Gain insights into applied machine learning for marketing analytics. Explore data science techniques, create predictive models with Python libraries, and drive results with data-driven decisions.

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Notebook Live 3

In this course, you’ll learn applied machine learning in marketing analytics and cover modern data science techniques such as data exploration, data preprocessing, feature engineering and evaluation. You’ll gain hands-on experience with the Python libraries pandas, Scikit-learn, and seaborn, and learn how to use them to perform data wrangling, data analysis, create predictive models, and visualize your results.

This course will introduce you to basic data manipulation techniques. Further, you’ll cover specific topics such as customer revenue prediction using Linear Regression, customer segmentation using the K-Means Algorithm, customer churn prediction using Logistic Regression, and Customer Lifetime Value (CLV) analysis and prediction.

Whether you're a marketer or business professional, this course will give you the skills to make data-driven decisions and drive results. You’ll have hands-on experience predicting future revenue, segmenting your customers into different groups, and predicting customer churn

Marketing Analytics Using Machine Learning Techniques

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)


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans

# Task: 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: Encode 'Gender' column
df_customers['Gender'] = df_customers['Gender'].apply(lambda x: 1 if x=='Male' else 0)

# Task: Pick 'AnnualIncome' and 'SpendingScore' for the segmentation model
df_features = df_customers[['AnnualIncome', 'SpendingScore']]

# Task: Standardize the features
#   Import the StandardScaler class from Scikit-learn
from sklearn.preprocessing import StandardScaler

#   Create an object of the Standard Scaler
scaler = StandardScaler()

#   Scale the df_features dataframe
features_std = scaler.fit_transform(df_features)


Walk-through the solution of the challenge.

Introduction

Data Manipulation

Predicting Customer Revenue

Customer Segmentation

Predicting Customer Churn

Predicting Customer Lifetime Value (CLV)

Conclusion

Solution: Customer Segmentation

Task 1