- Level Foundation
- المدة 3 ساعات hours
- الطبع بواسطة Coursera Project Network
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Offered by
عن
Welcome to this project-based course Decision Tree Classifier for Beginners in R. This is a hands-on project that introduces beginners to the world of statistical modeling. In this project, you will learn how to build decision tree models using the tree and rpart libraries in R. We will start this hands-on project by importing the Sonar data into R and exploring the dataset. By the end of this 2-hour long project, you will understand the basic intuition behind the decision tree algorithm and how it works. To build the model, we will divide or partition the data into the training and testing data set. Finally, you will learn how to evaluate the model’s performance using metrics like Accuracy, Sensitivity, Specificity, F1-Score, and so on. By extension, you will learn how to save the trained model on your local system. Although you do not need to be a data analyst expert or data scientist to succeed in this guided project, it requires a basic knowledge of using R, especially writing R syntaxes. Therefore, to complete this project, you must have prior experience with using R. If you are not familiar with working with using R, please go ahead to complete my previous project titled: “Getting Started with R”. It will hand you the needed knowledge to go ahead with this project on Decision Tree. However, if you are comfortable with working with R, please join me on this beautiful ride! Let’s get our hands dirty!الوحدات
Your Learning Journey
1
Assignment
- Assess Your Knowledge
1
Labs
- Decision Tree Classifier for Beginners in R
3
Readings
- Project Overview
- Decision Tree Classifier for Beginners in R Script and data sets
- Additional Reading: Link to read about important concepts
Auto Summary
Dive into the world of statistical modeling with "Decision Tree Classifier for Beginners in R," a hands-on course designed for those new to data science. Led by a Coursera expert, this 2-hour project guides you through building decision tree models using R's tree and rpart libraries. Ideal for beginners with basic R knowledge, you'll learn to import data, partition datasets, and evaluate model performance using key metrics. Join now to enhance your data science skills with practical, project-based learning!

Instructor
Arimoro Olayinka Imisioluwa