# MLTrackFlow 🚀
[](https://pypi.org/project/mltrackflow/)
[](https://pypi.org/project/mltrackflow/)
[](https://opensource.org/licenses/MIT)
[](https://pypi.org/project/mltrackflow/)
**A User-Friendly Python Library for Making Machine Learning Training Processes Transparent and Traceable**
MLTrackFlow is a **beginner-friendly** Python package that enables you to track, record, and visualize your ML model development process step by step.
---
## 🌟 Why MLTrackFlow?
### 🎯 Get Started in One Line
```python
from mltrackflow import ExperimentTracker
tracker = ExperimentTracker(experiment_name="my_project")
with tracker.start_run("first_experiment"):
tracker.log_model_metrics(model, X_test, y_test) # Automatic!
```
### ✨ Key Features
- **🎓 Perfect for Beginners**: Simple API, automatic logging, plenty of examples
- **📊 Automatic Metric Tracking**: Accuracy, precision, recall, F1 calculated automatically
- **🔄 Pipeline Management**: Organize all steps from data preparation to model
- **📈 Rich Visualization**: Confusion matrix, learning curves, feature importance
- **🏆 Model Comparison**: Easily compare different models
- **📄 HTML Reports**: Professional reports with one click
- **💾 Model Versioning**: Organize all your models
- **🔒 Data Tracking**: Track changes with automatic data hashing
## 🚀 Quick Start
### Installation
```bash
pip install mltrackflow
```
### Your First Experiment (60 seconds!)
```python
from mltrackflow import ExperimentTracker
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
# Prepare data
data = load_iris()
X_train, X_test, y_train, y_test = train_test_split(
data.data, data.target, test_size=0.2, random_state=42
)
# Start tracker
tracker = ExperimentTracker(experiment_name="iris_demo")
# Train and log
with tracker.start_run("random_forest"):
# Log parameters
tracker.log_params({"n_estimators": 100, "max_depth": 5})
# Train model
model = RandomForestClassifier(n_estimators=100, max_depth=5)
model.fit(X_train, y_train)
# Automatically calculate and log metrics
tracker.log_model_metrics(model, X_test, y_test)
# Save model
tracker.save_model(model, "my_model")
# Generate HTML report
tracker.generate_report()
print("✅ Report created: experiments/iris_demo/experiment_report.html")
```
**Output:**
```
🚀 Run started: random_forest
📊 Metric logged: accuracy = 0.9667
📊 Metric logged: precision = 0.9722
📊 Metric logged: recall = 0.9667
📊 Metric logged: f1_score = 0.9667
✅ Run completed: random_forest
```
## 📚 Feature Details
### 1️⃣ Modular Workflow with Pipeline
```python
from mltrackflow import MLPipeline, PipelineStep
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
# Create pipeline
pipeline = MLPipeline(name="data_pipeline", tracker=tracker)
# Add steps
pipeline.add_step(PipelineStep(name="scaler", transformer=StandardScaler()))
pipeline.add_step(PipelineStep(name="pca", transformer=PCA(n_components=2)))
pipeline.add_step(PipelineStep(name="model", model=RandomForestClassifier()))
# Train
with tracker.start_run("pipeline_demo"):
pipeline.fit(X_train, y_train)
predictions = pipeline.predict(X_test)
# Visualize
pipeline.visualize_steps(output_path="pipeline.png")
```
### 2️⃣ Model Comparison
```python
from mltrackflow import ModelComparator
# Try different models
models = {
"rf": RandomForestClassifier(n_estimators=100),
"svm": SVC(kernel='rbf'),
"logistic": LogisticRegression(),
}
for name, model in models.items():
with tracker.start_run(name):
model.fit(X_train, y_train)
tracker.log_model_metrics(model, X_test, y_test)
# Compare
comparator = ModelComparator(tracker=tracker)
comparator.compare_runs()
comparator.print_comparison_table()
# Find best
best = comparator.get_best_model(metric="accuracy", maximize=True)
print(f"🏆 Best model: {best}")
```
### 3️⃣ Visualization
```python
from mltrackflow import Visualizer
viz = Visualizer(tracker=tracker)
