Add detailed README for ML pipeline contribution

Author: mtandrita

Diabetes Prediction [END 2 END]/diabetes_pipeline/README.md

@@ -0,0 +1,152 @@
+# Diabetes Prediction – Machine Learning Pipeline
+
+> ⚠️ This repository is a **forked project**.  
+> The work below represents my **independent contribution and extension** to the original codebase.
+
+This project implements a complete **end-to-end machine learning pipeline** for predicting diabetes using the Pima Indians Diabetes dataset.  
+The pipeline covers **data preprocessing, model training, evaluation, experimentation, and inference via CLI**.
+
+---
+
+## 📁 Project Structure
+diabetes_pipeline/
+│
+├── dataset/
+│ └── kaggle_diabetes.csv
+│
+├── model/
+│ ├── diabetes_model.pkl
+│ └── scaler.pkl
+│
+├── experiments/
+│ └── experiment_runner.py
+│
+├── data_preprocessing.py
+├── train.py
+├── predict.py
+├── evaluate.py
+└── README.md
+
+---
+
+## 🚀 My Contributions
+
+I independently designed and implemented the following components:
+
+### 1. Data Preprocessing Pipeline
+- Handled missing values in medical features:
+	- `Glucose`, `BloodPressure`, `SkinThickness`, `Insulin`, `BMI`
+- Replaced invalid zeros with `NaN`
+- Applied **mean / median imputation**
+- Standardized features using `StandardScaler`
+- Ensured consistent feature names across training and inference
+
+📄 `data_preprocessing.py`
+
+---
+
+### 2. Model Training
+- Implemented a reproducible training pipeline
+- Trained and persisted:
+	- Random Forest classifier
+	- Feature scaler
+- Stored trained artifacts for reuse and deployment
+
+📄 `train.py`
+
+---
+
+### 3. Model Evaluation
+- Added evaluation logic with:
+	- Accuracy
+	- Precision, Recall, F1-score
+- Verified generalization on the test set
+
+📄 `evaluate.py`
+
+---
+
+### 4. Experimentation Framework
+- Benchmarked multiple ML models:
+	- Logistic Regression
+	- Decision Tree
+	- Random Forest
+	- Support Vector Machine (SVM)
+- Automatically reports accuracy and F1-score
+
+📄 `experiments/experiment_runner.py`
+
+#### Sample Results
+
+| Model                 | Accuracy | F1 Score |
+|----------------------|----------|----------|
+| Logistic Regression  | 0.7875   | 0.6320   |
+| Decision Tree        | 0.9875   | 0.9805   |
+| Random Forest        | 0.9950   | 0.9921   |
+| SVM                  | 0.8450   | 0.7328   |
+
+✔️ **Random Forest performs best on this dataset**
+
+---
+
+### 5. Command-Line Prediction Interface
+- Built a CLI-based inference script
+- Ensures:
+	- Correct feature order
+	- Feature-name alignment with trained scaler
+- Predicts diabetes for a single patient input
+
+📄 `predict.py`
+
+Example:
+```bash
+python predict.py \
+	--pregnancies 2 \
+	--glucose 120 \
+	--bp 70 \
+	--skin 20 \
+	--insulin 80 \
+	--bmi 25 \
+	--dpf 0.5 \
+	--age 35
+
+
+
+---
+
+## 🛠️ Tech Stack
+
+- Python 3.10+
+- pandas
+- numpy
+- scikit-learn
+- joblib
+
+---
+
+## 🧩 Notes
+
+- Project is modular and deployment-ready
+- Structured to support FastAPI / Flask integration
+- Generated files cleaned using `.gitignore`
+- Suitable for internship-level ML engineering evaluation
+
+---
+
+## 👩‍💻 Author Contribution
+
+**Contributor:** Tandrita Mukherjee  
+
+**Contribution Scope:**
+- ML pipeline design
+- Data preprocessing
+- Model training & evaluation
+- Experimentation framework
+- CLI-based inference system
+
+---
+
+## 📌 Disclaimer
+
+This repository is a fork of an existing project.  
+All enhancements, restructuring, and ML pipeline components listed above were implemented independently as part of my learning and internship preparation.