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Marketing Analytics Using Machine Learning Techniques

Solution: Customer Segmentation

Explore how to perform customer segmentation by applying feature engineering, standardizing data, and using the k-means clustering algorithm. Understand each step from encoding categorical variables to assigning segment labels, equipping you to categorize customers for targeted marketing analysis.

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Task 1

Here is the solution to the feature engineering challenge.

Python 3.10.4
import numpy as np
import pandas as pd
# Task 1: Read the csv dataset named 'mall_customers.csv', use the first row as header
df_customers = pd.read_csv('mall_customers.csv', header=0)
# Task 2: Encode 'Gender' column
df_customers['Gender'] = df_customers['Gender'].apply(lambda x: 1 if x=='Male' else 0)
# Task 3: Pick 'AnnualIncome' and 'SpendingScore' for the segmentation model
df_features = df_customers[['AnnualIncome', 'SpendingScore']]
# Task 4: Standardize the features
# Task 4.1: Import the StandardScaler class from Scikit-learn
from sklearn.preprocessing import StandardScaler
# Task 4.2: Create an object of the Standard Scaler
scaler = StandardScaler()
# Task 4.3: Scale the df_features dataframe
features_std = scaler.fit_transform(df_features)

Explanation

  • In line 6, load the CSV dataset named mall_customers.csv and use the first row as a ...