-Developed a predictive model for breast cancer classification using machine learning algorithms such as Logistic Regression, Decision Tree, Random Forest, and Support Vector Classifier, leveraging Jupyter Notebook for data analysis and model development.

-Engineered and processed medical datasets to optimize the training and testing phases, ensuring high accuracy in predicting malignant or benign tumors based on gene abnormalities.

-Implemented data pre-processing techniques including normalization, feature selection, and cross-validation to enhance model performance and reliability.

-Integrated multiple machine learning models to compare and validate the accuracy and robustness of predictions, providing insights into the effectiveness of various algorithms in a healthcare context.