Predictive Models: Build, Explore Data & Deploy

位教师：EDUCBA

访问权限由 New York State Department of Labor 提供

2个模块

深入了解一个主题并学习基础知识。

6 小时完成

灵活的计划

自行安排学习进度

2个模块

深入了解一个主题并学习基础知识。

6 小时完成

灵活的计划

自行安排学习进度

您将学到什么

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.

您将获得的技能

要了解的详细信息

可分享的证书

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作业

8 项作业

授课语言：英语（English）

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

该课程共有2个模块

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.

涵盖的内容

9个视频4个作业

9个视频总计73分钟

Introduction to Predictive Model for Term Deposit 3分钟
Problem Statement 6分钟
Problem Statement Continue 6分钟
Variable Explanation 11分钟
Variable Explanation Continue 7分钟
EDA and Insights 8分钟
EDA and Insights Continue 10分钟
Data Imputation 10分钟
Variable Selection 12分钟

4个作业总计60分钟

Exploratory Analysis and Data Preparation 30分钟
Introduction and Problem Definition 10分钟
Understanding and Exploring Variables 10分钟
Preparing Data for Modeling 10分钟

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.