🩺 Breast Cancer Classification using Machine Learning

A machine learning classification project focused on predicting whether a breast tumor is malignant or benign using the Breast Cancer Wisconsin dataset from scikit-learn.

---

📌 Project Objective

The objective of this project is to build and evaluate multiple machine learning classification models while understanding:

• Classification techniques
• Evaluation metrics
• ROC Curve & AUC analysis
• Handling imbalanced data
• Model comparison
• Feature importance interpretation

---

📂 Dataset Information

• Dataset: Breast Cancer Wisconsin Dataset
• Source: scikit-learn built-in dataset
• Total Samples: 569
• Total Features: 30

🎯 Target Classes

Value	Meaning
0	Malignant
1	Benign

---

⚙️ Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn

---

🤖 Machine Learning Models

The following classification models were implemented:

Model	Purpose
Logistic Regression	Baseline classification
Decision Tree	Rule-based classification
Random Forest	Ensemble learning

---

📊 Evaluation Metrics

The models were evaluated using:

• Accuracy
• Precision
• Recall
• F1-score
• Confusion Matrix
• ROC Curve
• AUC Score

---

📈 Project Workflow

1. Data Loading
2. Exploratory Data Analysis (EDA)
3. Missing Value Analysis
4. Feature Correlation Heatmap
5. Train-Test Split
6. Feature Scaling
7. Logistic Regression Modeling
8. ROC-AUC Evaluation
9. Handling Imbalanced Data
10. Decision Tree Classification
11. Random Forest Classification
12. Model Comparison
13. Feature Importance Analysis
14. Final Insights & Conclusion

---

🏆 Results Summary

Model	Accuracy
Logistic Regression	98.24%
Decision Tree	91.22%
Random Forest	95.61%

✅ Best Performing Model

Logistic Regression achieved the strongest overall balance between:

• Accuracy
• Stability
• Interpretability
• ROC-AUC performance

---

🔍 Key Insights

• Recall is highly important in medical diagnosis because false negatives may lead to undetected cancer cases.
• ROC-AUC analysis provides better evaluation than accuracy alone.
• Random Forest improved predictive capability using ensemble learning.
• Feature importance analysis identified influential medical indicators.

---

📁 Repository Structure

AI_ML_Task4_Classification_Project/
│
├── AI_ML_Task4_Classification.ipynb
├── AI_ML_Task4_Classification.pdf
└── README.md

---

🚀 Conclusion

This project demonstrates how machine learning classification techniques can be applied to real-world medical diagnosis problems using proper evaluation metrics and model comparison techniques.

---

👨‍💻 Author

Sahil Bhatti