Update README.md

README.md

@@ -1,11 +1,11 @@
 ---
 title: Jet Engine Predictive Maintenance
-emoji: 🚀
+emoji: ✈️
 colorFrom: blue
 colorTo: red
 sdk: streamlit
-sdk_version: 1.54.0
-python_version: 3.1
+sdk_version: 1.31.0
+python_version: "3.10"
 app_file: app.py
 pinned: false
 ---
@@ -13,30 +13,47 @@ pinned: false
 # ✈️ 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.
+This project implements an Industrial AI solution to predict the **Remaining Useful Life (RUL)** of turbofan engines. By analyzing multivariate time-series data from 21 sensors, the system identifies degradation patterns and provides proactive maintenance alerts.
 
 ---
 
 ## 📌 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.
+Traditional maintenance is either **Reactive** (fixing after failure) or **Preventive** (fixing on a schedule). This project uses **Predictive Maintenance**, which uses machine learning to forecast exactly when a part will fail, saving costs and improving safety.
 
 ### 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**.
+* **Real-time RUL Prediction:** Forecasts how many flight cycles remain before an engine requires service.
+* **Interactive Dashboard:** Built with Streamlit, allowing users to input sensor readings manually.
+* **Health Analytics:** Visualizes engine health status through a dynamic Gauge chart (Healthy, Caution, Critical).
+* **Machine Learning Backend:** Uses an **XGBoost Regressor** optimized for time-series tabular data.
 
 ---
 
 ## 🛠️ Technical Stack
 * **Language:** Python 3.10
-* **ML Framework:** XGBoost
-* **UI/Deployment:** Streamlit, Hugging Face Spaces
-* **Visualization:** Plotly
+* **Model:** XGBoost (Extreme Gradient Boosting)
+* **Web Framework:** Streamlit
+* **Libraries:** Pandas, NumPy, Scikit-learn, Joblib, Plotly
+* **Deployment:** Hugging Face Spaces
 
 ---
 
-## ⚙️ How to Run Locally
-1. **Install Dependencies:**
-   ```bash
-   pip install -r requirements.txt
+## 📁 Repository Structure
+| File | Description |
+| :--- | :--- |
+| `app.py` | The main Streamlit web application. |
+| `engine_model.pkl` | The trained AI model (XGBoost). |
+| `requirements.txt` | Lists the Python packages needed to run the app. |
+| `train_local.py` | (Optional) The script used to train the model locally. |
+
+---
+
+## ⚙️ How to Use
+1. **Enter Sensor Readings:** Adjust the sliders in the sidebar with data from your test set.
+2. **Analyze:** Click the "Analyze Engine Health" button.
+3. **Review Results:** The Gauge chart will indicate the remaining life. If the RUL is below 30 cycles, a **Critical Warning** will be triggered.
+
+---
+
+## 👨‍🎓 Author
+**[Your Name]** 3rd Year B.Tech, AI & Data Science  
+[Your University Name]