LCBB5000 - Business Intelligence: Superstore Data Analysis & Insights

This report provides a comprehensive analysis of Superstore data, focusing on data handling and business intelligence techniques. It begins by evaluating the use of Excel for preprocessing data to determine the reasons behind declining profits and sales. The analysis involves cleaning and sorting the data, applying filters, and utilizing functions like 'autosum' to calculate yearly sales and profit totals. The data is then summarized in tables and visualized through charts and graphs to identify trends. The report also explores data mining methods used in business, including association, classification, and clustering analysis, and compares the advantages and disadvantages of using SPSS over Excel for data analysis. K-means clustering is applied to analyze customer data based on gender, age, and rice consumption, providing insights into customer segmentation. The findings highlight the importance of managing expenses and considering promotional strategies like discounts to improve profitability. Desklib provides access to similar reports and solved assignments for students seeking to enhance their understanding of data analysis and business intelligence.