MovieMetrix

MovieMetrix is a personalized movie recommendation system built using Streamlit. The application leverages natural language processing (NLP) techniques and machine learning to provide tailored movie recommendations based on user preferences and moods.

Features

• Personalized Recommendations: Input your movie preferences and get a list of recommended movies based on cosine similarity.
• Mood-Based Recommendations: Enter your current mood to receive movie recommendations that match your sentiment using TextBlob sentiment analysis.
• Filter Movies: Filter movies by director, actor, or genre for more targeted recommendations.

Installation

1. Clone the repository:

   git clone https://github.com/RashmitTopG/MovieMetrix.git

2. Navigate to the project directory:

   cd MovieMetrix

3. Install the required packages:

   pip install -r requirements.txt

4. Download the necessary NLTK data:

   import nltk
   nltk.download('punkt')
   nltk.download('stopwords')
   nltk.download('wordnet')

Technologies Used

• Streamlit for building the web application
• Pandas for data manipulation
• scikit-learn for vectorization and similarity calculations
• TextBlob for sentiment analysis
• NLTK for text preprocessing

Screenshots of the project have been uploaded above.