Optimizing Hardware Sales Analysis and Product Recommendation

A hardware company faces challenges due to fragmented and static sales reporting, limiting timely insights and strategic decisions. This project develops an interactive Sales Insights Dashboard using Microsoft Power BI to unify sales data and dynamically visualize key KPIs such as revenue, profit, units sold, and growth trends. By leveraging Power Query, an optimized data model, and DAX measures, the dashboard enables multi-dimensional sales analysis and delivers real-time, actionable insights to support data-driven decision-making and improve overall sales performance. With the rapid growth of e-commerce platforms, customers often face information overload while searching for relevant products. Personalized recommendation systems play a vital role in improving user experience and business outcomes. This project focuses on developing a Product Recommendation System for an E-commerce Platform to enhance user engagement and increase conversion rates. The system uses user interaction data, product metadata, and user profiles to generate personalized recommendations. Data preprocessing techniques are applied to clean and structure the data, followed by the implementation of suitable recommendation algorithms such as collaborative and content-based filtering. System performance is evaluated using standard ranking metrics and simulated business metrics. The proposed architecture demonstrates an efficient and scalable approach to delivering personalized product recommendations in an e-commerce environment.