I will build end to end machine learning models with preprocessing, training, insights

Up to 25k rows / 5 features, numeric data. Light preprocessing (missing values, simple encoding, optional scaling) + train/test split. 1 baseline model (classification or regression). Core metrics + 1 visual (confusion matrix or ROC). 3–5 bullet insights. Python code included.

Up to 100k rows / 7 features. Robust preprocessing pipeline (encoding/scaling, basic data-quality checks). 1 baseline model (Logistic/Linear) + 1 strong tree/ensemble model. Metrics + 2–3 visuals (incl. ROC/feature importance when applicable). Light tuning. Python code + short README.

Up to 500k rows / 15 features. Advanced preprocessing + deeper data-quality checks. Compare up to 3 models, targeted hyperparameter tuning, and deeper error analysis. 3–5 visuals + interpretability where applicable. Handover-ready Python code + documentation.

I work with structured data in CSV, Excel, or similar tabular formats. Typical datasets include customer data, sales records, survey results, or experimental measurements. If your dataset is very large or in a different format (e.g. SQL, JSON), feel free to message me first.

Absolutely. If you already know which algorithm you’d like to use (e.g. logistic regression, decision tree, random forest, KNN), just let me know and I’ll implement it. If you’re unsure, I’ll analyze your data and goals and recommend the most suitable approach.

Your data may require a custom approach if it includes: text columns, very high-cardinality categories (IDs, emails, URLs), many missing values, strong class imbalance, multiple target columns, time-series dependencies, data leakage risk, or heavy feature engineering needs. Please message me.