swarajaya/user-behavior-analytics

1<<<<<<< HEAD2import pandas as pd3import pickle4import os5from sklearn.linear_model import LogisticRegression6 7data = pd.DataFrame({8    "sessions": [1, 2, 3, 5, 8, 13, 21],9    "pageviews": [5, 10, 15, 30, 50, 80, 130],10    "timeOnSite": [50, 120, 300, 600, 1200, 2000, 3500],11    "converted": [0, 0, 0, 1, 1, 1, 1]12})13 14X = data[["sessions", "pageviews", "timeOnSite"]]15y = data["converted"]16 17model = LogisticRegression()18model.fit(X, y)19 20os.makedirs("model", exist_ok=True)21with open("model/conversion_model.pkl", "wb") as f:22    pickle.dump(model, f)23 24print("✅ Real ML model saved")25 26=======27import streamlit as st
28import pandas as pd
29import numpy as np
30
31from sklearn.linear_model import LogisticRegression
32
33X_demo = np.array([[0,0],[0,1],[1,0],[1,1]])
34y_demo = np.array([0,0,0,1])
35model = LogisticRegression()
36model.fit(X_demo, y_demo)
37
38
39st.title("User Conversion Prediction Demo")
40
41st.write("Enter features to predict conversion:")
42
43feature1 = st.number_input("Feature 1", value=0)
44feature2 = st.number_input("Feature 2", value=0)
45
46if st.button("Predict Conversion"):
47    prediction = model.predict([[feature1, feature2]])
48    st.success(f"Predicted Conversion: {prediction[0]}")
49>>>>>>> 49b75a7536def019dcd82fd3782d3a79dad269a950