Project 2

This project employs ML techniques, to forecast weekly sales and actionable insights. By integrating historical sales data, store performance metrics, and macroeconomic factors such as fuel prices, temperature, and holidays, the system provides a comprehensive analysis. It includes data preprocessing pipeline and EDA to ensure high data quality and hidden trends. Key achievements include improving sales prediction accuracy by 15% and generating insights that optimize inventory management, pricing strategies, and marketing decisions across 50+ retail stores.

• Name: Exploratory Data Analysis of Retail Store Using LSTM
• Technology: Python, Panda, Numpy, Matplotlib, Seaborn
• Link: View Project