Build A Board Game Predictor Using Machine Learning (Eduonix)

Build A Board Game Predictor Using Machine Learning (Eduonix)

Learn linear regression algorithm building a real project. Machine Learning is slowly spreading its tentacles into all aspects of technology and even further. Better algorithms are helping devices become smarter and users to become more informed. Now, its time to add a little fun to Machine Learning and in this course, we have tried to do exactly that!

Board games have been a great way to pass the time, from simple ones like The Game of Life to more complicated ones like Dungeons & Dragons. But before you become vested in a Game, what if you could find out how popular it really is? What if it was rated to help you learn how fun it really is? Well, now using Machine Learning you can!

This is short and concise Machine Learning course, we will focus on predicting the reviews of over 80,000 different board games. This project based course is a fun way for you to master two important Machine Learning algorithms that can be applied on a much grander scale to other data sets - Linear Regression Model and a Random Forest Regressor.
However, for this project course we will focus on board games. The information that we will work on was scrubbed from a database of 80,000 board games and includes information such as minimum players, maximum players, minimum playtime, maximum playtime, etc. We will use the models to ensure that using all of this information, it gives an accurate prediction regarding the reviews.
In this course, we will walk you through all of the steps needed to generate this output including how to train the models, how to load and preprocess the dataset appropriately, and so much more. At the end of this course, you will not only have an accurate prediction of the board game reviews, but also hands-on experience to learn how you can actually train two significant Machine Learning algorithms to learn and sort data, as well as make accurate predictions using a data set.
Heres what you will learn in this course:

  • Intro To Linear Regression Model And A Random Forest Regressor Machine Learning Algorithms
  • How To Import A Dataset
  • Write The Codes For Training The Two Models
  • How To Load And Preprocess The Dataset Appropriately
  • How To Generate The Needed Results
  • And So Much More!

Master two important Machine Learning algorithms in this EPIC project-based course! Enroll now and get started!

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Algorithms and Software Engineering for Professionals (Eduonix) Eduonix
Eduonix Learning Solutions Pvt. Ltd.

Algorithms and Software Engineering for Professionals (Eduonix)

Learn algorithms, data structures & the basics of data structure programs in this algorithms & software engineering course. Everything has a beginning and everything must be built from the ground up. This holds true even when it comes to software engineering and programming languages. Data structures provide a grounding for programming language and hold data and codes that determine what action will trigger what reaction.

Self Paced
Self-Paced
Approximation Algorithms Part I (Coursera) Coursera
École normale supérieure

Approximation Algorithms Part I (Coursera)

How efficiently can you pack objects into a minimum number of boxes? How well can you cluster nodes so as to cheaply separate a network into components around a few centers? These are examples of NP-hard combinatorial optimization problems. It is most likely impossible to solve such problems efficiently, so our aim is to give an approximate solution that can be computed in polynomial time and that at the same time has provable guarantees on its cost relative to the optimum.

Sep 28th 2026
5-12 Weeks
Cloud: Platform as a Service - Master's (Coursera) Coursera
Illinois Tech

Cloud: Platform as a Service - Master's (Coursera)

This course is aimed at preparing individuals to gain knowledge, skills, and abilities to demonstrate the knowledge for managing Platform as a Service (PaaS) in the Cloud. Students will learn to deploy, operate, and maintain cloud platforms for storing, processing, and transferring information with architecture design principles and a structured approach. Students will also learn the shared responsibility model and cloud security best practices to secure PaaS platforms for the application-hosting environments.

Sep 28th 2026
5-12 Weeks
Algorithms and Software Engineering for Professionals (Eduonix) Eduonix
Eduonix Learning Solutions Pvt. Ltd.

Algorithms and Software Engineering for Professionals (Eduonix)

Learn algorithms, data structures & the basics of data structure programs in this algorithms & software engineering course. Everything has a beginning and everything must be built from the ground up. This holds true even when it comes to software engineering and programming languages. Data structures provide a grounding for programming language and hold data and codes that determine what action will trigger what reaction.

