📚 Project Overview
The Breast Cancer Predictor applies machine learning to classify tumors as malignant or benign, helping demonstrate how AI can support early detection in healthcare. Built with Python and scikit-learn, it transforms real-world data into actionable predictions.

🧪 Data Analysis & Preparation
Started by cleaning and preprocessing the dataset, performing exploratory analysis to understand feature importance. Applied scaling and encoding to prepare the data for machine learning algorithms.

🤖 Model Building & Evaluation
Trained classification models like logistic regression and decision trees, comparing accuracy, precision, and recall to choose the best performer. Used train/test splits to validate results and avoid overfitting.

📈 Visualization & Insights
Leveraged Matplotlib and Seaborn to visualize feature distributions, correlations, and confusion matrices, making model predictions easier to interpret and explain.

⚙️ Key Features

• Predicts if tumors are benign or malignant

• Multiple classification models evaluated

• Data preprocessing for higher accuracy

• Visual explanations of model results

• Clean, modular Python code with documentation

🧠 Learnings & Impact
This project strengthened my skills in medical data analysis, supervised learning, and performance evaluation. It also deepened my understanding of how machine learning can support real-world healthcare use cases.

🛠 Tech Stack & Tools
Languages & Libraries: Python, scikit-learn, Pandas, NumPy, Matplotlib, Seaborn
Version Control: Git & GitHub
Development Environment: Jupyter Notebook & VS Code

🔗 Project Repository
View on GitHub → Breast Cancer Predictor