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Predictive Models: Build, Explore Data & Deploy

EDUCBA via Coursera

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Overview

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Build practical skills in predictive modeling by working through the complete model development lifecycle using a real-world banking use case. In this course, you will define a business problem, explore and interpret data through Exploratory Data Analysis (EDA), and prepare datasets using data imputation and variable selection techniques. Next, you will develop predictive models using Information Value (IV) analysis and multicollinearity checks to identify meaningful variables. You will evaluate model performance with ranking techniques, decile analysis, KS statistics, AUC, and Lift, then improve model performance through monotonic binning and tree-based optimization methods. The course concludes by validating models on unseen datasets and deploying them to a simulated production environment, giving you practical experience with the end-to-end predictive modeling workflow. Whether you are a learner interested in predictive analytics, machine learning workflows, or data-driven decision-making, this course provides a structured, hands-on approach to building, evaluating, optimizing, validating, and deploying predictive models using established statistical techniques and real-world business scenarios.

Syllabus

Exploratory Analysis and Data Preparation

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.

Model Building and Evaluation

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.