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app.py

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+import streamlit as st
+import pandas as pd
+import joblib
+import os
+
+# Load the saved model
+model_path = "ADAB_TunedModle.joblib"
+try:
+    model = joblib.load('ADAB_TunedModle.joblib')
+    st.success("Model loaded successfully.")
+except FileNotFoundError:
+    st.error(f"Model file not found at {model_path}. Please check the file path.")
+    st.stop()
+except Exception as e:
+    st.error(f"Error loading model: {str(e)}")
+    st.stop()
+
+# Streamlit app title
+st.title("ExtraLearn Customer Status Prediction")
+
+# Description
+st.write("Enter customer details to predict their status (0 or 1).")
+
+# Input fields for numerical features
+st.header("Numerical Features")
+age = st.number_input("Age", min_value=18, max_value=100, value=30)
+website_visits = st.number_input("Website Visits", min_value=0, value=5)
+time_spent_on_website = st.number_input("Time Spent on Website (seconds)", min_value=0, value=500)
+page_views_per_visit = st.number_input("Page Views per Visit", min_value=0.0, value=2.0)
+
+# Input fields for categorical features
+st.header("Categorical Features")
+current_occupation = st.selectbox("Current Occupation", ["Unemployed", "Professional", "Student"])
+first_interaction = st.selectbox("First Interaction", ["Website", "Mobile App"])
+profile_completed = st.selectbox("Profile Completed", ["Low", "Medium", "High"])
+last_activity = st.selectbox("Last Activity", ["Website Activity", "Email Activity", "Phone Activity"])
+print_media_type1 = st.selectbox("Print Media Type 1", ["Yes", "No"])
+print_media_type2 = st.selectbox("Print Media Type 2", ["Yes", "No"])
+digital_media = st.selectbox("Digital Media", ["Yes", "No"])
+educational_channels = st.selectbox("Educational Channels", ["Yes", "No"])
+referral = st.selectbox("Referral", ["Yes", "No"])
+
+# Prepare input data for prediction
+input_data = pd.DataFrame({
+    "age": [age],
+    "website_visits": [website_visits],
+    "time_spent_on_website": [time_spent_on_website],
+    "page_views_per_visit": [page_views_per_visit],
+    "current_occupation": [current_occupation],
+    "first_interaction": [first_interaction],
+    "profile_completed": [profile_completed],
+    "last_activity": [last_activity],
+    "print_media_type1": [print_media_type1],
+    "print_media_type2": [print_media_type2],
+    "digital_media": [digital_media],
+    "educational_channels": [educational_channels],
+    "referral": [referral]
+})
+
+# Predict button
+if st.button("Predict"):
+    try:
+        prediction = model.predict(input_data)[0]
+        # Since this is a regression model, round the prediction for clarity
+        status = round(prediction)
+        st.success(f"Predicted Status: {status} (Raw prediction value: {prediction:.4f})")
+        if status == 1:
+            st.write("The customer is likely to be in status 1 (e.g., engaged or converted).")
+        else:
+            st.write("The customer is likely to be in status 0 (e.g., not engaged or not converted).")
+    except Exception as e:
+        st.error(f"Error in prediction: {str(e)}")
+
+# Display model details
+
+st.write("Features used: age, website_visits, time_spent_on_website, page_views_per_visit, current_occupation, first_interaction, profile_completed, last_activity, print_media_type1, print_media_type2, digital_media, educational_channels, referral.")