IMDB-Movie-Analysis-With-PowerBI

This project explores what makes movies successful on IMDB by analyzing factors like genres, durations, languages, directors, and budgets. Through data analysis and visualization, it aims to provide valuable insights for stakeholders in the film industry, helping them make informed decisions for future projects.

Tools Used

Languages: Python

Libraries: Pandas, NumPy, Seaborn, Matplotlib

Visualization: PowerBI

Key Actions

1. Data Cleaning:

• Handled null values using mean and removed irrelevant entries.
• Converted data types for better analysis.
• Created a new Excel file for a cleaned dataset.

2. Visualization:

• Developed PowerBI dashboards for mean and median analysis, and standard deviation and variance analysis.
• Created a treemap showcasing top movies with directors, highlighting average and median IMDB scores, title year, and Facebook likes.

3. Analysis Tasks:

• Explored the distribution of movie genres and their impact on IMDB scores.

• Analyzed the relationship between movie durations and IMDB scores.

• Examined the distribution of movies based on language and its impact on IMDB scores.

• Identified top directors based on average IMDB score.