"One line to install. One class to classify."
📦 Installation
pip install git+https://github.com/mazyad-alrashidi/binary-classification-pipeline.git
⚡ Usage
| 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 |
💎 Features
| 🔧 | 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 |
📊 Performance
| Model | Accuracy | Status |
|---|---|---|
| Logistic Regression | ~95% | 🟡 Good |
| SVM (RBF) | ~90% | 🟡 Moderate |
| Random Forest | ~100% | 🟢 Excellent |
| Gradient Boosting | ~100% | 🟢 Excellent |
| Ensemble | 100% | 🌟 Outstanding |
🛠️ Tech Stack
| Category | Tools |
|---|---|
| Language | |
| ML Framework | |
| Data Processing | |
| Visualization |
Metadata
Release files for bcpipeline 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| bcpipeline-0.1.0.tar.gz | 3.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| bcpipeline-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 7.7 kB
Release files / bcpipeline-0.1.0.tar.gz
| Download URL | bcpipeline-0.1.0.tar.gz |
|---|---|
| Size | 3.7 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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Release files / bcpipeline-0.1.0-py3-none-any.whl
| Download URL | bcpipeline-0.1.0-py3-none-any.whl |
|---|---|
| Size | 4.0 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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twine/7.0.0 CPython/3.13.16
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