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app.py

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+import numpy as np
+import joblib
+import streamlit as st
+
+#loading the model
+model = joblib.load("performance.h5")
+
+def predict_marks(Hours_Studied,Previous_Scores,Extracurricular_Activities,Sleep_Hours,Sample_Question_Papers_Practiced):
+    "predict the student marks based on the input data"
+    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
+
+def main():
+
+    st.title("Student Performance marks")
+
+    # input data    
+    Hours_Studied = st.number_input("Enter no. of Hours you studied",min_value=0.0,max_value=10.0,value=0.0)
+    Previous_Scores = st.number_input("Enter your previous exam score",min_value=0.0,max_value=100.0,value=0.0)
+    Extracurricular_Activities = st.number_input("Enter your Extra activities",min_value=0.0,max_value=10.0,value=0.0)
+    Sleep_Hours = st.number_input("Enter no. of hours you slept",min_value=0.0,max_value=12.0,value=0.0)
+    Sample_Question_Papers_Practiced = st.number_input("Enter no. of sample questions you practiced",min_value=0.0,max_value=50.0,value=0.0)
+
+    if st.button("Predict your marks"):
+        prediction = predict_marks(Hours_Studied,Previous_Scores,Extracurricular_Activities,Sleep_Hours,Sample_Question_Papers_Practiced)
+
+        #Displat the result
+        if prediction >=90:
+            st.balloons()
+            st.success(f"Congralution you have high chances to pass by {prediction} marks")
+        elif prediction>=35:
+            st.warning(f"you have to work hard you have chances to score with {prediction} marks")
+        else:
+            st.error(f"you have high chances of failing with {prediction} marks")        
+
+if __name__ == "__main__":
+    main()

performance.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0032adf07e82dac858c025329d46bd909d57cffee9b0c356e1ed0872a4e80adb
+size 1040

requirements.txt

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+joblib
+streamlit
+numpy
+scikit-learn