Conducted exploratory data analysis (EDA) using Python and Pandas to analyze performance metrics across retail regions, identifying key drivers of revenue growth and providing strategic sales recommendations through SQL insights.

To Develop the Retail Insights Sales API Task By Using Flask

Exploratory Data Analysis Projects

This project is designed to demonstrate SQL skills and techniques typically used by data analysts to explore, clean, and analyze retail sales data. The project involves setting up a retail sales database, performing exploratory data analysis (EDA), and answering specific business questions through SQL queries.

This project involves analyzing retail sales data using SQL to uncover insights into sales patterns, customer behavior, and product performance. It serves as an exercise to develop foundational SQL skills in data exploration, cleaning, and analysis.

This project focuses on predicting retail sales using historical sales data and time-series regression techniques. It leverages Python, Scikit-learn, and XGBoost to build predictive models capable of forecasting sales trends. The goal is to provide actionable insights to retailers for inventory and sales strategy planning.

retial_sales anlaysis using sql, this project includes data cleaning, analysis, and insights on retail sales performances.

Descriptive and Predictive Analysis with Interactive Dashboard

Engineered PowerBI P&L financial dashboard that identified cost inefficiencies, leading to a 10% cost reduction and a 7% increase in profitability for a national retail chain.

Descriptive and Predictive Analysis with Interactive Dashboard