Welcome to the Movie Recommendation System project! This innovative system leverages advanced machine learning techniques to provide personalized movie recommendations. Whether you’re an avid movie watcher or just looking for the perfect film, our system has got you covered!

This project combines collaborative filtering, content-based filtering, and hybrid approaches to deliver powerful movie recommendations. Key features include analyzing movie metadata and uncovering fascinating trends in movie data.

• Theory: A matrix factorization technique that decomposes the user-item interaction matrix into three components:
  • U: User features matrix.
  • Σ: Singular values (diagonal matrix).
  • Vᵀ: Item features matrix.
• Relevance: SVD reduces dimensionality, enabling predictions of missing ratings and uncovering latent factors in user-item interactions.

• Theory: An extension of SVD that incorporates implicit feedback (e.g., clicks, views, purchases) in addition to explicit ratings.
• Relevance: Provides more accurate recommendations by leveraging both observed and unobserved user interactions.

• Combines outputs from SVD and SVD++ to improve performance by averaging their results.

• Concept: Combines collaborative filtering and content-based filtering using weighted scores.
• Steps:
  1. Generate recommendations from collaborative and content-based models.
  2. Normalize scores to a [0, 1] range.
  3. Combine scores based on predefined weights.
  4. Return top recommendations.

• Key Features:
  • Uses TF-IDF vectorization to analyze movie descriptions.
  • Employs cosine similarity to calculate the relevance between movies.
  • Suggests movies based on genres and content similarity.
• Model Persistence: Saves and loads the recommendation model using pickle files.

1. Movie Recommendations:

   • Suggests movies based on genres, metadata, and user preferences.
   • Provides real-time personalized recommendations.

2. Hybrid Recommendations:

   • Combines collaborative and content-based filtering for enhanced accuracy.

3. Visualizations:

   • Stunning charts and graphs to explore genre distributions and trends.
   • Word clouds for genre frequency visualization.

4. Data Analysis:

   • In-depth exploration of movie datasets to uncover patterns and trends.

• Python: Core programming language for building the system.
• Pandas: Data cleaning and manipulation.
• Surprise: Machine learning models, including SVD and content-based filtering.
• Matplotlib & Seaborn: Data visualization libraries.
• WordCloud: Visual representation of genre frequencies.

1. Data Preparation:

   • Clean and preprocess movie datasets using Pandas.
   • Vectorize movie descriptions using TF-IDF.

2. Recommendation Engine:

   • Collaborative filtering (SVD, SVD++).
   • Content-based filtering using cosine similarity.
   • Hybrid model combining both approaches with weighted scores.

3. Model Persistence:

   • Save the recommendation model as a pickle file for future use.

• Genre Distributions: Explore how movie genres are represented in the dataset.
• Trends: Visualize changes in genre popularity over time.
• Word Clouds: Highlight the frequency of different genres in a creative way.

• Personalized movie recommendations for users.
• Analyzing trends in movie data for insights and business decisions.

1. Clone the repository:

   git clone https://github.com/Recommendation-System-ML/Recommendation-system-with-prediction-project.git

2. Install the required libraries:

   pip install -r requirements.txt

3. Run the system:

   python main.py

4. Explore recommendations and visualizations through the provided interface.

We welcome contributions! Feel free to open issues, submit pull requests, or suggest features to enhance the system.