IMDb Movie Rating Analysis

This Python application performs various analyses on IMDb movie data and includes a machine learning model for rating prediction.

Features

• Average rating analysis by movie genre
• Movie rating analysis by year
• Correlation analysis between variables
• Simple linear regression analysis
• Revenue analysis and patterns
• Runtime vs rating relationship analysis
• Machine learning model for rating prediction
  • Random Forest Regressor
  • Feature importance analysis
  • Model performance evaluation

Setup

1. Install required Python packages:

pip install -r requirements.txt

2. Download the IMDb dataset from Kaggle:
   • Download from IMDB Movie Dataset
   • Copy the downloaded IMDB-Movie-Data.csv file to the project directory

Usage

To run the analysis:

python movie_analysis.py

The program will generate the following visualizations:

• genre_ratings.png: Average ratings by movie genre
• year_ratings.png: Average ratings by year
• correlation_matrix.png: Correlation matrix between variables
• regression_analysis.png: Relationship between Metascore and IMDb Rating
• rating_revenue.png: Relationship between movie ratings and revenue
• runtime_rating.png: Relationship between movie runtime and rating
• feature_importance.png: Most important features for rating prediction
• prediction_performance.png: Actual vs predicted ratings comparison

Analysis Results

When the program runs, it will create visualizations and print summary information to the console. These visualizations will show:

• Which movie genres receive higher ratings
• How movie ratings have changed over the years
• Relationships between different variables
• Correlation between Metascore and IMDb Rating
• How movie ratings affect revenue
• How movie runtime affects ratings
• Which features are most important for predicting movie ratings
• How well the machine learning model performs

Machine Learning Model

The application includes a Random Forest Regressor model that:

• Predicts movie ratings based on various features
• Uses genre, director, runtime, votes, revenue, and other features
• Provides feature importance analysis
• Shows model performance metrics (MSE and R-squared)
• Visualizes actual vs predicted ratings