This research analyses which model would be the most efficient for a movie recommendation system. We have compared a cosine similarity model (CSM) and 4 Neural network models - ANN, CNN, RNN, and RBFNN. We have a cleaned dataset of 45,000 movies, however, we have used a sample of it only. Using the sample as our main dataset each run we got randomized data, we created a user dataset that stores info for user_id, movie_list, and genre-lists, ensuring it is not biased. Using the user dataset, we compute each user’s top three genres they prefer. Then using all of these data sets we train, compile, and apply the various models to recommend 10 movies and their performance. We used a different user dataset for the cosine similarity model because we realized it wouldn’t be efficient. But the same ones for the neural networks. Lastly, we compared the performances of each neural network and cosine similarity model (CSM) to determine the best model. We did 6 runs, 1 run on a sample of 5000 movies and 5000 users each of 50 movies. To confirm the obtained best model, we ran the code 5 more times but this time with a sample of 1000 movies and 1000 users for each of 50 movies.