Netflix Content Strategy & Catalog Analytics Platform

Project Overview

Single-file Streamlit capstone platform analyzing Netflix's global catalog using the Kaggle dataset shivamb/netflix-shows. The application follows a five-level business intelligence hierarchy: executive KPIs, content velocity, portfolio drivers, risks and whitespace, and strategic actions.

Tech Stack

• Frontend: Streamlit
• Data preparation: pandas and NumPy
• Visualization: Plotly
• Data source: Kaggle via kagglehub
• Report export: python-docx

Quick Start Guide

Prerequisites

• Python 3.10+
• Kaggle account and API credentials configured for kagglehub

Installation

pip install -r requirements.txt

Running the App

streamlit run app.py

Kaggle Dataset

Dataset link: https://www.kaggle.com/datasets/shivamb/netflix-shows

The app downloads the dataset automatically on first run through kagglehub.

Single-File Design

All logic is consolidated in app.py:

• Data ingestion and caching
• Data validation and quality reporting
• KPI calculation engine
• 5-level BI analysis
• Plotly visualization layer
• Streamlit dashboard UI
• Word report generation

5-Level BI Framework

1. KPIs: What is happening?
2. Trends: Direction of movement?
3. Drivers: Why is it happening?
4. Risks & Opportunities: What could go wrong/right?
5. Actions: What should we do?

Dashboard Features

• Five-column executive KPI layout: total titles, movies, TV shows, global reach, and content diversity
• Year-over-year acquisition trajectory and monthly seasonality
• Top genres, movie runtime histogram, TV season-retention donut, and audience maturity segmentation
• Catalog aging, single-season churn, maturity skew, APAC/LATAM whitespace, and strategic action matrix
• Comprehensive multi-page Word report generated dynamically in the Report tab

Data Quality Assurance

• Missing value detection and reporting
• Duplicate checks
• Outlier and anomaly screening
• Type and date validation
• Transparent calculation logic derived from the actual dataset

Important Note on Rating Data

The Netflix dataset's rating field is a categorical maturity label such as TV-MA, TV-14, PG-13, or TV-Y. The application does not convert these labels into a fake quality score or IMDb-style rating. It groups them into audience segments for portfolio analysis.

Assumptions & Limitations

• The analytics use the Kaggle dataset as the source of truth.
• Rating-related metrics are based on audience maturity segments.
• Geographic and genre analysis are derived from country and genre fields in the catalog.

Project Structure

• app.py — complete single-file dashboard
• requirements.txt — environment dependencies
• README.md — documentation

Report Generation

The app includes a Download Report (.docx) action that builds a project report on demand using the current filtered dataset and aggregated metrics.

References

• Kaggle dataset: shivamb/netflix-shows
• Streamlit documentation: https://streamlit.io
• Plotly documentation: https://plotly.com/python