Phoenix - Your AI AssistantBuild and evaluate a machine learning model for your data
Delivery in
2 days
- Views 70
Amount of days required to complete work for this Offer as set by the freelancer.
Rating of the Offer as calculated from other buyers' reviews.
Average time for the freelancer to first reply on the workstream after purchase or contact on this Offer.
What you get with this Offer
Turn your raw data into a reliable, explainable Machine Learning model built around your business objective.
I will prepare your data, compare suitable algorithms, optimise the strongest candidates and deliver a tested model with clear metrics, SHAP explanations and prediction uncertainty. You will understand how the model performs, why it makes its predictions and when they can be trusted.
WHAT I WILL DO
✓ Understand your objective and success criteria
✓ Review, clean and preprocess your dataset
✓ Handle missing values, outliers, encoding and scaling
✓ Create training, validation and test splits
✓ Build a transparent baseline model
✓ Compare suitable classification or regression algorithms
✓ Perform feature engineering and selection
✓ Use cross-validation for reliable evaluation
✓ Tune the strongest candidate with Optuna
✓ Select the final model using technical and business value
✓ Explain global and individual predictions with SHAP
✓ Estimate prediction uncertainty or confidence intervals
✓ Save a reproducible prediction pipeline
The model will not be selected on accuracy alone. Depending on your use case, I will consider precision, recall, F1, ROC-AUC, MAE, RMSE, false-prediction costs, interpretability and operational constraints.
YOU WILL RECEIVE
• Cleaned modelling dataset
• Reproducible preprocessing pipeline
• Baseline and model comparison results
• One optimised final ML model
• Cross-validation and test metrics
• SHAP explainability analysis
• Prediction uncertainty or confidence estimates
• Business-focused model recommendation
• Saved model and preprocessing pipeline
• Clean, documented Python code
• Jupyter notebook or Python scripts
• Evaluation summary and usage instructions
• Seven days of support for delivery-related bugs
BASE OFFER SCOPE
• One CSV or Excel dataset
• Up to 50,000 rows and 30 input features
• One classification or regression objective
• Standard CPU-based tabular ML models
• One final trained and evaluated model
NEED SOMETHING MORE CUSTOM?
For larger datasets, forecasting, Deep Learning, computer vision, NLP, multiple targets, API deployment, real-time data or causal analysis, please message me before ordering. I will recommend a tailored scope and quote.
Please send your dataset, target variable, business objective, preferred metric and examples of costly prediction errors.
I will prepare your data, compare suitable algorithms, optimise the strongest candidates and deliver a tested model with clear metrics, SHAP explanations and prediction uncertainty. You will understand how the model performs, why it makes its predictions and when they can be trusted.
WHAT I WILL DO
✓ Understand your objective and success criteria
✓ Review, clean and preprocess your dataset
✓ Handle missing values, outliers, encoding and scaling
✓ Create training, validation and test splits
✓ Build a transparent baseline model
✓ Compare suitable classification or regression algorithms
✓ Perform feature engineering and selection
✓ Use cross-validation for reliable evaluation
✓ Tune the strongest candidate with Optuna
✓ Select the final model using technical and business value
✓ Explain global and individual predictions with SHAP
✓ Estimate prediction uncertainty or confidence intervals
✓ Save a reproducible prediction pipeline
The model will not be selected on accuracy alone. Depending on your use case, I will consider precision, recall, F1, ROC-AUC, MAE, RMSE, false-prediction costs, interpretability and operational constraints.
YOU WILL RECEIVE
• Cleaned modelling dataset
• Reproducible preprocessing pipeline
• Baseline and model comparison results
• One optimised final ML model
• Cross-validation and test metrics
• SHAP explainability analysis
• Prediction uncertainty or confidence estimates
• Business-focused model recommendation
• Saved model and preprocessing pipeline
• Clean, documented Python code
• Jupyter notebook or Python scripts
• Evaluation summary and usage instructions
• Seven days of support for delivery-related bugs
BASE OFFER SCOPE
• One CSV or Excel dataset
• Up to 50,000 rows and 30 input features
• One classification or regression objective
• Standard CPU-based tabular ML models
• One final trained and evaluated model
NEED SOMETHING MORE CUSTOM?
For larger datasets, forecasting, Deep Learning, computer vision, NLP, multiple targets, API deployment, real-time data or causal analysis, please message me before ordering. I will recommend a tailored scope and quote.
Please send your dataset, target variable, business objective, preferred metric and examples of costly prediction errors.
What the Freelancer needs to start the work
To get started, please provide:
• Your dataset in CSV or Excel format
• A clear description of your business objective
• The decision the model should help you make
• The target to predict, recommend, forecast or segment
• A description of the columns, if their meaning is not obvious
• Your current process or performance baseline, if available
• The business metric you want to improve
• The potential cost of incorrect predictions, if relevant
• Any required deadline or technical constraints
Please anonymise confidential or personally identifiable information before sharing your dataset. Contact me before ordering if you are unsure which Machine Learning approach is appropriate.