Master Data-Driven Decisions with Decision Tree Modeling Using R

In today’s data-driven world, organizations rely on powerful statistical techniques to make informed
decisions. Decision Tree Modeling is one of the most intuitive and widely used methods for
classification and prediction in data science. By leveraging R, a leading programming language for
statistical analysis, professionals can build and interpret decision trees to solve real-world problems
efficiently.

ENCODE-IT’s Decision Tree Modeling Using R course is a comprehensive program designed to teach
you how to use decision trees for classification and regression tasks. The course covers the core
concepts of decision trees, such as entropy, Gini impurity, and pruning, and walks you through
building and evaluating decision tree models using R's robust libraries like rpart and tree. You’ll learn
how to visualize decision trees, understand feature importance, and apply the technique to a variety
of real-world datasets. This hands-on course will provide you with the skills to build reliable and
interpretable predictive models.

Salary Scale in India
The demand for data scientists and analysts proficient in decision tree modeling and statistical
techniques is high in India. Professionals with skills in decision tree modeling using R can expect
competitive salaries. Entry-level positions typically offer a salary of ₹6,00,000 to ₹9,00,000 per year.
With 3-5 years of experience, data scientists and analysts can earn between ₹12,00,000 and
₹20,00,000 annually, depending on their skill set and the industry. Senior professionals, such as
machine learning engineers or data science managers, can earn upwards of ₹25,00,000 per year,
reflecting the growing need for data-driven decision-making in businesses across sectors.

Placement Assistance & Certification
At ENCODE-IT, we prioritize your career success. Upon completing the Decision Tree Modeling Using
R course, you will receive an industry-recognized certification, which will help validate your expertise
and open doors to exciting career opportunities. Additionally, our placement assistance team will
support you in securing job opportunities by connecting you with top companies seeking
professionals with expertise in data analysis, machine learning, and predictive modeling.

Course Curriculum

1. Introduction to Decision Trees and R Programming
 Overview of Decision Trees: Definition and Types (Classification and Regression)
 Understanding R Programming for Data Analysis
 Introduction to Key R Libraries: rpart, tree, caret, and randomForest
 Setting up R and Installing Necessary Packages
 Data Preprocessing in R: Handling Missing Values, Categorical Data, and Feature Scaling

2. Decision Tree Fundamentals
 Understanding How Decision Trees Work
 Key Concepts: Nodes, Branches, Leaves, and Splits
 Splitting Criteria: Gini Impurity vs. Entropy

 Overfitting and Underfitting in Decision Trees
 Pruning and Stopping Criteria for Tree Construction
 Visualizing Decision Trees in R

3. Building Classification Trees with R
 Introduction to Classification Trees
 Building a Basic Classification Tree Using the rpart Package
 Tuning Parameters for Optimal Performance
 Model Evaluation Metrics: Accuracy, Precision, Recall, F1 Score, and AUC
 Cross-validation and Hyperparameter Tuning for Classification Trees
 Handling Imbalanced Datasets in Classification

4. Building Regression Trees with R
 Introduction to Regression Trees: Use Cases and Applications
 Constructing Regression Trees Using the rpart Package
 Interpreting Regression Trees: Predicting Continuous Variables
 Evaluating Regression Tree Performance: RMSE, MAE, R-Squared
 Model Validation Techniques for Regression Trees
 Practical Examples: Predicting Housing Prices, Stock Prices, etc.

5. Advanced Techniques in Decision Trees
 Random Forests: Introduction and Building Ensemble Models
 Boosting Techniques: Gradient Boosting and XGBoost
 Feature Selection and Feature Importance in Decision Trees
 Handling Missing Data in Decision Trees
 Decision Trees with Continuous and Categorical Data
 Visualizing Model Performance: ROC Curve, Confusion Matrix

6. Decision Trees in Real-World Applications
 Building a Customer Segmentation Model Using Decision Trees
 Predicting Loan Defaults and Credit Scoring with Decision Trees
 Analyzing Marketing Campaigns Using Classification Trees
 Time Series Forecasting with Regression Trees
 Real-World Project: Applying Decision Trees to a Business Problem
 Model Deployment and Integration with Other Tools

7. Performance Evaluation and Model Interpretation
 Analyzing Model Results: Interpretation of Decision Tree Splits
 Evaluating Model Performance: Cross-validation vs. Holdout Method
 Fine-Tuning Hyperparameters for Optimal Decision Tree Performance
 Model Deployment: Exporting R Models for Use in Production
 Explainability and Interpretable AI in Decision Trees
 Troubleshooting and Improving Decision Tree Models

8. Final Project and Certification Exam
 Hands-on Final Project: Building a Predictive Model Using Decision Trees on a Real-World
Dataset
 Performance Tuning and Model Optimization
 Final Exam to Assess Understanding of Course Concepts
 Certification and Placement Assistance

Key Features
 Tools & Platforms: R, rpart, tree, caret, randomForest, ggplot2, RStudio
 Real-World Projects: Apply decision tree modeling techniques to real datasets in various
industries like finance, marketing, and healthcare
 Certification & Placement Support: Earn a certificate of completion and receive job
placement assistance
 Expert Instructors: Learn from professionals with extensive experience in data science and
machine learning
 Career Advancement: Gain the skills required for roles such as Data Analyst, Data Scientist,
Machine Learning Engineer, and Business Analyst

Why Choose ENCODE-IT for Decision Tree Modeling Using R?
This course is designed for individuals who want to excel in predictive modeling and gain a deep
understanding of decision trees. Whether you're looking to advance your career in data science or
build expertise in machine learning algorithms, this course will equip you with the necessary skills to
handle complex data problems and make data-driven decisions. With expert guidance, hands-on
projects, and a focus on real-world applications, ENCODE-IT’s Decision Tree Modeling Using R
course is the perfect choice for aspiring data professionals. Enroll today and unlock your potential in
the world of data science!