Retail Sales Forecasting and Monitoring project offers real-time analysis and forecasts for retail sales.

A simple Market Basket Analysis that uses the apriori algorithm to find affinities between retail products

A machine learning solution to forecast sales for Rossmann Pharmaceuticals' stores across various cities six weeks in advance. Factors like promotions, competition, holidays, seasonality, and locality are considered for accurate predictions.

A repository focusing on implementing Market Basket Analysis using the Apriori Algorithm in Python, providing insights into customer purchasing behaviour.

This project includes two Power BI dashboards for analyzing cost reduction and inventory management in the apparel industry. It helps optimize costs, improve inventory turnover, and support supplier negotiations.

Linear programming model to optimize product mix decisions in a retail setting, implemented in R with cost and capacity constraints.

End to end sales analytics pipeline for a multi channel e commerce business built with Excel and Power Query. Covers data ingestion from multiple folders, cross source merging, data quality handling, and a KPI dashboard across 3 sales channels.

MavenProfitPulse: Data-driven analysis of Maven Toys & Games to boost sales, profitability, and inventory using Pandas. Uncovers trends in performance, demand, and efficiency with actionable insights.

End-to-End Retail Customer Churn Prediction using Gradient Boosting and Streamlit. This repository showcases a comprehensive data science workflow, from feature engineering with RFM to building a Gradient Boosting model and deploying an interactive dashboard for actionable customer retention insights.

• Analyzed Retail Stored Data To Identify Behavioral Patterns. Generated Reports Using SQL Queries.• Analyzed KPIs Like Total Revenue, User Counts, Login Counts etc. For Year 2021 and 2022. • Created Dynamic Dashboard With Interactive Graphs Using Excel. • Techstack : Excel | SQL | Power Point

End-to-end retail analytics project transforming messy sales data using SQL, Python, and Power BI to clean, analyze, and visulaize insights for data-driven decision making.

This project predicts sales for Big Mart 🛒📈 using machine learning algorithms. By analyzing various factors such as product attributes, store location, and customer demographics, it aims to provide accurate sales forecasts to enhance inventory management and strategic planning.

Power BI dashboards analyzing retail data for revenue trends, top customers, and regional demand. Completed as part of the TCS Forage Data Analytics virtual experience.

Forecasting of retail sales data for a brick-and-mortar store. The focus is on exploring time series characteristics, building ARIMA and SARIMAX models, and selecting the optimal model based on AIC and RMSE metrics. The project provides insights into trends, seasonality, and prediction accuracy for business decision-making.

End-to-end sales analytics project using SQL, Power BI, and Python. Extracted customer insights, product trends, and revenue performance through interactive dashboards and KPI-driven reporting.

Generating point forecasts for future daily sales based on historical sales data.

End-to-end Rossmann weekly sales forecasting using XGBoost + SHAP explainability

Predict Big Mart sales using XGBoost Regressor. Learn data preprocessing, EDA, and model evaluation in Python.

An advanced SQL project analyzing over 1 million rows of Apple retail sales data to solve real-world business problems, optimize query performance, and extract actionable insights. The analysis includes sales trends, warranty claims, product performance, and year-over-year growth

Consolidate multi-platform ecommerce sales data into a unified dashboard to enable clear, data-driven commercial insights and decision-making.