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README.md

@@ -1,38 +1,5 @@
----
-title: EV Performance Prediction
-emoji: 🔋
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: "4.44.1"
-app_file: app.py
-pinned: false
----
 
-# 🔋 Electric Vehicle Performance Prediction
+# 🔋 Electric Vehicle Performance Prediction Model
 
-## 📌 Description
-This application predicts the **technical performance (Electric Range)** of electric vehicles  
-using a machine learning regression model.
-
-## 🧠 Model Information
-- Task: Regression
-- Target variable: Electric Range
-- Algorithm: XGBoost Regressor
-- Dataset size: ~73,000 samples
-- Number of features: 6
-
-## 📂 Input Features
-1. Model Year  
-2. Make (encoded)  
-3. Model (encoded)  
-4. Electric Vehicle Type  
-5. CAFV Eligibility  
-6. Electric Utility  
-
-## ▶️ How it works
-The Gradio web interface allows users to input vehicle specifications  
-and returns the predicted electric range in kilometers.
-
-## 🚀 Deployment
-This demo is deployed using **Hugging Face Spaces + Gradio**.
+## 👨‍🎓 Author
+Student Research Project – Academic Year 2024–2025

app.py

@@ -22,7 +22,7 @@ with gr.Blocks(title="EV Performance Prediction") as demo:
     gr.Markdown(
         """
         # 🔋 Electric Vehicle Performance Prediction
-        **Dự báo hiệu suất kỹ thuật xe điện (Electric Range)**  
+        **Dự báo hiệu suất kỹ thuật xe điện (Electric Range)**
         ---
         """
     )