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Your First AI Model using Python and Scikit-learn

BeginnerGuided Project

Step into the world of AI with this beginner-friendly project on machine learning using Python and scikit-learn. Discover the magic of data analysis through the intuitive Random Forest algorithm. Unleash your potential as you learn to transform data into insights with captivating visualizations using matplotlib and scikitplot. Perfect for those new to AI, this project is your gateway to mastering essential skills in the exciting realm of data science and machine learning.

4.4 (183 Reviews)

Language

  • English

Topic

  • Machine Learning

Enrollment Count

  • 1.39K

Skills You Will Learn

  • Artificial Intelligence, Machine Learning, Python

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 30 minutes

Platform

  • SkillsNetwork

Last Update

  • February 20, 2025
About this Guided Project

A Look at the Project Ahead

In an era where data is king, the ability to analyze and interpret it effectively is a crucial skill. If you're new to AI and eager to delve into the realm of data analysis, this project will immerse you in the exciting world of machine learning, focusing on a highly relevant and impactful application: predicting customer churn in online trading platforms. Customer retention is a vital aspect of business sustainability, especially in competitive markets. By completing this project, you will not only learn the theoretical aspects of machine learning but also apply these concepts to a real-world problem, gaining skills that are highly valued in today's job market.


What You'll Learn:

  1. Practical Machine Learning Implementation: Dive into the practical aspects of machine learning using Python and scikit-learn, and understand how these tools can be used to solve real business problems.
  2. Random Forest Classification Mastery: Master the Random Forest algorithm, a versatile and powerful classification method that can be applied to a wide range of data analysis tasks.
  3. Data Visualization Techniques: Learn to create insightful 2D and 3D visualizations of your data using matplotlib and scikitplot, enhancing your ability to communicate complex information effectively.
  4. Real-world Application: Gain hands-on experience by working with actual data from online trading platforms, allowing you to understand the nuances and challenges of working with real-world data sets.

What You'll Need:

  • Technical Requirements: A basic understanding of Python programming and some familiarity with data analysis concepts will be beneficial. All necessary machine learning and data visualization libraries, such as scikit-learn, matplotlib, and scikitplot, are pre-installed in the IBM Skills Network Labs environment.
  • Hardware and Software: A computer with a stable internet connection and a modern browser like Chrome, Edge, Firefox, Internet Explorer, or Safari. Our platform is designed to be compatible with current browser versions to ensure a smooth learning experience.
  • A Keen Mind: Bring your curiosity and willingness to explore the fascinating world of machine learning and data analytics.

This project is more than just a learning exercise; it's a stepping stone to becoming proficient in machine learning, opening doors to numerous opportunities in data science and analytics. Get ready to embark on a journey that will equip you with the skills to make data-driven decisions in a business context.

Instructors

Mark Sturdevant

Open source software developer and advocate

Open Source Developer at IBM

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Kang Wang

Data Scientist

I am a Data Scientist in the IBM. I am also a PhD Candidate in the University of Waterloo.

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Contributors

Joseph 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.

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Lucy Xu

Data Scientist

I am a Data Scientist Intern at IBM. I am also currently in my fourth year at the University of Waterloo studying Statistics with a minor in Computing.

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