Machine Learning Projects

End-to-end machine learning projects: data preparation, model training, evaluation, and the reasoning behind model choice.

Projects

Project	Techniques	Summary
Animal Behavior Classification	K-Means, PCA, Logistic Regression, Random Forest, SVM	Clusters cattle sensor data into four behaviors, then trains and compares three multiclass classifiers; Random Forest wins on rumination true-positives

Stack

Python · pandas · NumPy · SciPy · scikit-learn · Matplotlib · Seaborn (pip install -r requirements.txt)

Skills demonstrated

• Unsupervised learning (K-Means) with PCA-based cluster interpretation
• Multiclass classification with model comparison and confusion-matrix analysis
• Choosing a model on the metric that matters, not accuracy alone