Tools and Technologies: Python, FastAPI, Machine Learning (Collaborative/Content-based Filtering), Tensorflow
Project Solution Approach: To build the Movie Recommendation API project, you would need a dataset containing information about movies, such as the MovieLens dataset, IMDb dataset, or TMDB dataset. Next, you must preprocess the dataset to extract relevant features such as genre, director, actors, and ratings. You can use tools like Pandas and NumPy for data cleaning and manipulation. Then, you will train a machine learning algorithm, such as collaborative or content-based filtering, using Python-based machine learning libraries like scikit-learn or TensorFlow to generate recommendations based on user preferences. After training the model, you will use FastAPI to create the API endpoints for user input and output. Users can input their preferred genres, actors, and directors, and the API will return a list of recommended movies based on the machine learning model's predictions. To handle user input, you will use FastAPI's request body feature to receive the user's input as a JSON object. You will also have to define the response model using Pydantic to ensure that the API returns a JSON object with the correct structure.
