Assessment 3: 3A and 3B Assessment type: Group project - Presentation and Report (2,000 words)...

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Assessment 3: 3A and 3B Assessment type: Group project - Presentation and Report (2,000 words) Purpose: The purpose of this assessment is that students will learn about different topics relevant to data analytics by carrying out research and also by listening to presentations made by their peers. This assessment contributes to learning outcomes b, c and d Value: Total 60% (Presentation 25%, Report 35%) Due Date: Weeks 11 – 12 Assessment topic: Students to select a data source and suggest a topic of analysis for that data source. Tutors to approve the topic before students proceed with further data preparation and analytics Students will work in groups (minimum 3 and maximum 4 students in each group). The first step will be to identify a data set from one of the publicly available data sets and present the summary of some of target models in Descriptive and Predictive Analytic layers to the tutor for approval. Once approved by their tutor, they will further define the research questions and prepare the data using consolidation and reduction if needed. Next, they will select one of the analytical tools (e.g. Excel, Tableau, Rapid Miner) and apply analytical methods for generating novel findings and draw insights from this data set. These outcomes need to be presented using visualisation models and also need to be explained in a detailed report. Students will present their findings as a group during tutorial sessions in week 11 for a duration of 10-15 mins per group. Tutors will provide feedback on their findings and students will then need to update their findings to reflect this feedback in their group report. Submission of a group report will be due in Week 10. This will be 2,000 words report excluding references and executive summary

Ajay · Accepted Answer

Table of contents 
Summary / Abstract---------------------------------------------------------------------------------------2
Introduction------------------------------------------------------------------------------------------------3 
A. Literature Review--------------------------------------------------------------------------------3
B. Data Description----------------------------------------------------------------------------------3 
C. Column Description------------------------------------------------------------------------------3 
D. Software Description-----------------------------------------------------------------------------4
E. Data Preparation / Preprocessing---------------------------------------------------------------4 
F. Business Questions--------------------------------------------------------------------------------4
Analysis----------------------------------------------------------------------------------------------------5 
A. Dashboard + Summary discussion--------------------------------------------------------------6 B. Analysis discussion of each business question (Descriptive + Predictive) ----------------6
Recommendations-----------------------------------------------------------------------------------------9
Discussion based on presentation feedback-----------------------------------------------------------11
Ethics/Security/Privacy---------------------------------------------------------------------------------13 
Recommendation------------------------------------------------------------------------------------------9
Conclusion------------------------------------------------------------------------------------------------15  
Bibliography----------------------------------------------------------------------------------------------16 
Summary / Abstract:
The Retail Sales and Customer Dataset is a thoughtfully generated synthetic dataset crafted to emulate a dynamic retail environment. This dataset serves as an excellent platform for enhancing data analysis skills via exploratory data analysis (EDA). Focusing on both retail sales and customer attributes, it encourages analysts to uncover intricate patterns, derive insights, and achieve a deeper comprehension of customer behaviour.
The dataset comprises vital attributes, including Transaction ID, Date, Customer ID, Gender, Age, Product Category, Quantity, Price per Unit, and Total Amount. These attributes facilitate multifaceted exploration, offering opportunities to unveil trends, demographic impacts, purchasing behaviours, and correlations within a retail context.
By immersing themselves in this dataset, analysts can delve into inquiries pertaining to the influence of age and gender on purchasing behaviour, sales trends throughout time, popular product categories, associations between age, expenditure, and product preferences, seasonal shopping tendencies, and more. Via data visualization, statistical analysis, and correlation assessment, analysts will unearth the subtleties that characterize retail operations and customer dynamics.
Introduction: 
In today's competitive retail landscape, the importance of data-driven decision-making cannot be overstated. Understanding customer behaviour, sales patterns, and demographic influences can profoundly affect a retailer's strategies. This project utilizes the Retail Sales and Customer Dataset to explore these aspects of retail operations. In this comprehensive analysis, we will dive into the dataset, conduct exploratory data analysis (EDA), and provide insights to address key business inquiries.
A. Literature Review: 
The field of retail analytics is well-established, combining data analysis, customer behaviour analysis, and business intelligence to optimize retail operations. Extensive research in this domain has underscored the significance of customer segmentation, comprehension of purchasing patterns, and the application of data-driven insights in shaping business strategies. Notable findings include the relevance of demographic factors such as age and gender in the retail context, the influence of pricing on consumer behaviour, and the role of seasonality in retail sales.
B. Data Description: 
The dataset employed in this analysis is the Retail Sales and Customer Dataset. It encompasses information regarding retail transactions, including Transaction ID, Date, Customer ID, Gender, Age, Product Category, Quantity, Price per Unit, and Total Amount. This dataset snapshot replicates a synthetic retail environment engineered to mimic real-world scenarios.
C. Column Description:
Transaction ID: A unique identifier for each transaction.
Date: The date of the transaction.
Customer ID: A unique identifier for each customer.
Gender: The gender of the customer (Male/Female).
Age: The age of the customer.
Product Category: The category of the purchased product.
Quantity: The number of units of the product purchased.
Price per Unit: The price for one unit of the product.
Total Amount: The overall monetary value of the transaction.
D. Software Description: 
The analysis is carried out using data analysis tools and programming languages, including Python, Jupyter Notebooks, and libraries such as Pandas, Matplotlib, and Seaborn for data manipulation and visualization. These tools offer a robust environment for performing exploratory data analysis and extracting insights from the dataset.
E. Data Preparation / Preprocessing: 
Data preparation and preprocessing are fundamental phases in the analysis process. These steps encompass addressing missing values, outliers, data type conversions, and ensuring data quality. Moreover, data may necessitate transformation or aggregation for specific analyses, such as customer segmentation or time series analysis.
F. Business Questions:
· How do customer age and gender influence their purchasing behaviour? Analyse the impact of age and gender on the products customers buy, the quantities they purchase, and their total spending.
· Are there noticeable sales patterns across different time periods? Investigate sales trends over time, encompassing seasonality, monthly variations, and the influence of holidays or special events.
· Which product categories have the highest appeal among customers? Identify the most favoured product categories and explore whether age or gender affects category preferences.
· What relationships exist between age, spending, and product preferences? Examine correlations between customer age, spending habits, and the types of products they choose.
· How do customers adapt their shopping habits during seasonal trends? Analyze how customer behaviour adjusts in response to seasonal trends and identify which products are more popular during specific seasons.
· Do distinct purchasing behaviours emerge based on the number of items bought per transaction? Investigate whether customers who buy more items in a single transaction exhibit different behaviours than those who purchase fewer items.
· What insights can be derived from the distribution of product prices within each category? Explore the pricing structure within product categories and its impact on customer choices.
By addressing these business questions,

Build an NLP model to perform sentiment analysis on a dataset of your choice (e.g., movie reviews). Submit your code AND a separate 1-page report on your approach and results.

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