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app.py

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+import streamlit as st
+import pandas as pd 
+import numpy as np
+import joblib
+
+# Load the trained model    
+model = joblib.load('student_performance_model.h5')
+# Define the input features
+def predict_marks(Hours_Studied,Previous_Scores,Extracurricular_Activities,Sleep_Hours,Sample_Question_Papers_Practiced):
+    input_data = np.array([[Hours_Studied,Previous_Scores,Extracurricular_Activities,Sleep_Hours,Sample_Question_Papers_Practiced]])
+    prediction = model.predict(input_data)
+    prediction = round(float(prediction), 2)
+
+    if prediction >= 100:
+        prediction = 100
+    
+    return prediction
+
+# Display the app title
+def main():
+    st.title("Student Performance Prediction")
+    name = st.text_input("Enter your name:--")
+    Hours_Studied = st.number_input("Enter Number of hours you daily study:--",max_value=12,min_value=0,value=0)
+    Previous_Scores = st.number_input("Enter your previous scores:--",max_value=100.0,min_value=0.0,value=0.0)
+    Extracurricular_Activities = st.number_input("Enter the number of extracurricular activities you participate in:--",max_value=10,min_value=0,value=0)
+    Sleep_Hours= st.number_input("Enter the number of hours you sleep daily:--",max_value=12,min_value=0,value=0)
+    Sample_Question_Papers_Practiced= st.number_input("Enter the number of sample question papers you practice:--",max_value=100,min_value=0,value=0)
+
+    st.sidebar.title("Prediction")
+    st.sidebar.write(f"Hey, {name}")
+
+    if st.button("Result"):
+        prediction = predict_marks(Hours_Studied,Previous_Scores,Extracurricular_Activities,Sleep_Hours,Sample_Question_Papers_Practiced)
+        if prediction > 90:
+            st.success(f"Your predicted grade is A you are on a correct path with the estimated score of {prediction}.")
+            st.balloons()
+        elif prediction > 35:
+            st.warning(f"Your predicted grade is B you need to impove your estimated score is {prediction}.")
+        else:
+            st.error(f"Work hard your estimated score is {prediction}.")
+
+if __name__ == "__main__":
+    main()
+

requirements.txt

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+pandas
+numpy
+matplotlib
+seaborn
+streamlit
+scikit-learn
+joblib

student_performance_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4157ea9e74dc017119fd526cc588e579c595ba7ab8ba62f5b7213eec0316d811
+size 1040