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app.py

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+
+# Hours Studied	Previous Scores	Extracurricular Activities	Sleep Hours	Sample Question Papers Practiced	Performance Index\
+
+import numpy as np
+import joblib
+import streamlit as st
+
+
+
+# Load the trained model
+model = joblib.load(r"C:\Users\asgar\Downloads\machine learning HN\models\student_performance_model.h5")
+
+def predict_marks(Hours_studied,Previous_Score,Extracurriculum_Activivities,Sleep_Hours,Sample_Question):
+    "predict the student marks based on the input data"
+    input_data = np.array([[Hours_studied,Previous_Score,Extracurriculum_Activivities,Sleep_Hours,Sample_Question]])
+    prediction= model.predict(input_data)
+    return round(float(prediction),2)
+
+def main():
+    st.title("Student Marks Predictor")
+
+    #Input data
+    name = st.text_input("Enter your name")
+    Hours_studied = st.number_input("Enter the number of Hours you Studied", min_value=0.0,max_value=20.0,value=0.0)
+    Previous_Score = st.number_input("Enter your Previous exam Score", min_value=0,max_value=100,value=0)
+    Extracurriculum_Activivities = st.number_input("Enter the number extracurriculum activities you have done",min_value=0,max_value=10,value=0)
+    Sleep_Hours = st.number_input("Enter the number of hours you slept",min_value=0.0,max_value=12.0,value=0.0)
+    Sample_Question = st.number_input("Enter the number of Sample Question you have practiced",min_value=0,max_value=50,value=0)
+
+    # predict
+    st.sidebar.write(f"# hi {name}")
+    st.sidebar.write("##### i am a helpful students marks predictor here to assist you in predicting your marks")
+    if st.button("Predict"):
+        prediction = predict_marks(Hours_studied,Previous_Score,Extracurriculum_Activivities,Sleep_Hours,Sample_Question)
+
+        # Display the predictions
+        if prediction >=90:
+            st.success(f"{name} You have a high chances of passing with the the exceptional marks of {prediction} marks keep it up")
+        elif prediction >=35:
+            st.success(f"{name} You have chances of Passing with {prediction} marks try to get 90+")
+        else:
+            st.error(f"{name} You have a very high chances of failing")    
+
+
+
+
+if __name__=="__main__":
+    main()      

requirements.txt

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+joblib==1.2.0
+numpy==1.24.3
+pandas==2.0.3
+streamlit==1.37.0

student_performance_model.h5

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