IMDB Movies Analysis with Python

Discover patterns in global cinema through data. Movies reflect culture, creativity, and audience preferences—and IMDB provides one of the richest sources of film data available. In this project, you’ll work with an IMDB dataset containing details such as movie titles, release years, genres, ratings, and votes. Using Pandas for data wrangling and Seaborn for visualization, you’ll analyze how cinema has evolved and what makes films successful. You’ll begin by cleaning and preparing the dataset—handling missing values, standardizing genres, and ensuring numerical columns like ratings and votes are properly formatted. With Pandas, you’ll group and aggregate data to calculate average ratings by decade, identify the most common genres, and compare popularity across time periods. Next, you’ll use Seaborn visualizations to highlight insights. Histograms will show the distribution of ratings, bar plots will compare genres by frequency or average score, and line charts will illustrate long-term trends in movie production and reception. You’ll explore questions such as: Which decades produced the highest-rated films? How do genres compare in terms of popularity and ratings? What is the relationship between number of votes and movie ratings? By the end of this project, you will be able to: Clean and organize a large media dataset with Pandas. Summarize trends in ratings, genres, and release years. Build Seaborn visualizations to make comparisons clear. Interpret patterns that explain shifts in the film industry. This project combines technical skills with a cultural lens, showing how data analysis can uncover stories behind movies. By working with IMDB data, you’ll strengthen your analytical toolkit while gaining a deeper appreciation of global cinema trends.