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app.py

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+
+## importing the necessary libraries
+import streamlit as st
+import joblib
+import numpy as np
+
+## to save the model
+#joblib.dump(mode_name,"the path where you want to save the model")
+
+
+## to load the model 
+
+model = joblib.load("student_performance_model.h5")
+
+
+def predict_marks(Hours_studied,Previous_score , Extracurriculum_activities, sleep_hours , sample_question):
+    #predict the students marks based on the input data
+
+    input_data = np.array([[Hours_studied,Previous_score , Extracurriculum_activities, sleep_hours , sample_question]])
+    prediction = model.predict(input_data)
+    prediction = round(float(prediction),2)
+
+
+    ## ensure the prediction does not exceed 100
+
+    if prediction > 100:
+        prediction = 100
+
+    return prediction
+
+
+def main():
+
+    st.title("Student Performance Prediction")
+
+    ## input data
+
+    name = st.text_input("enter your name")
+
+    Hours_studied = st.number_input("number of hours you studied",min_value = 0.0 ,max_value=18.0,value = 0.0)
+
+    Previous_score = st.number_input("enter your previous score",min_value = 0.0 ,max_value=100.0,value = 0.0)
+
+    Extracurriculum_Activites = st.number_input("no.of  extracurricular activities done",min_value = 0.0,max_value = 10.0 ,value= 0.0 )
+
+    sleep_hours = st.number_input ("Hours you slept", min_value =0.0,max_value = 12.0, value = 0.0)
+
+    sample_questions = st.number_input ("number of sample paper you solved", min_value =0.0,max_value = 20.0, value = 0.0)
+
+    ## sidebar interaction
+
+    st.sidebar.title(f"# hey {name}")
+    st.sidebar.title(f"Welcome to your Marks predictor!...")
+
+
+    ## prediction button
+
+    if st.button("Predict your Marks"):
+        prediction = predict_marks(Hours_studied,Previous_score , Extracurriculum_Activites,sleep_hours ,sample_questions)
+
+
+        ## display the prediction
+
+        if prediction >=90:
+            st.balloons()
+            st.success(f"congrats {name} you are on a track to score  {prediction} marks!.kepp it up ")
+        elif prediction>=35:
+            st.warning(f"hey {name} you are on a track to score {prediction} marks. but there's a room to aim higher!")
+        else:
+            st.error(f"{name} , oh no you might fail the exam as you will be getting {prediction}, work hard and conncentrate on your studies")
+            
+
+if __name__ =="__main__":
+    main()
+
+
+
+
+
+

requirements.txt

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+pandas==2.0.3
+joblib==1.2.0
+numpy==1.24.3
+streamlit==1.37.0
+scikit-learn

student_performance_model.h5

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