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 pandas as pd
import numpy as np

from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

# challenge 1 - load the dataset
df_retail = pd.read_csv('wrangled_transactions.csv', header=0, index_col='customer_id')

# challenge 2 - handle outliers
df_retail = df_retail.query('revenue_2019 < 5000')

# challenge 3 - split the dataset into test & training dataset
X = df_retail[['revenue_2019']]
y = df_retail['revenue_2020']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=11)

# challenge 4 - build and train a linear regression model
model = LinearRegression()
model.fit(X_train, y_train)

# challenge 5 - calculate the RMSE score
rmse = mean_squared_error(y_test, model.predict(X_test))**0.5
print("Root mean squared error: %.2f" % rmse)


Learn by doing your work alongside the regression challenge solution.

Introduction

Data Manipulation

Predicting Customer Revenue

Customer Segmentation

Predicting Customer Churn

Predicting Customer Lifetime Value (CLV)

Conclusion

Solution: Revenue Prediction

Solution code