Update app.py

app.py

@@ -8,6 +8,22 @@ from data_preparation import preprocess_data
 from clustering import perform_clustering, plot_clusters
 from feature_selection import select_features_pca, select_features_rfe, select_features_rf
 from sklearn.preprocessing import StandardScaler
+feature_descriptions = {
+    "CustID": "Unique identifier for each customer.",
+    "FirstPolYear": "Year when the customer first bought an insurance policy.",
+    "BirthYear": "Birth year of the customer, used to calculate age.",
+    "EducDeg": "Highest educational degree obtained by the customer.",
+    "MonthSal": "Monthly salary of the customer. (Numerical, float64)",
+    "GeoLivArea": "Geographical area where the customer lives.",
+    "Children": "Number of children the customer has.",
+    "CustMonVal": "Total monetary value of the customer to the company.",
+    "ClaimsRate": "Rate at which the customer files insurance claims.",
+    "PremMotor": "Premium amount for motor insurance.",
+    "PremHousehold": "Premium amount for household insurance.",
+    "PremHealth": "Premium amount for health insurance.",
+    "PremLife": "Premium amount for life insurance.",
+    "PremWork": "Premium amount for work insurance."
+}
 
 def load_data(dataset_choice):
     if dataset_choice == "Insurance":
@@ -51,6 +67,8 @@ def display_dataset_selection():
     st.write("Number of columns:", data.shape[1])
     st.write("First five rows of the data:")
     st.write(data.head())
+    if dataset_choice=="Insurance":
+        st.write(feature_descriptions)
     return data
 # Function to display Modeling & Evaluation section
 def display_modeling_evaluation():