| import streamlit as st | |
| import pandas as pd | |
| import joblib | |
| st.header('FTDS Model Deployment') | |
| st.write(""" | |
| Created by FTDS Curriculum Team | |
| Use the sidebar to select input features. | |
| """) | |
| df = pd.read_csv('https://raw.githubusercontent.com/ardhiraka/PFDS_sources/master/campus.csv') | |
| gender = st.selectbox('Gender', df['gender'].unique()) | |
| ssc = st.number_input('Secondary School Points', value=67.00) | |
| hsc = st.number_input('High School Points', 0.0, value=91.0) | |
| hsc_s = st.selectbox('High School Spec', df['hsc_s'].unique()) | |
| degree_p = st.number_input('Degree Points', 0.0, value=58.0) | |
| degree_t = st.selectbox('Degree Spec', df['degree_t'].unique()) | |
| workex = st.selectbox('Work Experience?', df['workex'].unique()) | |
| etest_p = st.number_input('Etest Points', 0.0, value=78.00) | |
| spec = st.selectbox('Specialization', df['specialisation'].unique()) | |
| mba_p = st.number_input('MBA Points', 0.0, value=54.55) | |
| data = { | |
| 'gender': gender, | |
| 'ssc_p': ssc, | |
| 'hsc_p': hsc, | |
| 'hsc_s': hsc_s, | |
| 'degree_p': degree_p, | |
| 'degree_t': degree_t, | |
| 'workex': workex, | |
| 'etest_p': etest_p, | |
| 'specialisation':spec, | |
| 'mba_p': mba_p | |
| } | |
| input = pd.DataFrame(data, index=[0]) | |
| st.subheader('User Input') | |
| st.write(input) | |
| load_model = joblib.load("my_model.pkl") | |
| if st.button('Predict'): | |
| prediction = load_model.predict(input) | |
| if prediction == 1: | |
| prediction = 'Placed' | |
| else: | |
| prediction = 'Not Placed' | |
| st.write('Based on user input, the placement model predicted: ') | |
| st.write(prediction) |