. Data Extraction

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.

Data Dictionary

Fact_Trans: Contains the food items order transactions by customers

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

Dim_Cust: Contains the demographic profile of each unique customer

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”

4. Your Tasks

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. Business Understanding

4.1.1. Come up with a relevant Business Problem, Business Objective, and Data Mining Goals for the given background.

4.2. Data Understanding

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.

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4.3. Data Preparation

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. Modeling

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. Evaluation

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. Deployment

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.

5. Deliverables

PowerPoint Slides: Submit a deck of not more than 10 slides (include Cover slide) which should cover step 4.1 and 4.6 indicated in the CRISP-DM in section 4. You are to include the Course Code, Student ID, Your Name, and Submission Date on the Cover slide.
Self-Recorded Video (Turn on your camera for me to verify your identity): Do a self-recorded video for your presentation using the PowerPoint Slides. Your recording should not exceed 8 minutes. During the presentation, you should also demo the KNIME workflow describing the utilization of each node, especially for step 4.2, 4.3, 4.4, and 4.5 indicated in the CRISP-DM in section 4.
KNIME Workflow: Submit your KNIME workflow by exporting it as ‘.knwf’ format.

6. Marking Rubrics

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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