Recently, I built a movie recommendation system as part of my learning in Artificial Intelligence and Machine Learning.
The idea is simple — when a user enters a movie name, the system suggests similar movies.
💡 Why I Built This
While learning machine learning, I wanted to build something practical instead of just studying theory.
Recommendation systems are used in platforms like Netflix and YouTube, so I thought it would be a good project to understand how they work.
🚀 What the Project Does
The system takes a movie name as input and recommends similar movies based on:
- overview (story)
- genres
- keywords
- cast
- director
🧠 How It Works (Simple)
- Combine all movie details into one text
- Convert text into numbers using TF-IDF
- Find similarity between movies using cosine similarity
- Return top 5 similar movies
🛠 Tech Used
- Python
- scikit-learn
- pandas
- Flask
- HTML, CSS, JavaScript
🔄 How It Works
- User enters a movie name
- Request goes to Flask backend
- Model finds similar movies
- Results are shown on screen
🧪 What I Learned
- basics of recommendation systems
- NLP and text processing
- TF-IDF and similarity
- how to connect ML with a web app
🔗 GitHub
https://github.com/alenkurian-ml/Content-Based-Movie-Recommendation
📌 Final Thoughts
This is a simple project, but it helped me understand how recommendation systems actually work.
I’m still learning and building projects step by step.
Thanks for reading 🙂
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