# Confusion matrix
viz.plot_confusion_matrix(y_test, predictions)
# Feature importance
viz.plot_feature_importance(model, feature_names)
# Model comparison
viz.plot_metrics_comparison(
run_names=["rf", "svm", "logistic"],
metrics=["accuracy", "f1_score"]
)
```
## 🆚 Comparison with Other Tools
| Feature | MLflow | W&B | **MLTrackFlow** |
|---------|--------|-----|-----------------|
| Installation | Complex | Registration required | `pip install` ✅ |
| Learning Curve | Medium | Medium | **Easy** 🎓 |
| Local Execution | ✅ | Limited | **✅** |
| Pipeline Support | ❌ | ❌ | **✅** |
| Auto Metrics | Limited | Limited | **Fully Automatic** 🤖 |
| Beginner Friendly | ⚠️ | ⚠️ | **✅** |
## 💡 Command Line Usage
```bash
# Start new experiment
mltrackflow init --name my_experiment
# List experiments
mltrackflow list
# Generate report
mltrackflow report --experiment iris_demo
# Compare models
mltrackflow compare --experiment iris_demo --runs rf svm logistic
```
## 📖 Documentation
- [Quick Start Guide](https://github.com/yourusername/mltrackflow/blob/main/QUICKSTART.md)
- [API Reference](https://github.com/yourusername/mltrackflow/tree/main/mltrackflow)
- [Example Projects](https://github.com/yourusername/mltrackflow/tree/main/examples)
## 🤝 Contributing
Contributions are welcome! Please see [CONTRIBUTING.md](https://github.com/yourusername/mltrackflow/blob/main/CONTRIBUTING.md).
## 📄 License
This project is licensed under the MIT License - see [LICENSE](LICENSE) file for details.
---
# 🇹🇷 Türkçe Açıklama
**Makine Öğrenimi Eğitim Süreçlerini Şeffaf ve İzlenebilir Hale Getiren Kullanıcı Dostu Python Kütüphanesi**
MLTrackFlow, ML model geliştirme sürecinizi adım adım izlemenize, kayıt altına almanıza ve görselleştirmenize olanak tanıyan **yeni başlayanlar için ideal** bir Python paketidir.
## 🌟 Neden MLTrackFlow?
- **🎓 Yeni Başlayanlar İçin**: Basit API, otomatik loglama
- **📊 Otomatik Metrik Takibi**: Tüm metrikler otomatik hesaplanır
- **🔄 Pipeline Yönetimi**: Veri hazırlıktan modele kadar tüm adımları organize edin
- **📈 Zengin Görselleştirme**: Karmaşıklık matrisi, öğrenme eğrileri
- **🏆 Model Karşılaştırma**: Farklı modelleri kolayca kıyaslayın
- **📄 HTML Raporları**: Tek tıkla profesyonel raporlar
## 🚀 Kurulum ve Kullanım
```bash
pip install mltrackflow
```
```python
from mltrackflow import ExperimentTracker
from sklearn.ensemble import RandomForestClassifier
# Tracker başlat
tracker = ExperimentTracker(experiment_name="proje_adi")
# Model eğit ve kaydet
with tracker.start_run("deneme_1"):
model = RandomForestClassifier()
model.fit(X_train, y_train)
# Metrikleri otomatik kaydet
tracker.log_model_metrics(model, X_test, y_test)
# Modeli kaydet
tracker.save_model(model, "modelim")
# HTML rapor oluştur
tracker.generate_report()
```
## 📚 Özellikler
### Pipeline ile Modüler İş Akışı
```python
from mltrackflow import MLPipeline, PipelineStep
from sklearn.preprocessing import StandardScaler
pipeline = MLPipeline(name="veri_pipeline")
pipeline.add_step(PipelineStep(name="olcekleme", transformer=StandardScaler()))
pipeline.add_step(PipelineStep(name="model", model=RandomForestClassifier()))
pipeline.fit(X_train, y_train)
pipeline.visualize_steps() # Pipeline'ı görselleştir
```
### Model Karşılaştırma
```python
from mltrackflow import ModelComparator
# Farklı modelleri dene
for model_name, model in models.items():
with tracker.start_run(model_name):
model.fit(X_train, y_train)
tracker.log_model_metrics(model, X_test, y_test)
# Karşılaştır
comparator = ModelComparator(tracker=tracker)
comparator.compare_runs()
best = comparator.get_best_model(metric="accuracy")
```
### Görselleştirme
```python
from mltrackflow import Visualizer
viz = Visualizer(tracker=tracker)
viz.plot_confusion_matrix(y_test, predictions)
viz.plot_feature_importance(model, feature_names)
viz.plot_metrics_comparison(["model1", "model2"])
