For movie recommend ation people reaction and action and details about data

1. Movie Recommendation System
Using Collaborative Filtering
Name: Madhan
Mentor: Mrs. Selva Priya
Duration: 1 Month
Domain: Data Science

2. Introduction
Importance of movie recommendation systems
Why collaborative filtering?
Examples: Netflix, Prime Video

3. Objectives
• Build a personalized recommender
• Use collaborative filtering (user-item ratings)
• Improve user experience

4. Problem Statement
• Users waste time searching for movies
• Existing systems show only trending movies
• Our system → personalized suggestions

5. Dataset Overview
Source: MovieLens dataset (~100k ratings, ~9k movies)
Features: userId, movieId, title, genres, rating

6. Tools & Technologies
• Python (Core Programming)
• NumPy & Pandas (Data Handling)
• Matplotlib & Seaborn (Visualization)
• Scikit-Learn (ML algorithms, evaluation)
• Surprise (Collaborative Filtering library)
• Streamlit (Deployment – Web App)
• Google Colab / Jupyter Notebook (Development Environment)
• Kaggle (Dataset Source & Experimentation)

7. Workflow
Data CollectionData Cleaning EDA
Collaborative
Filtering Model
Evaluation Deployment

8. EDA – Genre Popularity

9. EDA – Rating Distribution

10. EDA – Top Rated Movies

11. Collaborative Filtering
• User-User & Item-Item similarity
• Matrix Factorization (SVD)
• User–Item matrix with missing values filled

12. Model Building
Libraries: Pandas, Surprise, Sklearn
Train/Test split
Prediction using SVD

13. Model Evaluation
Metrics: RMSE, Precision@K, Recall@K
Example: RMSE ≈ 0.85

14. Results
If user liked Inception → recommends Interstellar, Prestige, Shutter Island

15. Deployment
Streamlit app demo
Input: User/Movie Name → Output: Top 5 recommendations

16. Applications
OTT platforms (Netflix, Prime)
E-commerce personalization
Music & Book recommendations

17. Future Work
Add NLP from reviews
Real-time recommendations
Cloud deployment (AWS/Heroku)

18. Conclusion
• Collaborative Filtering improves personalization
• Works well on large datasets
• Extendable to other domains

19. References
• MovieLens (Kaggle)
• Surprise, Sklearn, Streamlit
• Research papers