How hammicher/movielens-analysis works
This is a MovieLens 100k data-analysis project whose documented workflow moves from exploratory analysis through relational modeling and analytical SQL views to an interactive Power BI report. The README also describes time, genre, decade, user-profile, and recommendation analyses. The supplied tree contains environment dependencies but none of the project notebooks, SQL files, dataset, or report, so paths are left null and connections reflect only the documented workflow, not inspected implementation.
Components 16
Data Analysis
- Exploratory analysis
- Python analysis
Data Modeling
- Relational model
- PostgreSQL
- Analytical SQL views
- Time trends
- Genre and decade
- User profiles
- Polarizing films
- Underrated films
- Top-rated films
- Genre recommendations
BI Reporting
- Power BI report
- Interactive dashboard
Other components
- Analyst
- MovieLens 100k
Connections 17
- MovieLens 100k → Exploratory analysis: provides data
- Analyst → Exploratory analysis: explores data
- Exploratory analysis → Python analysis: analyzes with
- Analyst → Relational model: designs model
- Relational model → PostgreSQL: defines schema
- PostgreSQL → Analytical SQL views: hosts views
- Analytical SQL views → Time trends: exposes
- Analytical SQL views → Genre and decade: exposes
- Analytical SQL views → User profiles: exposes
- Analytical SQL views → Polarizing films: exposes
- Analytical SQL views → Underrated films: exposes
- Analytical SQL views → Top-rated films: exposes
- Analytical SQL views → Genre recommendations: exposes
- Analyst → Power BI report: builds report
- Power BI report → Analytical SQL views: uses data
- Power BI report → Interactive dashboard: presents
- Analyst → Interactive dashboard: explores