sales analysis project using PySpark provides valuable insights into customer behavior, sales trends, and product performance. These insights can inform strategic decision-making to enhance customer satisfaction, optimize marketing efforts, and improve overall business performance

SAMS: Stock analysis and management system

This project demonstrates a complete workflow for analyzing sales data with missing values. It includes data cleaning, feature engineering, aggregation, and visualizations using Python libraries such as Pandas, NumPy, and Matplotlib.

This R script analyzes Michaels store traffic, compares it with competitors, and assesses market share. Explore proximity, sales trends, and demographic insights for strategic decision-making.

FastTrack Sales Forecaster leverages ARIMA models for advanced time series analysis in fast food sales. It efficiently predicts future trends from historical data, aiding in strategic planning and market analysis. This tool is essential for data-driven decision-making in the fast food industry.

Utilized SQL in Google BigQuery to analyze sales performance, extracting key business insights to enhance decision-making and optimize operations.

Python pipeline for time series sales forecasting using data preprocessing, feature engineering, and ML/DL models.

This is detailed that shows a business’s sales aaperformance, as well as customer data and generated aarevenue.

SQL analysis of Walmart sales data to uncover customer behavior, product trends, and revenue insights. Built using MySQL.

TateoMerch is a web application built by Gino Tateo. The application started as a merchandising data analytics platform and evolved into ShipTeo a warehouse management system.

"⚡📊 Interactive Tableau dashboard analyzing India’s energy sales trends, 📍 high-consumption regions, and 📈 demand forecasts for smarter energy distribution."