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import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score
import streamlit as st
import altair as alt
# Streamlit app title
st.title("📊 Student Performance Predictor")
# Load dataset
try:
df = pd.read_csv("student_performance_data.csv") # Ensure the file is in the same directory
st.write("### Preview of Dataset")
st.write(df.head()) # Show first few rows
except FileNotFoundError:
st.error("File 'student_performance_data.csv' not found! Please upload the dataset.")
# Ensure dataset contains the required columns
required_columns = ['Study Hours', 'Attendance Rate', 'Assignment Grades', 'Final Exam Score']
if not all(col in df.columns for col in required_columns):
st.error("Dataset must contain the following columns: " + ", ".join(required_columns))
else:
# Prepare data for training
X = df[['Study Hours', 'Attendance Rate', 'Assignment Grades']]
y = df['Final Exam Score']
# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Train a Linear Regression model
model = LinearRegression()
model.fit(X_train, y_train)
# Make predictions
y_pred = model.predict(X_test)
# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
# Create tabs
tab1, tab2, tab3 = st.tabs(["📈 Data Visualization", "📊 Model Performance", "🎯 Prediction"])
# Tab 1: Data Visualization
with tab1:
st.write("### Data Visualization")
# Scatter plots
for col in ['Study Hours', 'Attendance Rate', 'Assignment Grades']:
st.write(f"**{col} vs Final Exam Score**")
chart = alt.Chart(df).mark_circle().encode(
x=col,
y='Final Exam Score',
tooltip=[col, 'Final Exam Score']
).interactive()
st.altair_chart(chart, use_container_width=True)
# Tab 2: Model Performance
with tab2:
st.write("### Model Performance")
st.write(f"✅ Mean Squared Error (MSE): {mse:.2f}")
st.write(f"✅ R-squared Score: {r2:.2f}")
# Tab 3: Prediction
with tab3:
st.write("### Predict Final Exam Score")
study_hours = st.number_input("📚 Study Hours", min_value=0, value=10, step=1)
attendance_rate = st.slider("🎟️ Attendance Rate", min_value=0.0, max_value=1.0, step=0.01, value=0.85)
assignment_grades = st.number_input("📝 Average Assignment Grade", min_value=0, max_value=100, value=80, step=1)
if st.button("🔮 Predict"):
# Prepare input for prediction
input_data = np.array([[study_hours, attendance_rate, assignment_grades]])
# Make prediction
predicted_score = model.predict(input_data)[0]
st.success(f"🎯 Predicted Final Exam Score: {predicted_score:.2f}")
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