The purpose of this project is to develop a movie recommendation system using Python and machine learning algorithms, specifically cosine similarity. The system aims to provide personalized movie recommendations to users based on their preferences and similarities to other users. The cosine similarity algorithm will be used to measure the similarity between movies and users, allowing for effective recommendation generation. The project involves data collection and pre-processing, where a dataset of movie ratings and user information will be gathered. Feature extraction techniques will then be applied to extract relevant information from the dataset, such as genre, director, and actors. The cosine similarity algorithm will be implemented to compute the similarity scores between movies and users based on their shared features. Evaluation metrics will be employed to assess the performance of the recommendation system, such as precision, recall, and accuracy. The experimental setup will involve splitting the dataset into training and testing sets, ensuring robustness of the system. Results and analysis will be presented to showcase the effectiveness of the system in providing accurate and personalized recommendations. In conclusion, this project aims to develop a movie recommendation system using Python and the cosine similarity algorithm, providing users with personalized movie suggestions based on their preferences. The implementation of this system has the potential to enhance the movie-watching experience and facilitate movie discovery for users. Future work may involve incorporating additional machine learning algorithms and enhancing the system's scalability