📌 Project Overview
This project analyzes streaming platform content data to uncover trends, content gaps, and actionable insights that support data-driven business decisions.
Using SQL, Python, and Tableau, the analysis focuses on content distribution, release trends, genre demand, and ratings breakdown to inform content strategy and acquisition planning.
🎯 Business Objectives
Identify high-demand genres and underrepresented content categories
Analyze content release trends over time
Evaluate content distribution by country, director, and rating
Provide insights to support strategic content acquisition decisions
🛠️ Tools & Technologies
Python (Pandas, NumPy)
SQL (SQLite)
Jupyter Notebook
Tableau Public
Git / GitHub
📊 Key Analysis Performed
Cleaned and transformed raw streaming content data using Python
Created and queried a SQLite database using SQL for structured analysis
Performed trend analysis on release years and content growth
Analyzed genre demand, director output, country distribution, and rating breakdowns
Exported analytics-ready CSV datasets for Tableau visualization
Documented logic, assumptions, and methodology within the Jupyter notebook
Note: The SQLite database is generated dynamically by running the notebook and is intentionally excluded from version control.
📈 Tableau Dashboard
An interactive Tableau dashboard was created to visualize key insights and trends from the analysis.
🔗 View the Dashboard:
https://public.tableau.com/views/NetflixDashboard_17673906111670/Dashboard1?:language=en-US&:sid=&:redirect=auth&:display_count=n&:origin=viz_share_link
Dashboard Highlights
Content distribution by genre and country
Release trends over time (Movies vs TV Shows)
Top directors by number of titles
Rating and content type breakdown
💡 Key Insights
Identified high-demand genres with growth potential
Highlighted content gaps in underrepresented categories
Revealed long-term release trends to support strategic planning
Enabled executive-ready, interactive reporting for stakeholders
