📈 Key Outcomes

🚀 Identified Netflix’s shift toward TV shows as a dominant content strategy.
🌍 Discovered that high IMDb votes cluster around 6.5 - 8.0 ratings, highlighting common audience preferences.
📊 Added business value by showcasing how runtime, content type, and year impact IMDb scores.
🔍 Implemented parameter-driven visualizations for an interactive user experience.

🚀 Solution

✅ Ratings Over Time – Tracked IMDb scores from 1950 to 2022, revealing recent declines in movie ratings.
✅ Content Production Trend – TV show production surged post-2015, surpassing movies.
✅ Runtime vs. IMDb Score – Shorter TV shows tend to have higher average IMDb ratings, while movies with longer runtimes don’t always score higher.
✅ IMDb Votes as a Popularity Indicator – Analyzed how audience engagement (IMDb votes) correlates with ratings.
✅ Interactive Dashboard – Enabled dynamic exploration using filters for Release Year & IMDb Votes and a toggle for overall vs. content-type trends.

🔍 Tools & Methods Used

Tableau (Data Visualization, Dashboard Design) | Python (Pandas, NumPy) for data cleaning & transformation | Statistical Analysis & Trend Forecasting