Create README.md

README.md

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+# ✈️ Jet Engine Predictive Maintenance System
+### B.Tech AI & Data Science - Special Project
+
+This project implements a **Predictive Maintenance (PdM)** solution for industrial jet engines. Using the **NASA C-MAPSS dataset**, the system predicts the **Remaining Useful Life (RUL)** of an engine based on multivariate time-series sensor data.
+
 ---
-title: ProactiveMachine
-emoji: 👁
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 6.5.1
-app_file: app.py
-pinned: false
-short_description: '"Real-Time Predictive Maintenance System using XGBoost and T'
+
+## 🚀 Live Demo
+You can access the live interactive dashboard here: 
+**[INSERT YOUR HUGGING FACE SPACE LINK HERE]**
+
+## 📌 Project Overview
+In industrial settings, equipment failure leads to high costs and safety risks. This project transitions from "Reactive Maintenance" to "Predictive Maintenance" by using machine learning to forecast failures before they occur.
+
+### Key Features:
+* **RUL Prediction:** Estimates the number of operational cycles left before failure.
+* **Interactive Dashboard:** Built with Streamlit for real-time sensor input.
+* **Visual Analytics:** Uses Gauge charts and status alerts (Healthy, Caution, Critical).
+* **AI Model:** Powered by **XGBoost Regression**, optimized for time-series tabular data.
+
+---
+
+## 📊 Dataset Information
+The model is trained on the **NASA Turbofan Engine Degradation Simulation Dataset (C-MAPSS)**.
+* **Units:** 100 engines.
+* **Sensors:** 21 different sensors (Temperature, Pressure, Fan Speed, etc.).
+* **Goal:** Predict the "cycles" remaining until the engine reaches a failure state.
+
+
+
+---
+
+## 🛠️ Technical Stack
+* **Language:** Python 3.10+
+* **ML Framework:** XGBoost
+* **Data Processing:** Pandas, NumPy
+* **UI/Deployment:** Streamlit, Hugging Face Spaces
+* **Visualization:** Plotly, Seaborn
+
+---
+
+## 📁 Repository Structure
+* `app.py`: The main Streamlit application code.
+* `train_local.py`: Script used to train the model and generate the `.pkl` file.
+* `engine_model.pkl`: The trained XGBoost model (Serialized).
+* `requirements.txt`: List of Python libraries required for the environment.
+
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## ⚙️ How to Run Locally
+1. **Clone the repository:**
+   ```bash
+   git clone [YOUR_REPO_URL]
+   cd [REPO_NAME]