```
## 💡 Komut Satırı
```bash
# Yeni deney başlat
mltrackflow init --name proje_adi
# Deneyleri listele
mltrackflow list
# Rapor oluştur
mltrackflow report --experiment proje_adi
```
## 🎯 Ne Görürsünüz?
Her deneyde:
- ✅ Otomatik parametre ve metrik kaydı
- ✅ Zaman damgası
- ✅ Karşılaştırma tablosu
- ✅ HTML rapor (grafiklerle)
- ✅ En iyi model otomatik seçimi
## 📖 Dokümantasyon
- [Hızlı Başlangıç Rehberi (Türkçe)](https://github.com/yourusername/mltrackflow/blob/main/QUICKSTART.md)
- [Örnek Projeler](https://github.com/yourusername/mltrackflow/tree/main/examples)
- [API Dökümanı](https://github.com/yourusername/mltrackflow/tree/main/mltrackflow)
## 🤝 Katkıda Bulunma
Katkılarınızı bekliyoruz! [CONTRIBUTING.md](https://github.com/yourusername/mltrackflow/blob/main/CONTRIBUTING.md) dosyasına bakın.
## 📄 Lisans
Bu proje MIT lisansı altında lisanslanmıştır.
---
**Quick Links:**
[GitHub](https://github.com/yourusername/mltrackflow) •
[PyPI](https://pypi.org/project/mltrackflow/) •
[Examples](https://github.com/yourusername/mltrackflow/tree/main/examples) •
[Issues](https://github.com/yourusername/mltrackflow/issues)
**Don't forget to star! ⭐ / Yıldız vermeyi unutmayın! ⭐**
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"keywords": "machine learning, experiment tracking, mlops, data science",
"author": "Your Name",
"author_email": "Your Name <your.email@example.com>",
"download_url": "https://files.pythonhosted.org/packages/b7/d4/878072e62de8481a747111f3877da668a16bc843548158cff2fa6b2a54c7/mltrackflow-0.1.2.tar.gz",
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"description": "# MLTrackFlow \ud83d\ude80\r\n\r\n[](https://pypi.org/project/mltrackflow/)\r\n[](https://pypi.org/project/mltrackflow/)\r\n[](https://opensource.org/licenses/MIT)\r\n[](https://pypi.org/project/mltrackflow/)\r\n\r\n**A User-Friendly Python Library for Making Machine Learning Training Processes Transparent and Traceable**\r\n\r\nMLTrackFlow is a **beginner-friendly** Python package that enables you to track, record, and visualize your ML model development process step by step.\r\n\r\n---\r\n\r\n## \ud83c\udf1f Why MLTrackFlow?\r\n\r\n### \ud83c\udfaf Get Started in One Line\r\n```python\r\nfrom mltrackflow import ExperimentTracker\r\n\r\ntracker = ExperimentTracker(experiment_name=\"my_project\")\r\nwith tracker.start_run(\"first_experiment\"):\r\n tracker.log_model_metrics(model, X_test, y_test) # Automatic!\r\n```\r\n\r\n### \u2728 Key Features\r\n\r\n- **\ud83c\udf93 Perfect for Beginners**: Simple API, automatic logging, plenty of examples\r\n- **\ud83d\udcca Automatic Metric Tracking**: Accuracy, precision, recall, F1 calculated automatically\r\n- **\ud83d\udd04 Pipeline Management**: Organize all steps from data preparation to model\r\n- **\ud83d\udcc8 Rich Visualization**: Confusion matrix, learning curves, feature importance\r\n- **\ud83c\udfc6 Model Comparison**: Easily compare different models\r\n- **\ud83d\udcc4 HTML Reports**: Professional reports with one click\r\n- **\ud83d\udcbe Model Versioning**: Organize all your models\r\n- **\ud83d\udd12 Data Tracking**: Track changes with automatic data hashing\r\n\r\n## \ud83d\ude80 Quick Start\r\n\r\n### Installation\r\n\r\n```bash\r\npip install mltrackflow\r\n```\r\n\r\n### Your First Experiment (60 seconds!)