How niravtrivedi23/mutual-fund-analysis-dashboard-python-powerbi works
This repository documents a mutual-fund analysis workflow: a large scheme dataset is cleaned, normalized, scored and ranked to identify 30 funds, then supported by Excel and presented in an interactive Power BI dashboard. The README also describes dashboard filters, KPI and comparison visuals, and insight cards. The listed files contain no sampled implementation or dashboard/data artifacts, so paths are null; relationships are limited to those documented in the README.
Components 14
Fund analysis
- Data cleaning
- Data description
- Normalization
- Fund scoring
- Top 30 ranking
Data support
- Excel support
Dashboard insights
- Power BI dashboard
- Dynamic filters
- Fund KPIs
- Cost and returns
- AMC and managers
- Insight cards
Other components
- Scheme dataset
- Dashboard user
Connections 13
- Scheme dataset → Data cleaning: is cleaned
README.md - Data cleaning → Data description: is summarized
README.md - Data description → Normalization: is normalized
README.md - Normalization → Fund scoring: supports scoring
README.md - Fund scoring → Top 30 ranking: selects top 30
README.md - Top 30 ranking → Excel support: is supported in
README.md - Excel support → Power BI dashboard: supports dashboard
README.md - Dashboard user → Power BI dashboard: views
README.md - Power BI dashboard → Dynamic filters: provides
README.md - Power BI dashboard → Fund KPIs: presents
README.md - Power BI dashboard → Cost and returns: compares
README.md - Power BI dashboard → AMC and managers: summarizes
README.md - Power BI dashboard → Insight cards: generates
README.md