README

Author: CPPavithra

README.md

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+# Kubernetes Failure Prediction
+
+## 📌 Project Overview
+This project predicts potential failures in Kubernetes clusters using machine learning. The model is trained to detect issues such as:
+- 🚨 **Node or pod failures**
+- 🖥 **Resource exhaustion** (CPU, memory, disk)
+- 🌐 **Network or connectivity issues**
+- ⚠️ **Service disruptions** based on logs and events
+
+The solution is packaged into a **FastAPI** service and deployed using **Docker** and **Kubernetes**.
+
+---
+
+## 📂 Directory Structure
+```
+📦 k8s-failure-prediction
+├── 📁 data                      # Raw & processed data files
+│   ├── raw_metrics.csv          # Original collected metrics
+│   ├── processed_metrics.csv    # Preprocessed data for training
+│
+├── 📁 models                    # Trained machine learning models
+│   ├── failure_predictor.pkl    # Final trained model
+│
+├── 📁 scripts                   # Model training and evaluation scripts
+│   ├── train_model.py           # Script to train the ML model
+│   ├── evaluate_model.py        # Model evaluation script
+│
+├── 📁 app                       # API service
+│   ├── app.py                   # FastAPI service for predictions
+│   ├── Dockerfile               # Dockerfile for containerization
+│
+├── 📁 deployment                # Kubernetes deployment files
+│   ├── deployment.yaml          # Kubernetes deployment manifest
+│   ├── service.yaml             # Kubernetes service manifest
+│
+├── README.md                    # Documentation
+└── requirements.txt              # Python dependencies
+```
+
+---
+
+## 🚀 Setup & Installation
+
+### 1️⃣ Install Dependencies
+Ensure you have Python 3.8+ installed. Then, install the required libraries:
+```bash
+pip install -r requirements.txt
+```
+
+### 2️⃣ Train the Model
+If needed, retrain the model using:
+```bash
+python scripts/train_model.py
+```
+The trained model will be saved in the `models/` directory.
+
+### 3️⃣ Run the API Locally
+```bash
+uvicorn app:app --host 0.0.0.0 --port 8000
+```
+Test the API using:
+```bash
+curl -X POST "http://localhost:8000/predict" -H "Content-Type: application/json" -d '{"cpu": 80, "memory": 90, "disk": 70}'
+```
+
+---
+
+## 🐳 Dockerization & Kubernetes Deployment
+
+### 🏗️ Build & Run with Docker
+1. **Build the Docker image**
+```bash
+docker build -t pavithra/k8s-failure-predictor:v1 .
+```
+2. **Run the container**
+```bash
+docker run -p 8000:8000 pavithra/k8s-failure-predictor:v1
+```
+3. **Push to Docker Hub**
+```bash
+docker push pavithra/k8s-failure-predictor:v1
+```
+
+### ☸️ Deploy to Kubernetes
+1. **Apply deployment and service manifests**
+```bash
+kubectl apply -f deployment/deployment.yaml
+kubectl apply -f deployment/service.yaml
+```
+2. **Check running pods**
+```bash
+kubectl get pods
+```
+3. **Expose the service**
+```bash
+kubectl port-forward service/k8s-failure-predictor 8000:8000
+```
+4. **Test the API**
+```bash
+curl -X POST "http://localhost:8000/predict" -H "Content-Type: application/json" -d '{"cpu": 85, "memory": 95, "disk": 80}'
+```
+
+---
+
+## 📊 Model Performance
+### ✅ Accuracy Scores:
+- **Train Accuracy:** 86.80%
+- **Test Accuracy:** 68.88%
+
+### 📉 Classification Report:
+| Class | Precision | Recall | F1-Score | Support |
+|-------|-----------|--------|----------|---------|
+| **0** (No Failure) | 0.82 | 0.64 | 0.72 | 904 |
+| **1** (Failure) | 0.56 | 0.77 | 0.65 | 542 |
+
+**Macro Avg:** 69% | **Weighted Avg:** 73%
+
+---
+
+## 📌 Future Improvements
+✅ **Enhance Feature Engineering** – Incorporate more time-series trends 📈
+✅ **Optimize Hyperparameters** – Use Bayesian optimization 🔬
+✅ **Deploy on Cloud** – Host on AWS/GCP/Azure ☁️
+✅ **Improve Model Interpretability** – Use SHAP/LIME 📊
+
+---
+
+## 🤝 Contributing
+Feel free to fork, contribute, and improve the model. PRs are welcome! 🎯
+
+---
+
+## 🏆 Acknowledgments
+Thanks to the open-source community and Kubernetes practitioners for providing valuable datasets and insights!
+