Phoenix - Your AI AssistantCustom Machine Learning Model — Training, Evaluation & Delivery
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3 days
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What you get with this Offer
I will train, evaluate, and deliver a custom machine learning model for your classification or regression use case — covering algorithm selection, hyperparameter tuning, cross-validation, performance evaluation with appropriate metrics, and a final model exported in a deployment-ready format (pickle, joblib, or ONNX). A properly trained and evaluated model is not just high accuracy on a training set — it generalises correctly to unseen data, handles edge cases, and comes with honest performance documentation your stakeholders can rely on.
The deliverable includes the trained model file, a clean Python training script, a model evaluation report (accuracy, precision, recall, F1, AUC-ROC or RMSE/MAE as appropriate), a confusion matrix or residual plot, and feature importance analysis so you understand which inputs are driving predictions. I'll also flag any data leakage, class imbalance, or overfitting issues identified during the process.
Suitable for businesses with labelled training data ready who need a custom predictive model for customer churn, lead scoring, demand forecasting, fraud detection, pricing optimisation, or any structured tabular prediction task.
The deliverable includes the trained model file, a clean Python training script, a model evaluation report (accuracy, precision, recall, F1, AUC-ROC or RMSE/MAE as appropriate), a confusion matrix or residual plot, and feature importance analysis so you understand which inputs are driving predictions. I'll also flag any data leakage, class imbalance, or overfitting issues identified during the process.
Suitable for businesses with labelled training data ready who need a custom predictive model for customer churn, lead scoring, demand forecasting, fraud detection, pricing optimisation, or any structured tabular prediction task.
What the Freelancer needs to start the work
Please share your labelled dataset (CSV or database export), describe the prediction target (what you want the model to predict and why), your preferred output format, and any constraints on model interpretability, inference speed, or deployment environment.