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I will build a python ml model with shap
DFT Calculations, GCMC Simulations, Machine Learning for Materials
Welcome! I’m Danish Ilyas, a Computational Chemist and Materials Scientist. I deliver publication-ready DFT calculations in Gaussian, GCMC adsorption simulations in RASPA2, and ML models for materials...
About this Gig
What I will do
I will build a reproducible machine learning model in Python to predict your target property from your dataset (CSV/Excel). I handle data checking, model training, evaluation, and clear reporting so you can use the results in research or product work.
What you get
- Clean, reproducible Python code (notebook or scripts)
- Trained model (optional .pkl) + preprocessing pipeline
- Performance metrics (R²/MAE/RMSE or accuracy/F1/ROC-AUC)
- Clear plots (parity/residuals or confusion matrix/ROC)
- Optional: SHAP feature importance and interpretation (Premium)
Tools Python, pandas, NumPy, scikit-learn, (XGBoost/LightGBM if needed), TPOT (AutoML), SHAP.
Before ordering Please message me with your dataset size, target column, and goal (regression or classification). Ill confirm the best package and timeline.
Programming language:
Python
•
Colab
Frameworks:
Scikit-learn
•
Keras
•
PyTorch
Tools:
Jupyter Notebook
•
Excel
•
Colab
My Portfolio
FAQ
Q1: What dataset format do you accept?
CSV or Excel. You can also share a Google Drive link.
Q2: Can you work with chemical/materials datasets?
Yes—property prediction, descriptor-based ML, and model interpretation.
Q3: Will you provide code and trained model files?
Yes. You get code + optional saved model pipeline.
Q4: Do you guarantee accuracy?
No model can be guaranteed, but I ensure clean validation, transparent metrics, and improvement recommendations.
