🎬 Movie Recommendation System

This project is a Capstone Project completed as part of the Edureka Data Science with SQL course.
It showcases a complete movie recommendation system using the MovieLens dataset.

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📚 About the Project

The project implements three types of recommendation systems:

1️⃣ Popularity-Based Recommender:
Recommends top-rated movies in a selected genre with a minimum review threshold.

2️⃣ Content-Based Recommender:
Suggests movies similar to a selected one based on genre similarity using TF-IDF and Cosine Similarity.

3️⃣ Collaborative Filtering Recommender:
Provides personalized movie recommendations based on ratings from similar users.

Interactive inputs and colorful visualizations make it easy to experiment with different genres, movies, and user IDs.

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🎯 Objective

The goal of this project is to apply data science techniques to create personalized content discovery systems, similar to platforms like Netflix or Prime Video.

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🛠️ Tools Used

• Python: Programming language
• Jupyter Notebook: Interactive coding environment
• Pandas: Data manipulation and analysis
• NumPy: Numerical computing
• scikit-learn: Machine learning models and similarity computations
• Plotly: Interactive data visualizations
• MovieLens Dataset: Real-world dataset for movies and ratings
• SQL (Course Context): While this project uses CSVs, concepts from SQL querying and data extraction are applied as part of the Edureka Data Science with SQL course

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📂 Project Structure

movie-recommendation-system/

├── README.md

├── LICENSE (MIT)

├── movie_recommendation_system.ipynb

├── movies.csv

├── ratings.csv

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• README.md: This documentation file
• LICENSE: Open-source MIT License
• movie_recommendation_system.ipynb: Main Jupyter Notebook with code and explanations
• movies.csv: Movie metadata (ID, title, genres)
• ratings.csv: User ratings data (user ID, movie ID, rating, timestamp)

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📊 Exploratory Data Analysis (EDA)

• 🎭 Genre Distribution: Visualized as a colorful horizontal bar chart
• ⭐ Rating Distribution: Visualized as a histogram with interactive features

These plots are generated using Plotly and are fully interactive in the notebook.

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🎯 Recommender Systems Implemented

1️⃣ Popularity-Based Recommender

• Input: Genre, Minimum number of reviews, Number of recommendations
• Output: Top-rated movies in the selected genre

2️⃣ Content-Based Recommender

• Input: Movie title, Number of similar movies to recommend
• Output: Similar movies based on genre similarity using TF-IDF and Cosine Similarity

3️⃣ Collaborative Filtering Recommender

• Input: User ID, Number of similar users (K), Number of recommendations
• Output: Personalized movie recommendations based on user similarity

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🏃‍♂️ Getting Started

1️⃣ Clone this repository: git clone https://github.com/Sravyatogarla/Movie-recommendation-system.git

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📚 Dataset Description

The data used in this project comes from the MovieLens dataset, a widely used dataset in recommender systems research.

• movies.csv: Contains movieId, title, and genres.
• ratings.csv: Contains userId, movieId, rating, and timestamp.

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• Key Highlights

1. Interactive user inputs for all three recommdation models

2. Clear and colorful data visualizations using Plotly

3. Clean and well-commented code with explanations

4.Real-world application of data science concepts from the Edureka Data Science with SQL course

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• Future Enhancements

Add advanced collaborative filtering (SVD, matrix factorization)

Build a web-based interface using Streamlit or Flask

Incorporate user feedback for dynamic recommendations

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• License

This project is licensed under the MIT License. See the LICENSE file for full details.

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👨‍💻 Author Sravya Togarla

Edureka Data Science with SQL Learner

Connect on LinkedIn www.linkedin.com/in/sravya-togarla

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Course Details This couse covers following topics :

• Python Programming

• Probabilty and Statistics

• Supervised Machine Learning

• Unsupervised Machine Learning

• Deep learning

• Natural language processing

• Data visualization using Tableau

• SQL for Data Science

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✅ Conclusion

This Movie Recommendation System demonstrates how data science can enhance user experience through personalized content discovery. With practical implementations of popularity-based, content-based, and collaborative filtering models, this project bridges the gap between learning and real-world applications.