🎗️ Pinktober: Breast Cancer Classification Challenge

This notebook was developed for Pinktober, a Micro Club internal datathon held in October during Breast Cancer Awareness Month.

🧠 Objective

To build a robust machine learning model to classify breast cancer cases based on anonymized patient data.

🧩 Workflow

• Data Exploration: Statistical summaries and visualizations
• Preprocessing: Feature selection, normalization with MinMaxScaler
• Modeling: Tried Logistic Regression, KNN, Decision Trees, and Random Forests
• Evaluation: Confusion matrix, classification report, and metrics (Precision, Recall, F1)

🎯 Final Results

• Accuracy: 0.98246
• Precision: 0.97872
• Recall: 0.97872
• F1-score: 0.97872

🛠️ Tech Stack

Python, Pandas, Seaborn, Scikit-learn, Matplotlib

🤝 Context

Created and submitted during a 48-hour ML datathon organized by Micro Club.