Data-Storytelling-Dashboard and Beyond-Charts-Interactive-Storytelling
About Data-Storytelling-Dashboard
AmirhosseinHonardoust/Data-Storytelling-Dashboard
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.
The dashboard ingests synthetic e-commerce data (4,000+ orders, 1,600+ customers across 10+ countries) and applies pandas-based KPI computation, RFM segmentation via scikit-learn, and cohort retention analysis to surface multi-dimensional insights. It features dynamic filtering by country, channel, category, and time period, with interactive Plotly visualizations including geographical treemaps and retention heatmaps. The Streamlit frontend supports custom CSV ingestion, enabling analysts to swap in production datasets while preserving the full analytical workflow.
About Beyond-Charts-Interactive-Storytelling
AmirhosseinHonardoust/Beyond-Charts-Interactive-Storytelling
A comprehensive guide and codebase for building interactive storytelling dashboards with Python, Streamlit, and Plotly. Learn how to transform static analytics into dynamic, user-driven data experiences that engage and inspire, featuring RFM segmentation, cohort analysis, and real-world insights.
Based on the README, here's a technical summary: The project implements a **narrative pipeline architecture** combining Streamlit for reactive web UI, Plotly for fully-interactive visualizations (with native hover/zoom/filter), and Pandas for data transformation—enabling end-to-end control over logic unlike traditional BI tools. It demonstrates advanced analytics patterns including RFM segmentation, cohort retention analysis, and time-series decomposition on a synthetic e-commerce dataset, with sidebar filters that redefine visualization context in real-time as users explore. The dashboard prioritizes guided exploration through structured story chapters (overview → trends → breakdowns → behavior) rather than static metric displays.
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