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app.py

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+
+
+# app.py
+import streamlit as st
+import pandas as pd
+import pickle
+
+# 1️⃣ App title
+st.title("Customer Status Prediction")
+
+st.write("""
+This web app predicts the **status** of a customer based on their activity and profile information.
+""")
+
+# 2️⃣ Load serialized ML model
+@st.cache_data
+def load_model(model_path):
+    with open(model_path, "rb") as f:
+        model = pickle.load(f)
+    return model
+
+model = load_model("best_model.pkl")  # Replace with your model path
+
+# 3️⃣ Create UI for user input
+st.sidebar.header("Provide Input Features")
+
+# Numeric Inputs
+age = st.sidebar.number_input("Age", min_value=0, max_value=100, value=25)
+website_visits = st.sidebar.number_input("Website Visits", min_value=0, value=5)
+time_spent_on_website = st.sidebar.number_input("Time Spent on Website (minutes)", min_value=0, value=10)
+page_views_per_visit = st.sidebar.number_input("Page Views per Visit", min_value=0, value=3)
+
+# Categorical Inputs (replace options with actual categories)
+current_occupation = st.sidebar.selectbox("Current Occupation", ["Student", "Professional", "Other"])
+first_interaction = st.sidebar.selectbox("First Interaction", ["Email", "Social Media", "Referral", "Other"])
+profile_completed = st.sidebar.selectbox("Profile Completed", ["Yes", "No"])
+last_activity = st.sidebar.selectbox("Last Activity", ["Last week", "Last month", "Older"])
+print_media_type1 = st.sidebar.selectbox("Print Media Type 1", ["Magazine", "Newspaper", "None"])
+print_media_type2 = st.sidebar.selectbox("Print Media Type 2", ["Magazine", "Newspaper", "None"])
+digital_media = st.sidebar.selectbox("Digital Media", ["Email", "Social Media", "Other"])
+educational_channels = st.sidebar.selectbox("Educational Channels", ["Online Course", "Webinar", "None"])
+referral = st.sidebar.selectbox("Referral", ["Friend", "Advertisement", "Other"])
+
+# 4️⃣ Convert user input to DataFrame
+input_dict = {
+    '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
+}
+
+input_df = pd.DataFrame([input_dict])
+
+# 5️⃣ Make prediction
+if st.button("Predict Status"):
+    prediction = model.predict(input_df)
+    prediction_proba = model.predict_proba(input_df)[:, 1]
+
+    st.write(f"**Predicted Status:** {prediction[0]}")
+    st.write(f"**Probability of Positive Status:** {prediction_proba[0]:.2f}")
+

churn_prediction_model_v1_0.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d969348d24e07a223f66fdc507a706e6e8c051b7526625d4dedb6755ceb58307
+size 90101

my_model_v1_0.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d969348d24e07a223f66fdc507a706e6e8c051b7526625d4dedb6755ceb58307
+size 90101

requirements.txt

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-altair
-pandas
-streamlit
+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+joblib==1.4.2
+streamlit==1.43.2