Movie Recommendation System

This project focuses on building a movie recommendation system using AI/ML techniques. The system suggests similar movies based on user preferences, movie genres, keywords, cast, crew, and movie descriptions.

Dataset

• The dataset used in this project consists of two CSV files:
  • tmdb_5000_movies.csv: Contains information about movies such as title, overview, genres, keywords, etc.
  • tmdb_5000_credits.csv: Contains information about movie credits including cast and crew details.

Data Preprocessing

• Merging: The two datasets are merged using the movie titles as the common key.
• Cleaning: Null values are dropped and duplicates are removed from the merged dataset.
• Feature Selection: Selected features include title, overview, genres, keywords, cast, and crew.

Feature Engineering

• Data Transformation: Certain columns containing JSON-formatted data (genres, keywords, cast, crew) are converted into lists.
• Text Processing: Textual data such as overviews, genres, keywords, cast names, and crew names are processed to remove spaces, convert to lowercase, and perform stemming.

Vectorization

• Count Vectorization: The textual data is vectorized using CountVectorizer to convert it into numerical form.
• Feature Extraction: The most frequent words are selected as features, and their occurrences are counted.

Similarity Calculation

• Cosine Similarity: Cosine similarity is calculated between vectors to measure the similarity between movies.
• Recommendations: Based on the similarity scores, top similar movies are recommended to the user.

Implementation

• The recommendation function takes a movie title as input and suggests similar movies.
• Visualization (Optional): PCA is used for visualization of the vectorized data (commented out in the code).
• Example Usage: The recommend() function is called with a movie title to demonstrate how the system works.

Technologies Used

• Python
• Libraries: Pandas, NumPy, NLTK, scikit-learn
• Data Visualization: Matplotlib (for optional visualization)

Setup and Installation

• Install the required Python libraries (Pandas, NumPy, NLTK, scikit-learn).
• Download the dataset files (tmdb_5000_movies.csv and tmdb_5000_credits.csv).
• Run the provided code to preprocess the data, extract features, and build the recommendation system.
• Call the recommend() function with a movie title to get recommendations.

For any issues or improvements, please feel free to contribute or reach out.

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