What you'll get
- 2+ Hours
- 2 Courses
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
Synopsis
- Develop a foundational understanding of tree-based models, with an emphasis on decision trees and their structure.
- Apply decision tree techniques to real-world scenarios such as forecasting bank loan defaults and analyzing varied datasets.
- Build skills in data preparation, model implementation in R, and performance evaluation using confusion matrix analysis.
- Gain guidance on using decision tree methods in business scenarios to generate actionable insights through predictive analytics.
- Decision tree modeling is a widely used skill among data scientists and professionals new to the field.
- Examine Decision Tree Regression concepts in depth.
- Master the fundamental ideas that form the foundation of decision tree algorithms.
- Implement Decision Tree Classification models in R using practical exercises.
- Understand the challenges of using decision trees in R and why they remain valued for their simplicity and interpretability compared to other models.
- Develop the skills needed to confidently create predictive models and tree-based learning solutions.
Content
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Decision Tree Modeling Using R | 1h 4m | ✔ | View Curriculum |
| Decision Tree Case Study Using R- Bank Loan Default Prediction | 1h 47m | ✔ | View Curriculum |
Description
This course provides a comprehensive overview of tree-based modeling, combining theoretical concepts with practical exercises. Participants will learn the fundamentals of decision trees and their role in predictive modeling, followed by hands-on applications, such as predicting bank loan defaults. Interactive sessions will cover data preprocessing, R-based model development, and performance evaluation using confusion matrices. Additional case studies include advertisement analysis and diabetes prediction. By the end of the course, learners will understand decision tree modeling and its applications in various fields.
Section 1: Introduction to Decision Trees
Learners will develop a basic understanding of decision tree modeling in R. This section covers the fundamentals of model construction, including decision nodes, paths, and splitting criteria.
Learners will develop a basic understanding of decision tree modeling in R. This section covers the fundamentals of model construction, including decision nodes, paths, and splitting criteria.
Section 2: Decision Trees - Bank Loan Default Prediction
Participants will use real datasets to apply decision trees to predict bank loan defaults. They will learn data preprocessing, model training, and performance evaluation.
Participants will use real datasets to apply decision trees to predict bank loan defaults. They will learn data preprocessing, model training, and performance evaluation.
Section 3: Advertisement Dataset
This section covers the analysis of an advertisement dataset using decision tree models. Participants will practice data preprocessing, feature scaling, and model evaluation for marketing analytics.
This section covers the analysis of an advertisement dataset using decision tree models. Participants will practice data preprocessing, feature scaling, and model evaluation for marketing analytics.
Section 4: Diabetes Dataset
Participants will apply decision tree techniques to a diabetes dataset, learning to visualize classifiers, generate predictions, and interpret outcomes. This section highlights the value of decision trees in healthcare analytics and predictive modeling.
Participants will apply decision tree techniques to a diabetes dataset, learning to visualize classifiers, generate predictions, and interpret outcomes. This section highlights the value of decision trees in healthcare analytics and predictive modeling.
Section 5: Caeseats Dataset
Learners will work with the Caeseats dataset to deepen their understanding of decision trees. They will explore data-splitting techniques, build models using R’s tree package, and analyze outcomes to extract practical insights.
Learners will work with the Caeseats dataset to deepen their understanding of decision trees. They will explore data-splitting techniques, build models using R’s tree package, and analyze outcomes to extract practical insights.
Section 6: Conclusions
In the final section, participants will review and consolidate key insights, summarizing their understanding of decision tree modeling in R and its real-world applications.
Requirements
- No previous experience in machine learning is necessary.
- Basic understanding of R programming is recommended.
Target Audience
- Professionals interested in building expertise in data and analytics.
- Data Engineers.
- Data Analysts.
- Data Architects.
- Software Developers.
- IT operations specialists.
- Technical Managers.
