Update lapse_model.pkl

lapse_model.pkl

@@ -1,16 +1,25 @@
-from xgboost import XGBClassifier
-from sklearn.model_selection import train_test_split
 import pandas as pd
+import xgboost as xgb
 import pickle
 
-# Example dataset
-df = pd.read_csv("lapse_training_data.csv")
+# Example dummy dataset
+data = {
+    "policy_term": [10, 15, 20],
+    "policy_age": [2, 5, 7],
+    "payment_mode_encoded": [0, 1, 2],
+    "communication_score": [0.3, 0.7, 0.5],
+    "lapse_label": [1, 0, 1]
+}
+df = pd.DataFrame(data)
 
+# Split features and label
 X = df[["policy_term", "policy_age", "payment_mode_encoded", "communication_score"]]
 y = df["lapse_label"]
 
-model = XGBClassifier()
+# Train model
+model = xgb.XGBClassifier()
 model.fit(X, y)
 
-# Save model
-pickle.dump(model, open("lapse_model.pkl", "wb"))
+# Save the model
+with open("lapse_model.pkl", "wb") as f:
+    pickle.dump(model, f)