import pandas as pd
from sklearn.preprocessing import MinMaxScaler

# Step 1: Load the dataset
df = pd.read_csv("customers.csv")
print("Original Dataset:")
print(df)

# Step 2: Clean - Fill missing values
df['age'] = df['age'].fillna(df['age'].mean())
df['income'] = df['income'].fillna(df['income'].mean())
print("\nAfter Cleaning:")
print(df)

# Step 3: Normalize - Scale income to 0-1
scaler = MinMaxScaler()
df['income'] = scaler.fit_transform(df[['income']])
print("\nAfter Normalizing Income:")
print(df)

# Step 4: Encode - Convert gender to numbers
df['gender'] = df['gender'].map({'Male': 0, 'Female': 1})
print("\nAfter Encoding Gender:")
print(df)