What you'll learn

Perform EDA and prepare banking data using imputation and variable selection.
Build predictive models with IV analysis, binning, and multicollinearity checks.
Evaluate models using KS, AUC, Lift, and deploy them in simulated production.

Skills you'll gain

Details to know

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Assessments

8 assignments

Taught in English

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There are 2 modules in this course

This hands-on course guides learners through the complete lifecycle of predictive modeling, using a real-world banking use case to forecast term deposit subscriptions. Learners will begin by defining a business problem, analyzing and interpreting raw data through Exploratory Data Analysis (EDA), and applying data preparation techniques such as imputation and variable selection.

The course then progresses to constructing robust models using industry-standard statistical practices, including Information Value (IV) analysis and multicollinearity checks. Learners will evaluate model performance using ranking techniques, decile analysis, KS statistics, AUC, and Lift. They will also enhance model effectiveness through optimization strategies such as monotonic binning and tree-based methods. Finally, the course concludes by validating the models on unseen datasets and deploying them to a simulated production environment. By the end, learners will have gained the skills necessary to confidently design, develop, and deliver predictive models that solve real-world business challenges.

This module introduces learners to the foundational steps of building a predictive model in a real-world banking context. It begins by clearly defining the business problem of predicting customer subscription to a term deposit product. The module then guides learners through understanding the dataset, exploring key variables using Exploratory Data Analysis (EDA), and preparing the data for modeling by handling missing values and selecting relevant features. By the end of the module, learners will be equipped with essential data preprocessing skills and the ability to frame analytical problems for machine learning applications.

What's included

9 videos4 assignments

9 videosTotal 73 minutes

Introduction to Predictive Model for Term Deposit2 minutes
Problem Statement6 minutes
Problem Statement Continue5 minutes
Variable Explanation10 minutes
Variable Explanation Continue7 minutes
EDA and Insights8 minutes
EDA and Insights Continue10 minutes
Data Imputation9 minutes
Variable Selection11 minutes

4 assignmentsTotal 60 minutes

Exploratory Analysis and Data Preparation30 minutes
Introduction and Problem Definition10 minutes
Understanding and Exploring Variables10 minutes
Preparing Data for Modeling10 minutes

This module equips learners with the tools and techniques required to build, assess, and improve predictive models. It begins with the development of models using Information Value and multicollinearity checks to select the right variables. Learners then explore techniques to assess model performance using ranking tables, the Kolmogorov-Smirnov (KS) statistic, AUC, and Lift metrics. The module concludes with optimization strategies such as monotonicity adjustment and decision tree refinement, followed by validation and deployment of the model to unseen datasets. By the end of the module, learners will be proficient in developing, evaluating, and preparing models for production environments.