\r\n\r\n```python\r\nfrom mltrackflow import ExperimentTracker\r\nfrom sklearn.datasets import load_iris\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.ensemble import RandomForestClassifier\r\n\r\n# Prepare data\r\ndata = load_iris()\r\nX_train, X_test, y_train, y_test = train_test_split(\r\n data.data, data.target, test_size=0.2, random_state=42\r\n)\r\n\r\n# Start tracker\r\ntracker = ExperimentTracker(experiment_name=\"iris_demo\")\r\n\r\n# Train and log\r\nwith tracker.start_run(\"random_forest\"):\r\n # Log parameters\r\n tracker.log_params({\"n_estimators\": 100, \"max_depth\": 5})\r\n \r\n # Train model\r\n model = RandomForestClassifier(n_estimators=100, max_depth=5)\r\n model.fit(X_train, y_train)\r\n \r\n # Automatically calculate and log metrics\r\n tracker.log_model_metrics(model, X_test, y_test)\r\n \r\n # Save model\r\n tracker.save_model(model, \"my_model\")\r\n\r\n# Generate HTML report\r\ntracker.generate_report()\r\nprint(\"\u2705 Report created: experiments/iris_demo/experiment_report.html\")\r\n```\r\n\r\n**Output:**\r\n```\r\n\ud83d\ude80 Run started: random_forest\r\n\ud83d\udcca Metric logged: accuracy = 0.9667\r\n\ud83d\udcca Metric logged: precision = 0.9722\r\n\ud83d\udcca Metric logged: recall = 0.9667\r\n\ud83d\udcca Metric logged: f1_score = 0.9667\r\n\u2705 Run completed: random_forest\r\n```\r\n\r\n## \ud83d\udcda Feature Details\r\n\r\n### 1\ufe0f\u20e3 Modular Workflow with Pipeline\r\n\r\n```python\r\nfrom mltrackflow import MLPipeline, PipelineStep\r\nfrom sklearn.preprocessing import StandardScaler\r\nfrom sklearn.decomposition import PCA\r\n\r\n# Create pipeline\r\npipeline = MLPipeline(name=\"data_pipeline\", tracker=tracker)\r\n\r\n# Add steps\r\npipeline.add_step(PipelineStep(name=\"scaler\", transformer=StandardScaler()))\r\npipeline.add_step(PipelineStep(name=\"pca\", transformer=PCA(n_components=2)))\r\npipeline.add_step(PipelineStep(name=\"model\", model=RandomForestClassifier()))\r\n\r\n# Train\r\nwith tracker.start_run(\"pipeline_demo\"):\r\n pipeline.fit(X_train, y_train)\r\n predictions = pipeline.predict(X_test)\r\n\r\n# Visualize\r\npipeline.visualize_steps(output_path=\"pipeline.png\")\r\n```\r\n\r\n### 2\ufe0f\u20e3 Model Comparison\r\n\r\n```python\r\nfrom mltrackflow import ModelComparator\r\n\r\n# Try different models\r\nmodels = {\r\n \"rf\": RandomForestClassifier(n_estimators=100),\r\n \"svm\": SVC(kernel='rbf'),\r\n \"logistic\": LogisticRegression(),\r\n}\r\n\r\nfor name, model in models.items():\r\n with tracker.start_run(name):\r\n model.fit(X_train, y_train)\r\n tracker.log_model_metrics(model, X_test, y_test)\r\n\r\n# Compare\r\ncomparator = ModelComparator(tracker=tracker)\r\ncomparator.compare_runs()\r\ncomparator.print_comparison_table()\r\n\r\n# Find best\r\nbest = comparator.get_best_model(metric=\"accuracy\", maximize=True)\r\nprint(f\"\ud83c\udfc6 Best model: {best}\")\r\n```\r\n\r\n### 3\ufe0f\u20e3 Visualization\r\n\r\n```python\r\nfrom mltrackflow import Visualizer\r\n\r\nviz = Visualizer(tracker=tracker)\r\n\r\n# Confusion matrix\r\nviz.plot_confusion_matrix(y_test, predictions)\r\n\r\n# Feature importance\r\nviz.plot_feature_importance(model, feature_names)\r\n\r\n# Model