Life Expectancy Analysis Using Regression

prerequisites: Intermediate Python, Basics of statistics (regression analysis), Basics of Numpy and Pandas
skills learned: Organize a data-mining project following the CRISP-DM model, Perform data cleaning and exploration, Perform regression modelling and diagnostics, Perform model selection

Monica Guimaraes and Alexey Grigoriev

5 weeks · 5-7 hours per week · INTERMEDIATE

In this liveProject, you’ll take on the role of a data analyst working for the Jones Family philanthropic foundation. The board of directors is interested in learning about the life expectancy of Americans so that they can better target their charitable spending. To help them in their research, they’ve turned to you. Your challenge in this liveProject is to run a regression analysis on demographic data to find factors related to life expectancy, and answer data-mining questions about the distribution of these demographic variables. To do this, you’ll plan your data-mining and regression analysis following the CRISP-DM model, clean and model your data, assess the accuracy of your findings, and present your results—all with open source tools from the Python ecosystem.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

book and video resources

When you start your liveProject, you get full access to the following books and videos for 90 days.

In MEAP, you get immediate access to a liveProject under development, so you can participate while it is created, tested, and improved. Get started today, and pick up right where you've left off whenever we update the project!

project authors

Monica Guimaraes

Monica Guimaraes has more than fifteen years of experience organizing and teaching undergraduate courses in automata theory, numerical analysis, programming languages and statistics. Her passion for artificial intelligence goes back to her college years, having done her thesis in automatic-theorem-proving and working later as an AI researcher. She has also extensive experience in software development in corporations, having worked on Santander Group in the projects of Data-Warehousing and CRM. She is currently doing a master in “Machine Learning and Data Analytics” and channels her passion for books and everything new in technology reviewing books for Manning Publications. In her free time, she loves reading science-fiction and going hiking in the German forests with her husband and her Shiba-Inu.

Alexey Grigoriev

Alexey Grigorev has more than ten years of experience as a software engineer, and has spent the last six years focused on machine learning. Currently, he works as a lead data scientist at the OLX Group, where he deals with content moderation and image models. He is the author of two other books on using Java for data science and TensorFlow for deep learning.

Prerequisites

This liveProject is for intermediate Python programmers who know the basics of regression analysis. To begin with this liveProject, you will need to be familiar with:

TOOLS

Basics of numpy and pandas
Basics of matplotlib
Basics of Jupyter notebooks

TECHNIQUES

Basics of statistics and regression analysis

you will learn

In this liveProject, you’ll learn vital skills for planning and orchestrating a data analysis project. These foundational skills are easy to transfer to almost any data undertaking.

Collecting, cleaning and exploring data
Regression modelling and diagnostics
Evaluating model appropriateness
Presenting findings as Jupyter notebooks

features

Self-paced: You choose the schedule and decide how much time to invest as you build your project.
Project roadmap: Each project is divided into several achievable steps.
Get Help: While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others: For each step, compare your deliverable to the solutions by the author and other participants.
book resources: Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.

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