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import pandas as pd
import numpy as np
def data_imp():
insurance_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."
}
retail_feature_descriptions = {
"Channel": "Indicates the sales channel through which the customer made purchases.",
"Region": "The geographical region where the customer is located.",
"Fresh": "Annual spending (in monetary units) on fresh products.",
"Milk": "Annual spending (in monetary units) on milk products.",
"Grocery": "Annual spending (in monetary units) on grocery items.",
"Frozen": "Annual spending (in monetary units) on frozen products.",
"Detergents_Paper": "Annual spending (in monetary units) on detergents and paper products.",
"Delicassen": "Annual spending (in monetary units) on delicatessen products."
}
bankng_feature_descriptions = {
"CUST_ID": "Unique identifier for each customer.",
"BALANCE": "The average balance left in the customer's account.",
"BALANCE_FREQUENCY": "Frequency with which the balance is updated.",
"PURCHASES": "The total amount of purchases made by the customer.",
"ONEOFF_PURCHASES": "The total amount of one-time purchases made by the customer.",
"INSTALLMENTS_PURCHASES": "The total amount of purchases made in installments.",
"CASH_ADVANCE": "The total amount of cash advances taken by the customer.",
"PURCHASES_FREQUENCY": "The frequency of purchases made by the customer.",
"ONEOFF_PURCHASES_FREQUENCY": "The frequency of one-time purchases made by the customer.",
"PURCHASES_INSTALLMENTS_FREQUENCY": "The frequency of purchases made in installments.",
"CASH_ADVANCE_FREQUENCY": "The frequency of cash advances taken by the customer.",
"CASH_ADVANCE_TRX": "The number of cash advance transactions made by the customer.",
"PURCHASES_TRX": "The number of purchase transactions made by the customer.",
"CREDIT_LIMIT": "The credit limit assigned to the customer's account.",
"PAYMENTS": "The total amount of payments made by the customer.",
"MINIMUM_PAYMENTS": "The minimum amount of payments made by the customer.",
"PRC_FULL_PAYMENT": "The percentage of full payments made by the customer.",
"TENURE": "The tenure of the customer in months."
}
insurance_defaults = {
"FirstPolYear": 1999,
"BirthYear": 1980,
"MonthSal": 1000,
"GeoLivArea": 0, # Options: 0, 1, 2, 3
"Children": 0, # Options: 0, 1, 2
"CustMonVal": 100,
"ClaimsRate": 2.33,
"PremMotor": 200,
"PremHousehold": 200,
"PremHealth": 200,
"PremLife": 200,
"PremWork": 200
}
# Define default values for banking dataset features
banking_defaults = {
"BALANCE": 2000,
"BALANCE_FREQUENCY": 0.5,
"PURCHASES": 500,
"ONEOFF_PURCHASES": 0,
"INSTALLMENTS_PURCHASES": 0,
"CASH_ADVANCE": 200,
"PURCHASES_FREQUENCY": 0.1,
"ONEOFF_PURCHASES_FREQUENCY": 0.1,
"PURCHASES_INSTALLMENTS_FREQUENCY": 0.5,
"CASH_ADVANCE_FREQUENCY": 5,
"CASH_ADVANCE_TRX": 5,
"PURCHASES_TRX": 5,
"CREDIT_LIMIT": 10000,
"PAYMENTS": 500,
"MINIMUM_PAYMENTS": 130,
"PRC_FULL_PAYMENT": 0.22,
"TENURE": 10
}
# Define default values for retail dataset features
retail_defaults = {
"Fresh": 6000,
"Milk": 9000,
"Grocery": 9000,
"Frozen": 4000,
"Detergents_Paper": 4000,
"Delicassen": 2000
}
return insurance_feature_descriptions,bankng_feature_descriptions,retail_feature_descriptions,insurance_defaults,banking_defaults,retail_defaults
def preprocess_data(data):
if 'CustID' in data.columns:
data = data.drop(columns=['CustID'])
if 'Cust_ID' in data.columns:
data = data.drop(columns=['Cust_ID'])
data = remove_outliers(data)
return data
def remove_outliers(df, threshold=3):
df_numeric = df.select_dtypes(include=[float, int])
z_scores = np.abs((df_numeric - df_numeric.mean()) / df_numeric.std())
df_clean = df[(z_scores < threshold).all(axis=1)]
return df_clean
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