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.

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.

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

• 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

Uncover insights, trends, and patterns within the retail data. Harness the power of data analytics to optimize inventory management, understand customer preferences, and drive strategic decision-making

An interactive dashboard for visualizing and analyzing retail sales and profits using various data visualization techniques.

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.

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

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.

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.

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

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.

Analysis and feature engineering of the Online Retail Transactions dataset to uncover customer behaviour, product trends, and optimise pricing. Includes interactive dashboards for actionable insights.

Power BI Projects