Movie Dataset Analysis 🎬

This project analyzes a dataset of 10,000 movies from The Movie Database (TMDb), focusing on understanding the impact of various features like budget, genre, and runtime on movie popularity, revenue, and ratings.

Objectives 📌

Budget and Popularity: Determine if higher budgets lead to higher popularity. Top Revenue Features: Identify common features in the top 10 revenue-generating movies. Genre Trends: Analyze the most popular genres over the years. Length, Ratings, and Revenue: Explore how movie length correlates with ratings and revenue.

Summary of Findings 📝

Budgets and Popularity: While high budgets don’t guarantee popularity, they tend to result in higher average popularity. Revenue Insights: Adventure and Western genres have become increasingly popular, with longer movies often achieving higher revenues. Trends Over Time: Revenue and popularity patterns show that genre preferences evolve, with significant increases in average revenue since the 1960s.

Dataset 📂 The dataset includes information such as:

Features: Budget, revenue, popularity, genres, runtime, release date, and adjusted financial figures for inflation.

Key Libraries 📚

Pandas: For data manipulation and cleaning. Matplotlib & Seaborn: For visualizing trends in the dataset.

Conclusion 🏁

This analysis provides insights into how movie characteristics like budget, genre, and runtime influence popularity and revenue, revealing trends that could be useful for industry predictions.