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analytics-project

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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.

  • Updated May 1, 2026

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.

  • Updated Apr 22, 2026

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.

  • Updated May 27, 2026

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.

  • Updated Apr 7, 2026

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.

  • Updated Dec 20, 2025
  • Jupyter Notebook

🐦 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.

  • Updated May 9, 2026
  • Python

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.

  • Updated Feb 28, 2026

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