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import streamlit as st
import pandas as pd
import numpy as np
import joblib
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
# Load dataset
file_path = "student-por.csv"
data = pd.read_csv(file_path, sep=';')
# Select features and target
features = ["studytime", "absences", "G1", "G2"]
X = data[features]
y = data["G3"]
# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Train the model
model = LinearRegression()
model.fit(X_train, y_train)
# Save model
joblib.dump(model, "student_performance_model.pkl")
# Load model
model = joblib.load("student_performance_model.pkl")
# Streamlit App
st.title("📚 Student Performance Predictor")
st.write("Predict final exam scores based on study time, absences, and previous grades.")
# Input fields
studytime = st.number_input("Study Time (hours per week)", min_value=0, max_value=20, value=5)
absences = st.number_input("Number of Absences", min_value=0, max_value=100, value=2)
G1 = st.number_input("Grade 1", min_value=0, max_value=20, value=10)
G2 = st.number_input("Grade 2", min_value=0, max_value=20, value=10)
# Prediction function
if st.button("Predict Final Grade"):
input_data = np.array([[studytime, absences, G1, G2]])
prediction = model.predict(input_data)[0]
st.success(f"Predicted Final Grade: {round(prediction, 2)}")
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