IMDb Top 250 Analysis

Project Information

  • Category: Data Analysis (EDA)
  • Course: Data Analysis with Python (Jovian)
  • Tech Stack: Python, Pandas, Matplotlib
  • Dataset: IMDb Top 250 Movies
  • Notebook: View on Kaggle

IMDb Top 250 Highest Rated Movies

This Exploratory Data Analysis (EDA) uncovers the factors contributing to a movie's success, analyzing genres, directors, release years, and runtimes.

Jovian Certificate

Interactive Notebook Preview

Situation

I set out to explore the top 250 highest-rated movies on IMDb to understand what makes a film successful. Does genre matter? Are certain directors more likely to succeed? How has movie length changed over time?

Task & Action

Using Python libraries (Pandas, NumPy, Matplotlib), I cleaned the dataset, examined value distributions, and created visualizations to illustrate key relationships.

Key Findings

📅 Golden Years

1995 produced the most top-rated films, followed by 2004 and 1957.

🎬 Top Directors

Christopher Nolan, Akira Kurosawa, Stanley Kubrick, and Martin Scorsese lead the list.

🔞 Age Ratings

R and PG-13 are the most common ratings, targeting mature audiences.

⏳ Runtime

Longer movies (approx. 180 mins) are popular, showing audiences value immersive experiences.

Conclusion

The analysis reveals that success is not random. Versatile directors who experiment across genres tend to succeed. While average ratings fluctuate by decade, the quality of top-tier cinema remains consistent. This analysis serves as a guide for both filmmakers aiming for acclaim and audiences seeking their next favorite film.