Intuitive Dashboard to Explore Large GitHub Organizations
Sample e-commerce dataset for business analysis, organized across six interconnected tables (Customers, Category, Products, Orders, Order_Items, Payments), designed to simulate realistic transactional workflows and support hands-on practice in Excel, MySQL, and Power BI for querying, data modeling, analysis, and generating actionable insights.
📊 End-to-end data analytics project analyzing a bank’s loan portfolio to identify profitable segments, high-risk borrowers, and strategic insights using Python, Jupyter Notebook, and data visualization.
End-to-end E-Commerce Sales Analysis using Python, Pandas, NumPy, Matplotlib, and Seaborn featuring data cleaning, exploratory data analysis (EDA), and business insights.
The_Fit_District is a comprehensive gym membership dataset comprising seven interconnected tables—Branches, Members, Subscriptions, Trainers, Plans, Attendance, and Payments—designed to model real-world fitness center operations, track member activity, manage trainer assignments, analyze plan utilization, and monitor attendance patterns.
Interactive Power BI dashboard analyzing credit card transactions to uncover spending patterns, customer insights, and key financial KPIs for data-driven decision-making.
A complete Power BI Student Result Analysis Dashboard with toppers, KPIs, subject insights, mark ranges, and data modeling using Excel, Power Query & DAX.
SQL Server and Power BI analytics project built on the AdventureWorks2022 database, featuring advanced T-SQL data modeling, profitability analysis, RFM customer segmentation, Pareto analysis, demographic & firmographic enrichment, sales channel analytics, and interactive business intelligence dashboards.
This repository demonstrates an end-to-end CTR time-series forecasting pipeline using SARIMA model. It analyzes daily advertising performance to uncover trend, weekly seasonality, and engagement patterns, and delivers 30-day forecasts to support budgeting, pacing, and marketing decision-making.
SQL-based portfolio risk analysis on 22k+ insurance records identifying loss ratios, revenue concentration, and farm-level profitability.
Developed an interactive Sales Performance Dashboard using Excel and Power BI to analyze key metrics such as sales, profit, and customer segments. Implemented time-based, regional, and category-level analysis with dynamic visualizations to identify trends and support data-driven decision-making.
Analyzed 10,000+ café transactions to uncover revenue drivers, customer behavior, and growth opportunities — with actionable recommendations to increase AOV, retention, and seasonal performance.
🐦 Bird Biodiversity Intelligence Dashboard | A professional ecological analytics platform built with Python, Streamlit, Plotly, and SQL to analyze bird species diversity, habitat distribution, environmental impact, and spatial biodiversity patterns across forest and grassland ecosystems.
Excel + SQL Dashboard for Customer Revenue Analysis with interactive KPIs, trends, and business insights
This dataset contains detailed coffee sales transactions across multiple store locations, including product details, quantities, prices, revenues, and timestamps. It enables comprehensive analysis of sales patterns, customer preferences, peak hours, and regional performance to support data-driven business decisions.
Excel dashboard analyzing road accident patterns, casualty severity, road conditions, and environmental factors using pivot tables and slicers.
End-to-end data analytics project analyzing 120+ marketing campaigns across Google, Facebook, Instagram, LinkedIn, Email & YouTube. Includes data cleaning, EDA, ROI analysis, conversion funnel analysis, and an interactive web dashboard (Chart.js) deployed on GitHub Pages.
End-to-end customer churn analysis using Python, SQL, and Power BI with actionable business insights.
🏦 Analyze bank loan data to uncover profitable segments and high-risk borrowers, guiding data-driven lending decisions and reducing portfolio losses.
End-to-end retail sales data analysis using Python, Pandas to uncover profit drivers, loss-making areas, and customer trends.
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