Self Paced
Self-Paced
The Unix Workbench (Coursera) Coursera
Johns Hopkins University

The Unix Workbench (Coursera)

Unix forms a foundation that is often very helpful for accomplishing other goals you might have for you and your computer, whether that goal is running a business, writing a book, curing disease, or creating the next great app. The means to these goals are sometimes carried out by writing software. Software can’t be mined out of the ground, nor can software seeds be planted in spring to harvest by autumn. Software isn’t produced in factories on an assembly line. Software is a hand-made, often bespoke good. If a software developer is an artisan, then Unix is their workbench.

Sep 28th 2026
4 Weeks
Decision Making and Reinforcement Learning (Coursera) Coursera
Columbia University

Decision Making and Reinforcement Learning (Coursera)

This course is an introduction to sequential decision making and reinforcement learning. We start with a discussion of utility theory to learn how preferences can be represented and modeled for decision making. We first model simple decision problems as multi-armed bandit problems in and discuss several approaches to evaluate feedback. We will then model decision problems as finite Markov decision processes (MDPs), and discuss their solutions via dynamic programming algorithms. We touch on the notion of partial observability in real problems, modeled by POMDPs and then solved by online planning methods.

Sep 28th 2026
5-12 Weeks
VLSI CAD Part II: Layout (Coursera) Coursera
University of Illinois at Urbana-Champaign

VLSI CAD Part II: Layout (Coursera)

A modern VLSI chip is a remarkably complex beast: billions of transistors, millions of logic gates deployed for computation and control, big blocks of memory, embedded blocks of pre-designed functions designed by third parties (called “intellectual property” or IP blocks). How do people manage to design these complicated chips? Answer: a sequence of computer aided design (CAD) tools takes an abstract description of the chip, and refines it step-wise to a final design.

Sep 21st 2026
5-12 Weeks
Fundamentos de estadística aplicada (Coursera) Coursera
Universidad de los Andes

Fundamentos de estadística aplicada (Coursera)

El curso está orientado a profesionales de diferentes campos, que estén interesados en adquirir conceptos fundamentales de estadística aplicada. El contenido del curso será particularmente útil para profesionales que estén interesados en adelantar estudios de postgrado en ingeniería, administración o economía, entre otras profesiones, y que requieran de una adecuada fundamentación en estadística.

Sep 28th 2026
4 Weeks
Ethical Issues in Data Science (Coursera) Coursera
University of Colorado Boulder

Ethical Issues in Data Science (Coursera)

Computing applications involving large amounts of data – the domain of data science – impact the lives of most people in the U.S. and the world. These impacts include recommendations made to us by internet-based systems, information that is available about us online, techniques that are used for security and surveillance, data that is used in health care, and many more. In many cases, they are affected by techniques in artificial intelligence and machine learning.

Sep 28th 2026
5-12 Weeks
Probabilistic Graphical Models 3: Learning (Coursera) Coursera
Stanford University

Probabilistic Graphical Models 3: Learning (Coursera)

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.

Sep 28th 2026
5-12 Weeks
Computer Vision with Embedded Machine Learning (Coursera) Coursera
Edge Impulse

Computer Vision with Embedded Machine Learning (Coursera)

Computer vision (CV) is a fascinating field of study that attempts to automate the process of assigning meaning to digital images or videos. In other words, we are helping computers see and understand the world around us! A number of machine learning (ML) algorithms and techniques can be used to accomplish CV tasks, and as ML becomes faster and more efficient, we can deploy these techniques to embedded systems.

Sep 28th 2026
3 Weeks
Getting Started with Machine Learning at the Edge on Arm (Coursera) Coursera
Arm

Getting Started with Machine Learning at the Edge on Arm (Coursera)

The age of machine learning has arrived! Arm technology is powering a new generation of connected devices with sophisticated sensors that can collect a vast range of environmental, spatial and audio/visual data. Typically this data is processed in the cloud using advanced machine learning tools that are enabling new applications reshaping the way we work, travel, live and play.

Sep 28th 2026
5-12 Weeks