comparison\r\nviz.plot_metrics_comparison(\r\n run_names=[\"rf\", \"svm\", \"logistic\"],\r\n metrics=[\"accuracy\", \"f1_score\"]\r\n)\r\n```\r\n\r\n## \ud83c\udd9a Comparison with Other Tools\r\n\r\n| Feature | MLflow | W&B | **MLTrackFlow** |\r\n|---------|--------|-----|-----------------|\r\n| Installation | Complex | Registration required | `pip install` \u2705 |\r\n| Learning Curve | Medium | Medium | **Easy** \ud83c\udf93 |\r\n| Local Execution | \u2705 | Limited | **\u2705** |\r\n| Pipeline Support | \u274c | \u274c | **\u2705** |\r\n| Auto Metrics | Limited | Limited | **Fully Automatic** \ud83e\udd16 |\r\n| Beginner Friendly | \u26a0\ufe0f | \u26a0\ufe0f | **\u2705** |\r\n\r\n## \ud83d\udca1 Command Line Usage\r\n\r\n```bash\r\n# Start new experiment\r\nmltrackflow init --name my_experiment\r\n\r\n# List experiments\r\nmltrackflow list\r\n\r\n# Generate report\r\nmltrackflow report --experiment iris_demo\r\n\r\n# Compare models\r\nmltrackflow compare --experiment iris_demo --runs rf svm logistic\r\n```\r\n\r\n## \ud83d\udcd6 Documentation\r\n\r\n- [Quick Start Guide](https://github.com/yourusername/mltrackflow/blob/main/QUICKSTART.md)\r\n- [API Reference](https://github.com/yourusername/mltrackflow/tree/main/mltrackflow)\r\n- [Example Projects](https://github.com/yourusername/mltrackflow/tree/main/examples)\r\n\r\n## \ud83e\udd1d Contributing\r\n\r\nContributions are welcome! Please see [CONTRIBUTING.md](https://github.com/yourusername/mltrackflow/blob/main/CONTRIBUTING.md).\r\n\r\n## \ud83d\udcc4 License\r\n\r\nThis project is licensed under the MIT License - see [LICENSE](LICENSE) file for details.\r\n\r\n---\r\n\r\n# \ud83c\uddf9\ud83c\uddf7 T\u00fcrk\u00e7e A\u00e7\u0131klama\r\n\r\n**Makine \u00d6\u011frenimi E\u011fitim S\u00fcre\u00e7lerini \u015eeffaf ve \u0130zlenebilir Hale Getiren Kullan\u0131c\u0131 Dostu Python K\u00fct\u00fcphanesi**\r\n\r\nMLTrackFlow, ML model geli\u015ftirme s\u00fcrecinizi ad\u0131m ad\u0131m izlemenize, kay\u0131t alt\u0131na alman\u0131za ve g\u00f6rselle\u015ftirmenize olanak tan\u0131yan **yeni ba\u015flayanlar i\u00e7in ideal** bir Python paketidir.\r\n\r\n## \ud83c\udf1f Neden MLTrackFlow?\r\n\r\n- **\ud83c\udf93 Yeni Ba\u015flayanlar \u0130\u00e7in**: Basit API, otomatik loglama\r\n- **\ud83d\udcca Otomatik Metrik Takibi**: T\u00fcm metrikler otomatik hesaplan\u0131r\r\n- **\ud83d\udd04 Pipeline Y\u00f6netimi**: Veri haz\u0131rl\u0131ktan modele kadar t\u00fcm ad\u0131mlar\u0131 organize edin\r\n- **\ud83d\udcc8 Zengin G\u00f6rselle\u015ftirme**: Karma\u015f\u0131kl\u0131k matrisi, \u00f6\u011frenme e\u011frileri\r\n- **\ud83c\udfc6 Model Kar\u015f\u0131la\u015ft\u0131rma**: Farkl\u0131 modelleri kolayca k\u0131yaslay\u0131n\r\n- **\ud83d\udcc4 HTML Raporlar\u0131**: Tek t\u0131kla profesyonel raporlar\r\n\r\n## \ud83d\ude80 Kurulum ve Kullan\u0131m\r\n\r\n```bash\r\npip install mltrackflow\r\n```\r\n\r\n```python\r\nfrom mltrackflow import ExperimentTracker\r\nfrom sklearn.ensemble import RandomForestClassifier\r\n\r\n# Tracker ba\u015flat\r\ntracker = ExperimentTracker(experiment_name=\"proje_adi\")\r\n\r\n# Model e\u011fit ve kaydet\r\nwith tracker.start_run(\"deneme_1\"):\r\n model = RandomForestClassifier()\r\n model.fit(X_train, y_train)\r\n \r\n # Metrikleri otomatik