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This project analyzes retail footwear sales data from 2017 to 2024 to identify trends, optimize inventory, refine pricing strategies, and improve operational efficiency. By applying data cleaning, exploratory analysis, and forecasting, it provides actionable recommendations for seasonal demand planning, profitability, and product performance.
This is Digital Case Study of Clique Bait food Store, which offers comprehensive insights into user behaviour, campaign effectiveness, and product performance through SQL Queries, providing actionable insights for optimization and growth strategies.
Analyzed a dataset comprising over 1,000 products to reveal consumer preferences and identify e-commerce trends. Employed rigorous data preparation and analysis techniques to delve into the extensive product landscape on Amazon. Developed a recommendation system and performed sentiment analysis using machine learning.
Developed a Power BI Sales Performance and Customer Insights Dashboard to visualize key sales metrics, analyze customer behavior, and identify growth opportunities. The dashboard provided insights into sales trends, product performance, customer retention, and geographic distribution.