Practical Tree Based Modeling with Decision Trees: From Theory to Application
Learning Path | 2 Course Series
Explore the world of decision tree modeling from theory to practice in our comprehensive course. Learn the fundamentals of tree-based modeling and its application in predicting bank loan defaults and analyzing datasets. Gain practical skills in data preprocessing, model coding in R, and evaluation techniques. Master decision tree modeling for diverse applications in predictive analytics.
Offer ends in:
What you'll get
- 2+ Hours
- 2 Courses
- Course Completion Certificates
- One year access
- Self-paced Courses
- Technical Support
- Mobile App Access
- Case Studies
Synopsis
- The fundamentals of tree-based modeling, focusing on decision trees and their structure.
- Practical applications of decision tree modeling, including predicting bank loan defaults and analyzing various datasets.
- Essential skills in data preprocessing, model coding using R, and evaluating model performance using confusion matrices.
- How to apply decision tree modeling techniques to real-world scenarios and gain insights from predictive analytics.
- This course includes learning decision tree modeling which are used by data scientists or people who aspire to be the data scientist
- Decision Tree Regression
- Decision Tree Theory
- Implementation of Decision Tree Classifications using R
- The decision tree is a key challenge in R and the strength of the tree is they are easy to understand and read when compared with other models.
- This course makes one become proficient to build predictive and tree-based learning models
- This course includes learning decision tree modeling which are used by data scientists or people who aspire to be the data scientist
- Implementation of Decision Tree Classifications using R
Content
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MODULE 1: Essentials Training
Courses No. of Hours Certificates Details Decision Trees Modeling using R 1h 4m ✔ Decision Trees - Bank Loan Default Prediction using R 1h 47m ✔
Description
This course provides a comprehensive introduction to tree-based modeling with decision trees, covering both theoretical concepts and practical applications. In the introductory section, participants will gain foundational knowledge of decision trees, understanding their structure and significance in predictive modeling. Building upon this, the course transitions into practical applications, starting with bank loan default prediction. Through a series of lectures, participants will learn how to preprocess data, write model code in R, and evaluate model performance using confusion matrices. Additionally, they will explore other practical scenarios such as analyzing an advertisement dataset and predicting diabetes occurrences. By the conclusion of the course, participants will have a solid understanding of decision tree modeling and its diverse applications across various domains.
Section 1: Introduction to Decision Trees
In this introductory section, participants will gain a foundational understanding of decision trees and their application in predictive modeling using R. The course will cover the basics of decision tree construction, including route nodes and decision criteria.
Section 2: Decision Trees - Bank Loan Default Prediction
Expanding upon the fundamentals covered in the previous section, participants will delve into a practical application of decision trees: predicting bank loan defaults. Through a comprehensive exploration of real-world datasets and hands-on exercises, participants will learn how to preprocess data, train decision tree models, and evaluate their performance for loan default prediction.
Section 3: Advertisement Dataset
This section introduces participants to another practical scenario: analyzing an advertisement dataset using decision trees. Participants will learn data preprocessing techniques, feature scaling, and model evaluation methods specific to the advertisement domain.
Section 4: Diabetes Dataset
In this section, participants will apply decision tree modeling to analyze a diabetes dataset. They will learn how to plot model classifiers, make predictions, and interpret results, gaining valuable insights into decision tree applications in healthcare analytics.
Section 5: Caeseats Dataset
Continuing their exploration, participants will work with the Caeseats dataset to further solidify their understanding of decision trees. They will learn data splitting techniques, model implementation using the tree package, and interpret the results for actionable insights.
Section 6: Conclusion
In the concluding section, participants will reflect on their learning journey and key takeaways from the course. They will summarize their understanding of decision trees modeling using R and its applications across various domains.
Requirements
- No prior knowledge of machine learning required
- Basics of R
Target Audience
- Anyone who wants to learn about data and analytics
- Data Engineers, Analysts, Architects, Software Engineers, IT operations, Technical managers
Offer ends in:
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