I will build and train a machine learning model using python scikit learn or pytorch
Python Developer, Machine Learning, AI Automation and Data Science
About this Gig
I will build and train a custom machine learning model using python (scikit-learn or PyTorch) tailored to your specific dataset and business goal
Whether you need a classification model, regression model or predictive analytics solution I will deliver a clean well-documented python solution with full source code
WHAT I OFFER
Data preprocessing & feature engineering
Train ML models: Logistic Regression, Random Forest, SVM, XGBoost, Neural Networks
Model evaluation: Accuracy, Precision, Recall, F1-Score, Confusion Matrix
Hyperparameter tuning for best performance
Clean Jupyter Notebook or Python script with full comments
Summary report of results (PDF or Markdown)
USE CASES I HAVE WORKED ON
Customer churn prediction
Sales forecasting
Disease prediction (medical datasets)
Spam/email classification
Image classification (PyTorch CNN)
️ TECH STACK
Python | scikit-learn | PyTorch | Pandas | NumPy | Matplotlib | Seaborn | XGBoost | Jupyter Notebook
YOU WILL RECEIVE
Full Python source code (.py or .ipynb)
Trained model file (.pkl or .pt)
Performance report with accuracy metrics
Neat & Clean code
Tell me your dataset and goal so I can confirm I can deliver exactly what you need.
Expertise:
Classification
•
Clustering
•
Decision trees
Programming language:
Python
Frameworks:
Scikit-learn
•
PyTorch
Tools:
Jupyter Notebook
•
Excel
My Portfolio
FAQ
What format should I send my dataset in?
CSV or Excel (.xlsx) format works best. If you have a different format (SQL, JSON) message me first and I will confirm compatibility
Can you work with my own custom dataset?
Yes, absolutely. I work with any tabular dataset sales data, medical data, student records, customer data or any CSV file you provide. Message me before ordering to discuss your specific use case
Will I receive the source code?
Yes. All packages include full Python source code either as a .py script or Jupyter Notebook (.ipynb). The code is fully commented so you can understand and modify it easily.

