IMDB-Analysis

I dove into a 75-year span of movie data 📊🍿. Using a Kaggle dataset of 16 IMDb CSVs (genre-based), I unified and cleaned the data to explore:

🎥 Trends in ratings, votes, and box office gross 🧠 Correlations across genres, certificates, and runtime 🌟 Top actors, directors, and blockbuster years

• Key tools: Pandas, Seaborn, Data Cleaning, Python
• Key lessons: Data wrangling is everything, .loc is your friend, and good plots need good questions!

📈 Fun findings:

• Biography films run the longest
• Adventure genre dominates box office 💰
• Christopher Nolan rules the votes — but not the ratings 🤔
• Check out the code, charts, and insights inside!