This project develops a predictive machine learning model using the AdaBoost algorithm to classify breast cancer as malignant or benign. The workflow includes data preprocessing, feature engineering, EDA, model training, evaluation, and feature-importance interpretation.
Built an AdaBoost classifier achieving ~95% accuracy.
Applied preprocessing pipelines using NumPy & Pandas.
Performed statistical EDA and visualization using Matplotlib & Seaborn.
Evaluated model using accuracy, confusion matrix & classification report.
Generated feature-importance insights to improve interpretability.
Machine Learning, EDA, Feature Engineering, Ensemble Learning, Model Evaluation, Python