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README.md

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+
+# 🔋 Electric Vehicle Performance Prediction Model
+
+## 👨‍🎓 Author
+Student Research Project – Academic Year 2024–2025

app.py

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+import streamlit as st
+import numpy as np
+import joblib
+
+# =========================
+# Load model (and scaler if needed)
+# =========================
+model = joblib.load("ev_model/model.joblib")
+# =========================
+# UI
+# =========================
+st.set_page_config(
+    page_title="EV Performance Prediction",
+    page_icon="🔋",
+    layout="centered"
+)
+
+st.title("🔋 Electric Vehicle Performance Prediction")
+st.write("Predict **Electric Range** of an electric vehicle using a trained ML model.")
+
+st.divider()
+
+# =========================
+# Input features
+# (encoded giống như lúc train)
+# =========================
+year = st.number_input("Model Year", min_value=1990, max_value=2035, value=2020)
+make = st.number_input("Make (encoded)", value=10)
+model_code = st.number_input("Model (encoded)", value=20)
+ev_type = st.number_input("Electric Vehicle Type (encoded)", value=1)
+cafv = st.number_input("CAFV Eligibility (encoded)", value=0)
+utility = st.number_input("Electric Utility (encoded)", value=60)
+
+# =========================
+# Prediction
+# =========================
+if st.button("🔍 Predict"):
+    X = np.array([[year, make, model_code, ev_type, cafv, utility]])
+
+    # Nếu dùng scaler thì bật dòng này
+    # X = scaler.transform(X)
+
+    prediction = model.predict(X)
+
+    st.success(f"🚗 Predicted Electric Range: **{prediction[0]:.2f} km**")

model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2bb47892592702dc88f2ae782402df93cc4f3ae36b83cc42647262756474e63f
+size 70445

requirements.txt

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+scikit-learn
+xgboost
+joblib
+numpy
+streamlit