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$
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I will build machine learning and deep learning models for research data
From raw data to trained model to live product, end to end!
Applied statistician, ML/DL researcher, and Web Developer (MSc in Statistics, Univ. of Rajshahi). End-to-end work - raw data to trained model to live web product.
Seven research papers in ML, causal ...
About this Gig
Hi, I'm Fahim, an AI/ML researcher with the background in Statistics. I build defensible machine learning, deep learning, and neural network models for thesis writers, journal authors, and analysts who need a result that survives review.
What you'll get:
- Python or R notebook, cleaned data, validated models
- 4-6 publication-ready figures, metrics with confidence intervals
- Methods note ready for thesis or reviewer response
- Revision support for premium packages
Methods I cover:
- Classification, regression, clustering, time series forecasting
- Cross-validation, bootstrap, permutation tests, SHAP, feature importance
- Logistic Regression, Random Forest, XGBoost, LightGBM, ARIMA, Prophet, LSTM, BERT, CNN, Transformer and so on
- Any else methods you want to include!
Why me:
- AI/ML researcher with published papers as lead author
- Workflows tested in peer review, not metric-tuned
- Confidential data, hourly response, scope-matched revisions
Not sure which method fits? Send your research objective, target variable, and a sample.
You'll have a recommendation and a package or custom offer within the hour.
My Portfolio
Other Data Science & ML Services I Offer
FAQ
What kinds of ML problems can I bring?
Classification, regression, clustering, forecasting, model comparison, feature selection, or explainability tasks. The gig handles tabular, time series, text, and survey datasets at research scale.
Python or R?
Either, or both. Notebooks delivered in Jupyter or R Markdown. Cross-language replication is available as an add-on if a coauthor uses the other tool.
Which models will work for my data?
Common choices include Logistic Regression, Random Forest, XGBoost, LightGBM, SVM, ARIMA, SARIMA, Prophet, LSTM, GRU, BERT, and tailored neural networks when the data justifies them. Share the objective and a sample first. The right model depends on your question, not on a preference.
Will the code be reusable?
Yes. The notebook includes comments, library notes, and a clean structure so a coauthor or supervisor can rerun the entire workflow.
Can you recommend a method before I order?
Yes. Send the research objective, target variable, dataset shape, and deadline. You will receive a method recommendation and the right package within the hour.
Is this allowed for thesis or journal work?
Yes, as ethical research support. The gig delivers modeling, validation, code, figures, interpretation, and revision help.
Can you improve an existing model?
Yes. Share the current code or notebook. You will receive a review, improved preprocessing, tuning, alternative comparisons, added validation, or clearer outputs.
What if my data is messy or incomplete?
Cleaning, missing-value handling, encoding, imbalance checks, and a documented preprocessing trail are part of every package.
What kinds of figures will I get?
Confusion matrices, ROC and PR curves, calibration plots, feature importance, SHAP visuals, forecast plots, and model comparison tables. All styled for journal submission.
Should I message before ordering?
Yes. The right package depends on dataset size, target variable, model type, and deadline. A two-minute scope check now prevents the wrong package later.
