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Fiverr Pro
English
$
USD
I will build custom machine learning models
About this Gig
I build custom machine learning solutions that transform business data into reliable predictions, actionable insights, and decision-support tools.
I develop machine learning models for business applications, including classification, regression, forecasting, customer analytics, fraud detection, and predictive modeling.
My services may include:
- Data analysis and preprocessing
- Feature engineering and feature selection
- Classification or regression model development
- Model comparison and hyperparameter optimization
- Cross-validation and error analysis
- SHAP and feature importance analysis
- Business-oriented performance evaluation
- Prediction APIs and Docker configuration
- Interactive dashboards and monitoring structures
- Complete source code and documentation
I do not evaluate models using accuracy alone. Metrics such as precision, recall, F1-score, ROC-AUC, PR-AUC, MAE, RMSE, MAPE, and R² are selected according to the business problem and the cost of prediction errors.
Please contact me before ordering when the project involves multiple datasets, unstructured data, data collection, model deployment, large-scale infrastructure, or custom AI agents.
FAQ
What types of machine learning problems do you work with?
I work with classification and regression problems, including churn prediction, lead scoring, conversion prediction, fraud detection, risk classification, price prediction, customer value prediction, and other predictive analytics use cases.
What data do I need to provide?
You should provide a CSV, Excel, Parquet, JSON, SQL export, or database sample containing the variables required for the analysis. A description of the columns and business objective is strongly recommended.
Can you guarantee a specific accuracy score?
No reliable data scientist can guarantee a performance score before analyzing the data. Model performance depends on data quality, target definition, sample size, class balance, signal availability, and the nature of the problem.
Do you work with imbalanced classification problems?
Yes. Depending on the problem, I may use class weighting, threshold optimization, resampling, precision-recall analysis, calibration, or other appropriate techniques.
Which algorithms may be used?
Possible algorithms include logistic regression, linear regression, random forest, gradient boosting, XGBoost, LightGBM, CatBoost, support vector machines, neural networks, and ensemble methods. The final selection depends on the data and business objective.
Is source code included?
Yes. All packages include the relevant notebook or Python code. Standard and Premium include a more organized and reusable project structure.
Is cloud deployment included?
Premium includes a deployment-ready API and Docker configuration. Deployment to AWS, Azure, Google Cloud, a VPS, or another environment is purchased separately because infrastructure requirements vary.
Does the Premium package include ongoing monitoring?
It includes the monitoring structure, metrics, dashboard, and drift-detection setup. Continuous operation, infrastructure maintenance, alerts, and recurring monitoring require a separate service.
Do you build AI agents and RAG systems?
Yes. AI agents, RAG pipelines, LLM integrations, tool-calling systems, workflow automation, and related solutions are handled through custom offers.
Will my data remain confidential?
Yes. Client data and project information are treated as confidential. An NDA can also be reviewed when required.
