In this project, I developed a machine-learning model for a movie recommender system. The model is built using NumPy, Pandas, CSV, Matplotlib, Seaborn, and Scikit-learn. The model analyzes a dataset of movies, calculates the similarity, and recommends similar movies based on the user's preferences.

Here are the modules that this project uses:

• NumPy
• Pandas
• CSV
• Matplotlib
• Seaborn
• Scikit-learn

This project uses a dataset of movies. The dataset is stored in two CSV files:
tmdb_5000_movies.csv and tmdb_5000_credits.csv.
The 2nd CSV file is too big to upload(about 39 Mb), you can download from:
https://www.kaggle.com/datasets/tmdb/tmdb-movie-metadata
The dataset contains information about movies such as their titles, overviews, genres, keywords, cast, and crew.

The project extracts the dataset features, transforms dataset column values, and calculates similarity via cosine_similarity.