This project aims to build a movie recommendation system based on the TMDB 5000 Movie Dataset. By leveraging the concept of cosine similarity and performing data analysis, we can provide personalized movie recommendations to users.
The first step involves collecting the necessary data for movie recommendation. This includes movie descriptions, genres, and other relevant information. The TMDB 5000 Movie Dataset serves as our data source.
To ensure data quality, we need to preprocess the dataset. This involves cleaning the data, handling missing or incomplete values, and resolving any inconsistencies.
As the dataset contains textual features, we need to extract meaningful numerical representations from them. This may involve techniques like TF-IDF (Term Frequency-Inverse Document Frequency) to convert text data into numerical vectors.
To find similarities between movies, we calculate a similarity score using cosine similarity. By comparing the numerical representations of movies, we can determine their similarity and assign a similarity confidence score.
The recommendation system prompts the user for input, such as a movie they have enjoyed or a genre they prefer. Based on this input, the system suggests movies that are similar to the user's preferences.
Cosine similarity is employed as a percentage-based similarity algorithm. By converting each movie into a vector representation, we can measure the similarity between movies using cosine similarity. This enables us to identify movies that are most similar to the one provided by the user.
With these steps, we can generate a list of recommended movies based on user input and their similarity to other movies in the dataset.