Breast Cancer

In the Medical Diagnosis Classification project, I was responsible for developing an end-to-end system for predicting medical conditions using patient data. Performed data cleaning, categorical encoding, and scaling to

prepare the dataset for modeling.

Implemented and evaluated multiple models (logistic regression, random forest, KNN, decision tree, naive Bayes, and XGBoost) to capture different patterns in the data.

The project utilized Python, Pandas, NumPy, Scikit-learn,

and XGBoost for preprocessing, modeling, and evaluation.

Results showed that XGBoost was the best-performing model based on precision, recall, and F1-score, ensuring reliable medical diagnosis.