BOJANAPALLY/Student_performance_prediction_using_linear_regression

1import streamlit as st2import pandas as pd3import numpy as np4import pickle5from sklearn.linear_model import LinearRegression6 7st.title("Student Performance Prediction")8 9# Inputs10hours_studied = st.number_input("Enter the number of hours studied")11previous_scores = st.number_input("Enter the previous score")12sleep_hours = st.number_input("Enter the sleep hours")13sample_papers_practiced = st.number_input("Enter number of sample papers practiced")14extracurricular_activities = st.text_input("Enter yes/no for extracurricular activities")15 16# Load model17model = pickle.load(open("lr.pkl", "rb"))18 19if st.button("Predict"):20    input_data = pd.DataFrame({21        "hours_studied": [hours_studied],22        "previous_scores": [previous_scores],23        "sleep_hours": [sleep_hours],24        "sample_papers_practiced": [sample_papers_practiced],25        "extracurricular_activities": [extracurricular_activities]26    })27    result = model.predict(input_data)28    st.success(f"Predictd Score: {result[0]:.2f}")29