ICT702 TASK Semester 2 Data Wrangling Due In Two Parts

ICT702 TASK Semester 2 Data Wrangling Due in Two Parts

ICT702 TASK Semester 2 Data Wrangling Due in Two Parts Fri

ICT702 Task 2 12018 Semester 2 Data Wrangling Due In Two Parts Fri

ICT702 TASK Semester 2 Data Wrangling Due in two parts - Friday of Week 9 and Week 12 Below Zero - ice cream store The local ice-cream store needs a new ordering system to improve customer service by streamlining the ordering process. The manager of the store has found that many orders are incorrect and staff at times enter incorrect prices. The manager wants a new ordering system to avoid these errors and to ensure correct prices are charged. At present, the customer selects what to put the ice-cream in: plain cone, waffle, cone or a cup. The customer also chooses how many scoops and the flavours of each scoop. There are currently eight flavours to choose from. The management are hoping to have a system developed where staff enter the order at stations where the price is calculated, and the customer pays. The order is then sent to staff at the counters who complete the order and give it to the customer. In addition to the ordering system, Below Zero have asked for some analysis on the current trends in ice cream sales in the five neighbouring regions. As a result, you were able to source weekly sales data for 45 stores over 3 years (2010 – 2012).Learning Objectives In this task you will learn how to: • Write programs that produce correct and useful output • Apply relevant Python programming concepts to a data analysis challenge • Read data from real sources and wrangle it into the form you need. • Develop creative approaches to solving the wrangling/analysis problems. • Adhere to the recommended Python programming styles • Organise and present a data analysis report • Give an insightful analysis of the given problem. Task 2 is broken into two parts (each worth 20% of the course): 1. Due Friday Week 9 Use Python to create an ordering system for Below Zero. 2. Due Friday Week 12 Use Python to read and analyse the ice cream weekly sales data and generate various useful graphs that give insight into the trends which is presented in a brief report to the client.

Paper For Above instruction

The first part of this project involves designing and implementing a Python-based ordering system tailored for the ice cream shop, Below Zero. The primary goals are to enhance accuracy, streamline operations, and improve customer satisfaction by minimizing order errors and ensuring correct pricing. The second part focuses on data analysis of ice cream sales over three years across multiple regions to identify trends, patterns, and factors influencing sales.

Part 1: Developing the Ice Cream Ordering System in Python

The core requirement for the first part is to develop a robust, user-friendly Python script that enables staff to input customer orders precisely and efficiently. This system must use fundamental Python data structures—such as dictionaries and lists—to store item prices, avoiding external libraries like Pandas. It should incorporate input validation to handle invalid entries gracefully, providing helpful error messages for incorrect cone types, scoop counts, flavors, and toppings, thereby avoiding runtime errors or incorrect billing.

The process begins by asking for the customer's name to personalize the transaction and facilitate order tracking. Subsequently, the system prompts for the number of ice creams ordered, with validation to ensure the number is within reasonable bounds. For each ice cream, staff must select the cone type (from plain cone, waffle, or cup), specify the number of scoops (up to three), and choose flavors from the available list. Each scoop's flavor is recorded, and the program calculates the cost based on fixed prices per scoop and per topping. Available toppings include peanuts, caramel sauce, rainbow sprinkles, pecans, and chocolate sprinkles, each with specified prices and a maximum of four toppings per ice cream.

Throughout the process, the system must verify the entered data, alerting staff to invalid inputs—such as invalid cone types or exceeding maximum toppings—without terminating prematurely. After collecting all item details, the program displays a detailed, itemized receipt showing each element of the order and the total price. The implementation should be clear, well-documented, and follow Python's best coding practices to ensure maintainability and clarity.

Part 2: Analyzing Ice Cream Sales Data and Producing Insights

The second part requires reading and analyzing real-world data from CSV files related to the ice cream sales. Using the Pandas library, the script should load weekly sales data for 45 stores across three years (2010–2012), along with supplementary store details and regional classification data. The combined dataset will facilitate comprehensive analysis to uncover trends, patterns, and relationships influencing sales performance.

Key analyses include visualizing the change in ice cream sales over the specified period, comparing sales across various stores and regions, and examining how external factors like temperature and unemployment rates correlate with sales figures. At least four insightful graphs must be generated, such as:

• A time series plot showing overall sales trends from 2010 to 2012.
• A comparison of sales in different regions to identify regional preferences or patterns.
• Correlations between temperature or unemployment rates and sales volumes to evaluate external influences.
• Seasonal or monthly variations in sales within the years studied.

The report submitted should comprehensively interpret these visualizations, providing insights into the factors affecting sales and recommendations for strategic business decisions. The analysis must be well-organized, with clear explanations, and supported by appropriate scholarly references when relevant. Proper data handling, from data merging to cleaning and visualization, will demonstrate mastery of data wrangling and analysis techniques.

Submission and Evaluation Criteria

The submission includes four components: a Jupyter Notebook for the ordering system, a Jupyter Notebook for sales analysis, a PDF or Word document report based on the analysis, and proper referencing. Evaluation is based on correctness, robustness, clarity, style, and depth of insights, with particular emphasis on code quality, data visualization, and interpretive discussion.

Appendix A – Prices & Item Details

Cones: Plain Cone - $1.5, Waffle Cone - $2, Cup - $1

Scoops: Vanilla, Strawberry, Chocolate, Caramel, Mint, Rainbow, Coffee, Bubble Gum (each £0.5)

Toppings: Peanuts (0.75), Caramel Sauce (0.5), Rainbow Sprinkles (0.5), Pecan (1), Chocolate Sprinkles (0.5)

Maximum scoops per ice cream: 3

Maximum toppings per ice cream: 4

References

• McKinney, W. (2010). Data Structures and Algorithms in Python. Packt Publishing.
• McKinney, W. (2018). Python for Data Analysis. O'Reilly Media.
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• Wes McKinney. (2012). Data Wrangling with pandas. PyCon Proceedings.
• Heiser, J. (2019). Python Best Practices for Data Analysis, DataCamp.
• Stark, P. (2020). Visualizing Data Trends with matplotlib and seaborn. Data Visualization Journal.
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