I will create personalized recommendation systems using machine learning
Transforming Data into Intelligent Solutions with Machine Learning
About this Gig
Are you looking to build a powerful recommendation system for your platform? I specialize in developing custom machine-learning models using Python and TensorFlow. With deep learning, LLMs, and collaborative filtering expertise, I create solutions tailored to your business. Whether it's a social network or an e-commerce platform, my data science skills ensure that your users get personalized recommendations. From concept to deployment, I integrate machine learning into your software development process, delivering efficient and scalable solutions.
Tools & Technologies:
- Python (pandas, Scikit-learn, TensorFlow, PyTorch)
- Matrix factorization
- Neural networks
- Flask/Django for API integration
Why Choose Me?
- Tailored solutions to your business needs
- Expertise in both traditional and deep learning approaches
- Full deployment of recommendation engines, including API and database integration
- Clear documentation and support
Let's build the perfect recommendation system to enhance your business.
Programming language:
Python
•
SQL
Frameworks:
Scikit-learn
•
Keras
•
PyTorch
•
Panda
APIs:
Other
Tools:
Jupyter Notebook
•
TensorFlow
•
Excel
My Portfolio
Other Data Science & ML Services I Offer
FAQ
What type of recommendation system is best for my business?
It depends on your data and user interaction depends on your data and user interaction patterns. I can help you choose between collaborative, content-based, or hybrid systems based on your ow patterns. I can help you choose between collaborative, content-based, or hybrid systems based on your goals.
Can I integrate this with my website or app?
Yes, I will create an API for your recommendation engine to integrate with your website, mobile app, or CRM system.
What kind of data do you need to build the recommendation system?
Ideally, I need user-item interaction data (e.g., purchase history, user ratings, browsing history). The more detailed your data, the better the recommendations.

