I will build and train machine learning models for prediction and classification
About this Gig
Struggling with data that needs a predictive model, not just a chart?
I'll build and train machine learning models for prediction and classification turning your raw data into a working model that actually performs, backed by real evaluation metrics you can trust.
What you'll get:
- Data preprocessing and feature engineering
- Trained classification or prediction model (XGBoost, Scikit-learn)
- Model testing, evaluation, and optimization
- Handling of imbalanced data (SMOTE) when needed
- Full source code and documentation (Standard & Premium)
Use cases I specialize in:
Fraud detection, credit risk/loan default prediction, stock trend classification, and financial sentiment analysis.
Tools I use:
Python, Scikit-learn, XGBoost, PyTorch, Pandas, Jupyter/Colab
Why choose me:
- Models with proven results up to 0.9998 ROC-AUC on real datasets
- Clear evaluation reports, not just a black-box model
- Fast turnaround and responsive communication
Send me your dataset and the outcome you want to predict I'll handle the rest.
Programming language:
Python
•
SQL
•
Colab
Frameworks:
Scikit-learn
•
Keras
•
PyTorch
•
Panda
APIs:
Other
Tools:
Jupyter Notebook
•
TensorFlow
•
Excel
•
Colab
My Portfolio
FAQ
Q: What type of machine learning problems do you handle?
A: Classification and prediction problems — fraud detection, loan default, stock trend, and financial sentiment analysis are my strongest areas.
Q: What do you need from me to get started?
A: Your dataset (Excel/CSV) and a clear description of what you want to predict or classify.
Q: Will I receive the source code?
A: Yes on Standard and Premium packages. Basic delivers the trained model and evaluation report only.
Q: What if my dataset is imbalanced (way more of one class than another)?
A: I handle class imbalance using SMOTE and other resampling techniques — this is a core part of my workflow, not an add-on.

