Thitikarn/Student_performance_Prediction

1import joblib2import pandas as pd3import streamlit as st 4 5EDU_DICT = {'Preschool': 1,6            '1st-4th': 2,7            '5th-6th': 3,8            '7th-8th': 4,9            '9th': 5,10            '10th': 6,11            '11th': 7,12            '12th': 8,13            'HS-grad': 9, 14            'Some-college': 10,15            'Assoc-voc': 11,16            'Assoc-acdm': 12,17            'Bachelors': 13,18            'Masters': 14,19            'Prof-school': 15,20            'Doctorate': 1621            }22 23model = joblib.load('model.joblib')24unique_values = joblib.load('unique_values.joblib')25    26unique_class =  unique_values["workclass"]27unique_education =  unique_values["education"]28unique_marital_status =  unique_values["marital.status"]29unique_relationship =  unique_values["relationship"]30unique_occupation =  unique_values["occupation"]31unique_sex =  unique_values["sex"]32unique_race = unique_values["race"]33unique_country =  unique_values["native.country"]34 35def main():36    st.title("Adult Income Analysis")37 38    with st.form("questionaire"):39        age = st.slider("Age", min_value=10, max_value=100)40        workclass = st.selectbox("Workclass", unique_class)41        education = st.selectbox("Education", unique_education)42        Marital_Status = st.selectbox("Marital Status", unique_marital_status)43        occupation = st.selectbox("Occupation", unique_occupation)44        relationship = st.selectbox("Relationship", unique_relationship)45        race = st.selectbox("Race", unique_race)46        sex = st.selectbox("Sex", unique_sex)47        hours_per_week = st.slider("Hours per week", min_value=1, max_value=100)48        native_country = st.selectbox("Country", unique_country)49 50        clicked = st.form_submit_button("Predict income")51        if clicked:52            result=model.predict(pd.DataFrame({"age": [age],53                                               "workclass": [workclass],54                                               "education": [EDU_DICT[education]],55                                               "marital.status": [Marital_Status],56                                               "occupation": [occupation],57                                               "relationship": [relationship],58                                               "race": [race],59                                               "sex": [sex],60                                               "hours.per.week": [hours_per_week],61                                               "native.country": [native_country]}))62            result = '>50K' if result[0] == 1 else '<=50K'63            st.success('The predicted income is {}'.format(result))64 65if __name__=='__main__':66    main()67