"One line to install. One class to classify."
| Step |
Code |
Result |
| ① Import |
from bcpipeline import BinaryClassifier |
✅ Ready |
| ② Initialize |
clf = BinaryClassifier() |
✅ Configured |
| ③ Train |
clf.fit(X, y) |
🏆 Best model selected |
| ④ Predict |
clf.predict(X_new) |
🎯 Predictions ready |
| ⑤ Evaluate |
clf.evaluate() |
📊 Full metrics |
| 🔧 |
Feature Engineering |
Interaction · Ratio · Squared |
| 🤖 |
Multi-Model Comparison |
LR · SVM · RF · GBM |
| ⚙️ |
Hyperparameter Tuning |
Grid Search + 5-fold CV |
| 🎯 |
Ensemble Voting |
Soft voting top 3 models |
| 📈 |
Threshold Optimization |
Optimal decision boundary |
| Model |
Accuracy |
Status |
| Logistic Regression |
~95% |
🟡 Good |
| SVM (RBF) |
~90% |
🟡 Moderate |
| Random Forest |
~100% |
🟢 Excellent |
| Gradient Boosting |
~100% |
🟢 Excellent |
| Ensemble |
100% |
🌟 Outstanding |
| Category |
Tools |
| Language |
 |
| ML Framework |
 |
| Data Processing |
 |
| Visualization |
 |
This project is licensed under the MIT License.
🇸🇦 Riyadh, Saudi Arabia
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