kaydet\r\n tracker.log_model_metrics(model, X_test, y_test)\r\n \r\n # Modeli kaydet\r\n tracker.save_model(model, \"modelim\")\r\n\r\n# HTML rapor olu\u015ftur\r\ntracker.generate_report()\r\n```\r\n\r\n## \ud83d\udcda \u00d6zellikler\r\n\r\n### Pipeline ile Mod\u00fcler \u0130\u015f Ak\u0131\u015f\u0131\r\n\r\n```python\r\nfrom mltrackflow import MLPipeline, PipelineStep\r\nfrom sklearn.preprocessing import StandardScaler\r\n\r\npipeline = MLPipeline(name=\"veri_pipeline\")\r\npipeline.add_step(PipelineStep(name=\"olcekleme\", transformer=StandardScaler()))\r\npipeline.add_step(PipelineStep(name=\"model\", model=RandomForestClassifier()))\r\n\r\npipeline.fit(X_train, y_train)\r\npipeline.visualize_steps() # Pipeline'\u0131 g\u00f6rselle\u015ftir\r\n```\r\n\r\n### Model Kar\u015f\u0131la\u015ft\u0131rma\r\n\r\n```python\r\nfrom mltrackflow import ModelComparator\r\n\r\n# Farkl\u0131 modelleri dene\r\nfor model_name, model in models.items():\r\n with tracker.start_run(model_name):\r\n model.fit(X_train, y_train)\r\n tracker.log_model_metrics(model, X_test, y_test)\r\n\r\n# Kar\u015f\u0131la\u015ft\u0131r\r\ncomparator = ModelComparator(tracker=tracker)\r\ncomparator.compare_runs()\r\nbest = comparator.get_best_model(metric=\"accuracy\")\r\n```\r\n\r\n### G\u00f6rselle\u015ftirme\r\n\r\n```python\r\nfrom mltrackflow import Visualizer\r\n\r\nviz = Visualizer(tracker=tracker)\r\nviz.plot_confusion_matrix(y_test, predictions)\r\nviz.plot_feature_importance(model, feature_names)\r\nviz.plot_metrics_comparison([\"model1\", \"model2\"])\r\n```\r\n\r\n## \ud83d\udca1 Komut Sat\u0131r\u0131\r\n\r\n```bash\r\n# Yeni deney ba\u015flat\r\nmltrackflow init --name proje_adi\r\n\r\n# Deneyleri listele\r\nmltrackflow list\r\n\r\n# Rapor olu\u015ftur\r\nmltrackflow report --experiment proje_adi\r\n```\r\n\r\n## \ud83c\udfaf Ne G\u00f6r\u00fcrs\u00fcn\u00fcz?\r\n\r\nHer deneyde:\r\n- \u2705 Otomatik parametre ve metrik kayd\u0131\r\n- \u2705 Zaman damgas\u0131\r\n- \u2705 Kar\u015f\u0131la\u015ft\u0131rma tablosu\r\n- \u2705 HTML rapor (grafiklerle)\r\n- \u2705 En iyi model otomatik se\u00e7imi\r\n\r\n## \ud83d\udcd6 Dok\u00fcmantasyon\r\n\r\n- [H\u0131zl\u0131 Ba\u015flang\u0131\u00e7 Rehberi (T\u00fcrk\u00e7e)](https://github.com/yourusername/mltrackflow/blob/main/QUICKSTART.md)\r\n- [\u00d6rnek Projeler](https://github.com/yourusername/mltrackflow/tree/main/examples)\r\n- [API D\u00f6k\u00fcman\u0131](https://github.com/yourusername/mltrackflow/tree/main/mltrackflow)\r\n\r\n## \ud83e\udd1d Katk\u0131da Bulunma\r\n\r\nKatk\u0131lar\u0131n\u0131z\u0131 bekliyoruz! [CONTRIBUTING.md](https://github.com/yourusername/mltrackflow/blob/main/CONTRIBUTING.md) dosyas\u0131na bak\u0131n.\r\n\r\n## \ud83d\udcc4 Lisans\r\n\r\nBu proje MIT lisans\u0131 alt\u0131nda lisanslanm\u0131\u015ft\u0131r.\r\n\r\n---\r\n\r\n**Quick Links:**\r\n[GitHub](https://github.com/yourusername/mltrackflow) \u2022 \r\n[PyPI](https://pypi.org/project/mltrackflow/) \u2022 \r\n[Examples](https://github.com/yourusername/mltrackflow/tree/main/examples) \u2022 \r\n[Issues](https://github.com/yourusername/mltrackflow/issues)\r\n\r\n**Don't forget to star! \u2b50 / Y\u0131ld\u0131z vermeyi unutmay\u0131n! \u2b50**\r\n",
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