I will clean your data and build ml classification models with scikit learn

Messy data ruins good models before they even start. I clean, preprocess, and structure your raw datasets, then build classification models with scikit-learn that actually hold up on new data.

What I handle:

• Missing values, duplicates, inconsistent formatting, and outliers
• - Querying and extracting data with SQL
• - Feature engineering and encoding for ML-readiness
• - Training and evaluating classification models (Logistic Regression, Naive Bayes, Decision Trees, and more)
• - Clear evaluation reports: accuracy, precision/recall, confusion matrix, ROC/AUC

Who this is for: founders, researchers, and teams who have a spreadsheet or CSV full of potential and need it turned into something a model can actually learn from.

Tools: Python, pandas, scikit-learn, Jupyter Notebook, SQL, and Power BI for visualization/dashboards.

Send me your dataset (or a sample) and a note on what you're trying to predict or classify, and I'll scope the right package for you.