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| import joblib | |
| import pandas as pd | |
| import streamlit as st | |
| sex_dict = {'Female': 0, 'Male': 1} | |
| address_dict = {'Urban': 0, 'Rural':1} | |
| famsize_dict = {'Greater than 3': 0, 'Less than or equal to 3': 1} | |
| Pstatus_dict = {'Living together': 0, 'Apart': 1} | |
| Mjob_dict = {'at_home': 0, 'health': 1, 'other': 1, 'services': 1, 'teacher': 1} | |
| Fjob_dict = {'at_home': 0, 'health': 1, 'other': 1, 'services': 1, 'teacher': 1} | |
| schoolsup_dict = {'no': 0, 'yes': 1} | |
| famsup_dict = {'no': 0, 'yes': 1} | |
| activities_dict = {'no': 0, 'yes': 1} | |
| higher_dict = {'no': 0, 'yes': 1} | |
| romantic_dict = {'no': 0, 'yes': 1} | |
| model = joblib.load('model.joblib') | |
| unique_values = joblib.load('unique_values.joblib') | |
| unique_sex = unique_values["sex"] | |
| unique_address = unique_values["address"] | |
| unique_famsize = unique_values["famsize"] | |
| unique_Pstatus = unique_values["Pstatus"] | |
| unique_Mjob = unique_values["Mjob"] | |
| unique_Fjob = unique_values["Fjob"] | |
| unique_schoolsup = unique_values["schoolsup"] | |
| unique_famsup = unique_values["famsup"] | |
| unique_activities = unique_values["activities"] | |
| unique_higher = unique_values["higher"] | |
| unique_romantic = unique_values["romantic"] | |
| def main(): | |
| st.title("Predict Score") | |
| with st.form("questionnaire"): | |
| age = st.slider("student's age", min_value=0, max_value=100) | |
| studytime = st.slider("weekly study time", min_value=0, max_value=10) | |
| famrel = st.slider("quality of family relationships (from 1 - very bad to 5 - excellent)", min_value=1, max_value=5) | |
| goout = st.slider("going out with friends (from 1 - very low to 5 - very high)", min_value=1, max_value=5) | |
| Dalc = st.slider("workday alcohol consumption (from 1 - very low to 5 - very high)", min_value=1, max_value=5) | |
| Walc = st.slider("weekend alcohol consumption (from 1 - very low to 5 - very high)", min_value=1, max_value=5) | |
| health = st.slider("current health status (from 1 - very bad to 5 - very good)", min_value=1, max_value=5) | |
| freetime = st.slider("free time after school (from 1 - very low to 5 - very high)", min_value=1, max_value=5) | |
| absences = st.slider("number of school absences", min_value=0, max_value=100) | |
| sex = st.selectbox("student's sex", unique_sex) | |
| address = st.selectbox("student's home address type", unique_address) | |
| famsize = st.selectbox("family size", unique_famsize) | |
| Pstatus = st.selectbox("parent's cohabitation status", unique_Pstatus) | |
| Mjob = st.selectbox("mother's job", unique_Mjob) | |
| Fjob = st.selectbox("father's job", unique_Fjob) | |
| schoolsup = st.selectbox("extra educational support", unique_schoolsup) | |
| famsup = st.selectbox("family educational support", unique_famsup) | |
| activities = st.selectbox("extra-curricular activities", unique_activities) | |
| higher = st.selectbox("wants to take higher education", unique_higher) | |
| romantic = st.selectbox("with a romantic relationship", unique_romantic) | |
| clicked = st.form_submit_button("Predict Student's Score") | |
| if clicked: | |
| result = model.predict(pd.DataFrame({ | |
| "age": [age], | |
| "studytime": [studytime], | |
| "famrel": [famrel], | |
| "goout": [goout], | |
| "Dalc": [Dalc], | |
| "Walc": [Walc], | |
| "health": [health], | |
| "freetime": [freetime], | |
| "absences": [absences], | |
| "sex": [sex_dict[sex]], | |
| "address": [address_dict[address]], | |
| "famsize": [famsize_dict[famsize]], | |
| "Pstatus": [Pstatus_dict[Pstatus]], | |
| "Mjob": [Mjob_dict[Mjob]], | |
| "Fjob": [Fjob_dict[Fjob]], | |
| "schoolsup": [schoolsup_dict[schoolsup]], | |
| "famsup": [famsup_dict[famsup]], | |
| "activities": [activities_dict[activities]], | |
| "higher": [higher_dict[higher]], | |
| "romantic": [romantic_dict[romantic]] | |
| })) | |
| result = result[0] | |
| st.success('The predicted score is {:.2f}'.format(round(result, 2))) | |
| if __name__=='__main__': | |
| main() | |