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| # Import necessary libraries | |
| import streamlit as st | |
| import pandas as pd | |
| import joblib | |
| # Load the model | |
| model = joblib.load('HistGradientBoostingClassifier.joblib') | |
| # Load and preprocess data (example with dummy data) | |
| def load_data(): | |
| data = { | |
| 'job': ['housemaid', 'services', 'services', 'admin.', 'services'], | |
| 'marital': ['married', 'married', 'married', 'married', 'married'], | |
| 'education': ['basic.4y', 'high.school', 'high.school', 'basic.6y', 'high.school'], | |
| 'default': ['no', 'unknown', 'no', 'no', 'no'], | |
| 'housing': ['no', 'no', 'yes', 'no', 'no'], | |
| 'loan': ['no', 'no', 'no', 'no', 'yes'], | |
| 'poutcome': ['nonexistent', 'nonexistent', 'nonexistent', 'nonexistent', 'nonexistent'], | |
| 'y': ['no', 'no', 'no', 'no', 'no'], | |
| 'log_campaign': [0.693147, 0.693147, 0.693147, 0.693147, 0.693147], | |
| 'log_duration': [5.568345, 5.010635, 5.424950, 5.023881, 5.730100], | |
| 'log_age': [4.043051, 4.060443, 3.637586, 3.713572, 4.043051], | |
| 'campaign_duration_interaction': [3.859682, 3.473108, 3.760289, 3.482289, 3.971803], | |
| 'contact_is_cellular': [0, 0, 0, 0, 0], | |
| 'total_loans': [0, 0, 1, 0, 1], | |
| } | |
| return pd.DataFrame(data) | |
| # Streamlit app | |
| st.title('Term Deposit Subscription Predictor') | |
| st.subheader('Enter client details:') | |
| job = st.selectbox('Job', ['admin.', 'blue-collar', 'entrepreneur', 'housemaid', 'management', 'retired', 'self-employed', 'services', 'student', 'technician', 'unemployed', 'unknown']) | |
| marital = st.selectbox('Marital Status', ['single', 'married', 'divorced']) | |
| education = st.selectbox('Education', ['basic.4y', 'basic.6y', 'basic.9y', 'high.school', 'professional.course', 'university.degree', 'unknown']) | |
| default = st.selectbox('Default', ['no', 'yes', 'unknown']) | |
| housing = st.selectbox('Housing', ['no', 'yes', 'unknown']) | |
| loan = st.selectbox('Loan', ['no', 'yes', 'unknown']) | |
| y = st.selectbox('y', ['no', 'yes']) | |
| poutcome = st.selectbox('Poutcome', ['nonexistent', 'failure', 'success']) | |
| log_campaign = st.number_input('Log Campaign', min_value=0.0, value=0.693147) | |
| log_duration = st.number_input('Log Duration', min_value=0.0, value=5.568345) | |
| log_age = st.number_input('Log Age', min_value=0.0, value=4.043051) | |
| campaign_duration_interaction = st.number_input('Campaign Duration Interaction', min_value=0.0, value=3.859682) | |
| contact_is_cellular = st.selectbox('Contact is Cellular', [0, 1]) | |
| total_loans = st.number_input('Total Loans', min_value=0, value=0) | |
| # Prepare input data | |
| input_data = pd.DataFrame({ | |
| 'job': [job], 'marital': [marital], 'education': [education], 'default': [default], 'housing': [housing], 'loan': [loan], | |
| 'poutcome': [poutcome], 'log_campaign': [log_campaign], 'log_duration': [log_duration], 'log_age': [log_age], | |
| 'campaign_duration_interaction': [campaign_duration_interaction], 'contact_is_cellular': [contact_is_cellular], 'total_loans': [total_loans] | |
| }) | |
| # Convert categorical variables to dummy/indicator variables | |
| #input_data = pd.get_dummies(input_data, drop_first=True) | |
| # Add a predict button | |
| if st.button('Predict'): | |
| # Make prediction | |
| prediction = model.predict(input_data)[0] | |
| # Display result | |
| if prediction == 1: | |
| st.success('The client will subscribe to a term deposit.') | |
| else: | |
| st.warning('The client will not subscribe to a term deposit.') | |