🚀 Project: End-to-End ML Pipeline with DVC & MLflow 🔁📊

Welcome to an exciting project that demonstrates how to build a production-grade Machine Learning pipeline from scratch using DVC 🗓️ for data version control and MLflow ⚙️ for experiment tracking.

🎯 Objective: Train a robust Random Forest Classifier 🌲 on the Pima Indians Diabetes Dataset 🧬, with a modular and reproducible ML pipeline including:

• 🔍 Data Preprocessing
• 🧠 Model Training
• 📈 Model Evaluation

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🔑 Key Highlights

📦 Data Versioning with DVC

With DVC, you can:

• 🧬 Track datasets, models, and code changes
• ⚙️ Structure workflows into stages (preprocess ➡️ train ➡️ evaluate)
• 🔁 Automatically re-run affected stages when changes occur
• ☁️ Connect to remote data storage (DagsHub/S3) for collaboration

🛠️ Your pipeline becomes:

• ✅ Modular
• ✅ Reproducible
• ✅ Scalable

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📊 Experiment Tracking with MLflow

MLflow allows:

• 🧪 Tracking experiments: log parameters, metrics, models
• 🧮 Comparing runs visually
• 📏 Optimizing hyperparameters (n_estimators, max_depth, etc.)
• 📦 Storing & reusing model artifacts

🔍 “What gets measured gets improved.” — With MLflow, you measure everything.

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📁 Dataset Used

• Pima Indians Diabetes Dataset 📊 Medical data for binary classification ✅ Balanced features ✅ Real-world healthcare relevance

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🤖 Model Used

• 🌲 Random Forest Classifier ✅ Robust ✅ Handles missing values well ✅ Performs well on tabular data

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📈 Final Output

At the end of this project, you will have:

• 🎯 A complete ML pipeline versioned with DVC
• ⚙️ Multiple model experiments tracked via MLflow
• ☁️ Integrated remote storage (DagsHub)
• 🔁 Reproducible and scalable pipeline stages

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🔥 Tech Stack

• 🐍 Python
• 📦 DVC
• ⚙️ MLflow
• ☁️ DagsHub
• 📊 Scikit-learn
• 🧪 Pandas, NumPy, Matplotlib

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⭐ Want to Contribute?

Feel free to fork, ⭐ star, or raise issues! Together, let’s build smarter pipelines 🔁💡

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✅ Built with ❤️ by Anand – Follow for more end-to-end ML & MLOps content! 🔗 View Project on DagsHub