Movie-Class-Genre-Classification

1. Data Collection and Preprocessing: Gathered and cleaned a dataset of movies, including metadata like titles, descriptions, and genres, using sources like IMDb or TMDb. Applied natural language processing (NLP) techniques to preprocess text data (e.g., tokenization, removing stop words).

2. Feature Extraction: Extracted key features from movie descriptions and other metadata using techniques like TF-IDF, word embeddings , transformers to create numerical representations for model input.

3. Model Development and Training: Built and trained classification models using machine learning techniques like Naive Bayes, Random Forests to predict movie genres based on the text description.

4. Evaluation and Optimization: Evaluated model performance using metrics like accuracy, precision, recall, and F1-score. Fine-tuned hyperparameters to improve classification performance and handled multi-label classification challenges where movies have multiple genres.

5. Deployment and Results: Documented results, highlighting model accuracy and improvements over baseline models of genre overlap in multi-label classification.