Propensity Modeling using XGBoost and Logistic Regression

Customers are unpredictable. Are they really? Learn all about implementing XGBoost and Logistic Regression to predict purchase propensity of customers.

Language: English

Instructors: AI Monks

Validity Period: 365 days

₹499 40.08% OFF


40% Cashback as Credits


Why this course?


Customer Analytics is one of the fastest-growing fields in customer-facing industries such as retail, eCommerce, real estate, banking, finance, insurance, automobile, and many more. One of the most common techniques is propensity modeling using machine learning. This is also one of the most popular problems to be asked in the data analytics and data scientist job interviews.

In this course, we will try to predict the customer’s propensity to purchase, which is nothing but the probability that a customer will make a transaction. There are many methods and machine learning techniques available to estimate the propensity score. We will cover the propensity score estimation using XGBoost and Logistic Regression.

Who should opt for this course?

  • Anyone who does not want to go deep into mathematics of the machine learning algorithm but wants to understand basic concepts and real-world application of XGBoost and Logistic Regression
  • Anyone who wishes to develop a practical understanding of XGBoost and Logistic Regression
  • Any student or professional preparing for a job interview and wish to understand and model a propensity model using machine learning techniques
  • Any professional willing to implement XGBoost and Logistic Regression at her work
  • Any professional who wants to implement propensity modeling to predict customer purchase behavior
  • Any student willing to work on academic projects using XGBoost and Logistic Regression

Comprehensive Course Coverage

This course covers the implementation of XGBoost and Logistic Regression for a propensity scoring problem – the propensity of purchase for the customers of a retail company, in great depth. It covers the following aspects:

  • Basics of the propensity modeling
  • Real-world applications of propensity modeling
  • Introduction to machine learning techniques can be used for propensity modeling
  • Basics of XGBoost and Logistic Regression and terminologies associated with them
  • In-depth understanding of the fundamentals of XGBoost and Logistic Regression
  • Building the SVM and Random Forest model in Python

In case of any query, please reach out to us at

Course Curriculum

Understanding Propensity Modeling
Introduction to Propensity Modeling (3 pages)
Application of Propensity Modeling (2 pages)
Machine Learning Techniques for Propensity Modeling (2 pages)
Propensity Modeling using Logistic Regression
Understanding Basics of Logistic Regression (3 pages)
Data Preparation (7 pages)
Training the Model (6 pages)
Evaluating the Model (9 pages)
Propensity Modeling using XGBoost
Understanding the Basics of XGBoost (5 pages)
Data Preparation (7 pages)
Training the Model (6 pages)
Evaluating the Model (8 pages)

How to Use

After successful purchase, this item would be added to your courses.You can access your courses in the following ways :

  • From the computer, you can access your courses after successful login
  • For other devices, you can access your library using this web app through browser of your device.


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