{"repo":"AmirhosseinHonardoust/Data-Storytelling-Dashboard","free":true,"listed":false,"github":"https://github.com/AmirhosseinHonardoust/Data-Storytelling-Dashboard","clone":"git clone https://github.com/AmirhosseinHonardoust/Data-Storytelling-Dashboard.git","description":"A fully interactive data storytelling dashboard for e-commerce analytics. Built with Python, Streamlit, and Plotly, it transforms transactional data into actionable insights through KPIs, cohort retention, RFM segmentation, and global visualizations, perfect for analysts and data scientists.","language":"Python","stars":27,"topics":["business-intelligence","cohort-analysis","dashboard","data-analytics","data-science","data-storytelling","data-visualization","ecommerce-analytics","machine-learning","pandas"],"license":"MIT","category":"dashboards-admin","readme_excerpt":"Data Storytelling Dashboard, E-Commerce Analytics An interactive data visualization and analytics dashboard that transforms raw e-commerce data into actionable business insights . Built with Python , Streamlit , and Plotly , this project demonstrates advanced data storytelling , combining statistical analysis, cohort segmentation, and dynamic visualization. --- Project Overview This dashboard simulates a full-fledged analytics workflow for an e-commerce company. It provides end-to-end functionality from data ingestion and cleaning to KPI reporting , customer segmentation , retention analysis , and geographical sales intelligence . The project is powered by a synthetic dataset (4,000+ orders across 2 years, 1,600+ customers, 10+ countries, and 5 categories). --- Objectives 1. Tell a story with data: Convert large, unstructured datasets into interactive visual narratives. 2. Build an analyst-friendly interface: Enable filtering by country, channel, category, and time period. 3. Provide actionable insights: Identify best-performing channels, categories, and customer segments. 4. Demonstrate advanced analytics: Use RFM segmentation and cohort analysis to uncover retention patterns. --- Key Metrics & Definitions Metric Description -------- -------------- Revenue Total gross sales after discounts Profit Revenue − Cost Orders Number of unique purchase transactions Customers Number of unique buyers AOV (Average Order Value) Mean revenue per order Margin % Profit ÷ Revenue --- Analyti","default_branch":null,"files":null,"tree":[],"storefront":"/r/AmirhosseinHonardoust","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/AmirhosseinHonardoust/Data-Storytelling-Dashboard/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}