I will build ai recommendation system using machine learning
Machine Learning Engineer with 2 years of hands on experience
Over deze dienst
I develop intelligent recommendation engines using Machine Learning and Data Science techniques that deliver accurate and personalized suggestions based on your data.
I can build:
- Movie Recommendation Systems
- Product Recommendation Systems
- Music & Video Recommenders
- Book Recommendation Systems
- News & Content Recommendation
- Personalized User Recommendation Engines
What you'll get:
- Data preprocessing & feature engineering
- Content-Based Filtering
- Collaborative Filtering
- Hybrid Recommendation Models
- Model training & optimization
- Flask/FastAPI web application
- Interactive and modern UI
- API integration (TMDB, etc.)
- Clean, well-documented source code
- Deployment assistance
- Post-delivery support
Whether you're building an AI startup, final-year project, research project, or business application, I'll deliver a scalable, accurate, and production-ready recommendation system tailored to your needs.
Please contact me before placing an order so we can discuss your requirements and choose the best solution for your project.
Programmeertaal:
Python
•
R
•
SQL
Frameworks:
Scikit-learn
•
Panda
•
Overige
API's:
Microsoft Computer Vision AI
Tools:
Jupyter-notitieboek
•
opencv
•
Excel
•
Colab
•
RStudio
Mijn portfolio
Andere Data science en ML diensten die ik aanbied
Veelgestelde vragen
What types of recommendation systems can you build?
I can develop movie, product, music, book, news, restaurant, e-commerce, and personalized recommendation systems using Machine Learning and AI techniques.
Which recommendation algorithms do you use?
Depending on your project, I use Content-Based Filtering, Collaborative Filtering, Hybrid Recommendation Systems, Cosine Similarity, Matrix Factorization, TF-IDF, NLP, and other modern ML techniques.
Can you build a complete web application?
Yes. I can develop a complete recommendation website using Flask or FastAPI with a modern responsive UI, search functionality, posters/images, API integration, and deployment support.
Can you use my own dataset?
Absolutely. I can build the recommendation engine using your custom dataset or help collect, clean, and preprocess the data if needed.
Do you provide the source code?
Yes. You'll receive clean, well-structured, and documented source code along with setup instructions.
Can you integrate external APIs?
Yes. I can integrate APIs such as TMDB, Spotify, OpenLibrary, News APIs, and other third-party services to enhance recommendations.
Will the recommendation system work for new users?
Yes. Depending on your requirements, I can implement cold-start solutions, popularity-based recommendations, or hybrid models for new users.
Can you improve an existing recommendation system?
Yes. I can optimize recommendation accuracy, improve performance, redesign the UI, fix bugs, and add new features.
Which technologies do you use?
I primarily work with Python, Scikit-learn, Pandas, NumPy, Flask, FastAPI, HTML, CSS, JavaScript, Bootstrap, SQLite, and REST APIs.
What do you need before starting?
Please provide your project requirements, dataset (if available), desired features, preferred technology stack, and any UI or design references.

