MiauRicc/Machine_Learning_for_Predictive_Modeling

1import streamlit as st2import pandas as pd3import numpy as np4import joblib5 6st.set_page_config(7    page_title='ExtraaLearn — Lead Conversion Predictor',8    page_icon='🎓',9    layout='centered'10)11 12st.title('🎓 ExtraaLearn Lead Conversion Predictor')13st.markdown(14    'Enter the lead details below to predict whether they are likely '15    'to convert to a **paid customer**.'16)17st.divider()18 19@st.cache_resource20def load_model():21    return joblib.load('lead_conversion_model.pkl')22 23model = load_model()24 25col1, col2 = st.columns(2)26 27with col1:28    age = st.slider('Age', min_value=18, max_value=63, value=35)29    current_occupation = st.selectbox(30        'Current Occupation', ['Professional', 'Student', 'Unemployed']31    )32    first_interaction = st.selectbox(33        'First Interaction', ['Website', 'Mobile App']34    )35    profile_completed = st.selectbox(36        'Profile Completed', ['High', 'Medium', 'Low']37    )38    last_activity = st.selectbox(39        'Last Activity', ['Email Activity', 'Phone Activity', 'Website Activity']40    )41 42with col2:43    website_visits = st.number_input(44        'Website Visits', min_value=0, max_value=30, value=345    )46    time_spent_on_website = st.number_input(47        'Time Spent on Website (seconds)', min_value=0, max_value=2537, value=50048    )49    page_views_per_visit = st.number_input(50        'Page Views per Visit', min_value=0.0, max_value=18.5, value=3.0, step=0.151    )52    print_media_type1 = st.selectbox('Saw Newspaper Ad?', ['No', 'Yes'])53    print_media_type2 = st.selectbox('Saw Magazine Ad?', ['No', 'Yes'])54 55col3, col4 = st.columns(2)56with col3:57    digital_media = st.selectbox('Saw Digital Media Ad?', ['No', 'Yes'])58    educational_channels = st.selectbox('Heard via Educational Channels?', ['No', 'Yes'])59with col4:60    referral = st.selectbox('Came via Referral?', ['No', 'Yes'])61 62st.divider()63 64if st.button('🔍 Predict Conversion', use_container_width=True, type='primary'):65    input_data = pd.DataFrame([{66        'age'                  : age,67        'current_occupation'   : current_occupation,68        'first_interaction'    : first_interaction,69        'profile_completed'    : profile_completed,70        'website_visits'       : website_visits,71        'time_spent_on_website': time_spent_on_website,72        'page_views_per_visit' : page_views_per_visit,73        'last_activity'        : last_activity,74        'print_media_type1'    : print_media_type1,75        'print_media_type2'    : print_media_type2,76        'digital_media'        : digital_media,77        'educational_channels' : educational_channels,78        'referral'             : referral,79    }])80 81    prediction = model.predict(input_data)[0]82    proba = model.predict_proba(input_data)[0]83 84    if prediction == 1:85        st.success(86            f'✅ HIGH CONVERSION PROBABILITY — This lead is likely to convert!\n\n'87            f'Confidence: {proba[1]*100:.1f}%'88        )89    else:90        st.warning(91            f'⚠️ LOW CONVERSION PROBABILITY — This lead is unlikely to convert.\n\n'92            f'Confidence: {proba[0]*100:.1f}%'93        )94 95    st.markdown('**Lead Summary:**')96    st.dataframe(input_data, use_container_width=True)97 98st.divider()99st.caption('ExtraaLearn Data Science Project · Powered by Scikit-learn & Streamlit')100