Predicting Movie Ratings using Machine Learning

In this mini project, we aim to predict movie ratings using machine learning techniques. The project involves collecting a dataset containing features such as movie genre, director, cast, budget, and audience demographics, along with historical ratings from platforms like IMDb or Rotten Tomatoes. After preprocessing the data to handle missing values and encode categorical variables, we will explore various machine learning algorithms, such as linear regression, decision trees, and random forests, to identify the most effective model for prediction. We’ll split the dataset into training and testing subsets to evaluate the model’s performance based on metrics like Mean Absolute Error (MAE) and R-squared values. Additionally, we will implement feature selection techniques to enhance the model’s accuracy and interpretability. Finally, the project will conclude with visualizations that illustrate the relationships between features and ratings, providing insights into the factors influencing audience perceptions of movies. This project not only showcases practical applications of machine learning but also enhances understanding of data analysis and model evaluation.