Lazy Predict All Models in One Go
Automated Machine Learning (AutoML) is a field of machine learning that automates many monotonous tasks of Machine learning. You can go from zero to hero with some basic Machine Learning knowledge and Python programming skills. In this project, you will explore "LazyPredict," a semi-automated ML library used to build many popular models using two simple lines of code; you will apply your knowledge to predict flight delays and admissions probability.
4.5 (29 Reviews)
Language
- English
Topic
- Data Science
Industries
- Banking, Retail
Enrollment Count
- 185
Skills You Will Learn
- Python, Machine Learning, Data Science, AutoML
Offered By
- IBMSkillsNetwork
Platform
- SkillsNetwork
Last Update
- May 17, 2024
Why you should do this Guided Project
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In this guided project, you will learn to use "LazyPredict", a semi-automated ML library for machine learning tasks. Specifically, you will use it to predict for two real-life scenarios: 1) whether a flight will be delayed and 2) the chances of admissions to university.
A Look at the Project Ahead
- Understand what AutoML is
- Apply "LazyPredict" to classification and regression problems
- Evaluate models' performance from "LazyPredict"
What You'll Need
Language
- English
Topic
- Data Science
Industries
- Banking, Retail
Enrollment Count
- 185
Skills You Will Learn
- Python, Machine Learning, Data Science, AutoML
Offered By
- IBMSkillsNetwork
Platform
- SkillsNetwork
Last Update
- May 17, 2024
Instructors
Cindy Huang
Data Science Intern at IBM
Hey there! I'm a senior at the University of Toronto studying data science. My passion for machine learning lies in NLP and using technology to improve human experience.
Read moreJoseph Santarcangelo
Senior Data Scientist at IBM
Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.
Read moreContributors
Roxanne Li
Data Scientist at IBM
I am an aspiring Data Scientist at IBM with extensive theoretical/academic, research, and work experience in different areas of Machine Learning, including Classification, Clustering, Computer Vision, NLP, and Generative AI. I've exploited Machine Learning to build data products for the P&C insurance industry in the past. I also recently became an instructor of the Unsupervised Machine Learning course by IBM on Coursera!
Read more