Classification Jobs

I have a clean, structured numerical dataset and need a supervised machine-learning model built, validated, and handed over with clear documentation. The goal is to predict future outcomes from past observations, so model accuracy and interpretability both matter. Here’s what I need from you: • A brief data-exploration notebook that highlights key correlations, missing-value handling, and basic visuals. • Feature engineering tailored to the data’s domain (scaling, encoding, derived metrics, etc.). • At least two supervised algorithms (for example, Gradient Boosting and Random Forest in scikit-learn, or an XGBoost/TensorFlow alternative) trained, cross-validated, and benchmarked. • A concise performance comparison using appropriate regression/classif...

I have a clean, structured numerical dataset and need a supervised machine-learning model built, validated, and handed over with clear documentation. The goal is to predict future outcomes from past observations, so model accuracy and interpretability both matter. Here’s what I need from you: • A brief data-exploration notebook that highlights key correlations, missing-value handling, and basic visuals. • Feature engineering tailored to the data’s domain (scaling, encoding, derived metrics, etc.). • At least two supervised algorithms (for example, Gradient Boosting and Random Forest in scikit-learn, or an XGBoost/TensorFlow alternative) trained, cross-validated, and benchmarked. • A concise performance comparison using appropriate regression/classif...