Build custom data science and machine learning models in Python

Need high-accuracy predictive intelligence, classification models, or statistical forecasting built on your business data? I provide end-to-end data science solutions using modern Python machine learning libraries to uncover deep data patterns and build scalable predictive algorithms.

What this offer includes for the base price:
- Exploratory data profiling & feature correlation analysis
- Feature engineering, encoding, and dataset train/test splitting
- 1 Trained baseline Machine Learning model (Classification, Regression, or Clustering)
- Hyperparameter tuning using Cross-Validation (GridSearchCV / Optuna)
- Complete evaluation metrics report (Confusion matrix, ROC-AUC, RMSE, R² score, Precision/Recall)
- Well-documented Jupyter Notebook (.ipynb) with clean Python code

Data Science & ML Capabilities:
- Algorithms: XGBoost, LightGBM, Random Forest, Logistic/Linear Regression, K-Means, PCA
- Libraries & Frameworks: Scikit-Learn, Pandas, NumPy, SciPy, Statsmodels, PyTorch
- Use Cases: Customer churn prediction, sales forecasting, credit risk scoring, anomaly detection, sentiment classification
- Feature Selection: Recursive feature elimination, SHAP values, feature importance ranking
- Deployment: Pickled model artifacts (.pkl / .joblib) with inference script for new inputs

Have an enterprise predictive task, imbalanced dataset, or time-series forecasting problem? Reach out to review your project scope!