first sync with the remote repo

app.py

@@ -0,0 +1,80 @@
+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}")

requirements.txt

@@ -0,0 +1,5 @@
+pandas
+numpy
+scikit-learn
+streamlit
+altair

student_performance_data.csv

@@ -0,0 +1,101 @@
+Study Hours,Attendance Rate,Assignment Grades,Final Exam Score
+39,0.86,75,70
+29,0.82,97,75
+15,0.94,98,78
+8,0.74,51,50
+21,0.56,50,50
+39,0.86,97,92
+19,0.88,61,61
+23,0.78,54,52
+11,0.89,86,71
+11,0.75,81,60
+24,0.76,58,62
+36,0.71,90,80
+24,0.51,84,56
+3,0.55,68,52
+22,0.52,97,69
+2,0.82,65,50
+24,0.66,52,54
+30,0.75,69,66
+38,0.95,73,76
+2,0.62,82,56
+21,0.71,73,68
+33,0.88,60,66
+12,0.61,98,70
+22,0.54,57,54
+25,0.64,85,66
+27,0.58,87,66
+28,0.96,89,72
+16,0.9,69,55
+15,0.82,84,66
+3,0.94,97,64
+37,0.9,74,74
+7,0.59,84,52
+21,0.95,74,59
+9,0.77,78,60
+39,0.9,67,71
+18,0.95,95,75
+4,0.66,67,50
+25,0.56,51,50
+14,0.61,84,66
+9,0.71,65,52
+26,0.91,90,79
+2,0.93,85,56
+20,0.5,82,69
+28,0.76,53,67
+7,0.71,82,64
+8,0.61,63,51
+35,0.56,70,62
+14,0.67,97,68
+17,0.97,69,70
+36,0.66,57,57
+4,0.76,56,50
+2,0.85,52,50
+6,0.68,66,50
+4,0.99,82,72
+29,0.98,97,81
+18,0.63,61,50
+26,0.75,71,65
+34,0.65,71,56
+10,0.64,95,66
+36,0.52,79,71
+14,0.8,87,67
+31,0.75,87,81
+15,0.53,94,54
+8,0.64,57,55
+14,0.95,76,62
+23,0.62,76,54
+21,0.57,83,59
+16,0.74,70,67
+18,0.99,79,70
+24,0.62,82,58
+26,0.84,77,62
+25,0.88,96,76
+29,0.62,82,55
+15,0.86,54,50
+1,0.68,97,67
+25,0.82,68,70
+7,0.82,53,50
+9,0.77,84,58
+24,0.55,98,67
+1,0.92,66,66
+8,0.66,93,63
+24,0.59,77,65
+11,0.52,79,51
+17,0.8,78,64
+8,0.84,95,70
+35,0.51,55,55
+35,0.76,84,64
+33,0.61,90,77
+5,0.82,86,58
+39,0.59,73,76
+28,0.85,78,74
+7,0.69,98,63
+9,0.97,95,73
+8,0.57,80,52
+12,0.67,84,57
+34,0.56,82,67
+33,0.96,70,77
+23,0.94,81,69
+24,0.63,72,56
+37,0.83,82,74