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You will learn to:
Load and explore a data frame in R.
Create data visualisations in R.
Apply logistic regression, decision tree, and neural networks on a dataset.
Evaluate a model in R.
Skills
Data Visualisation
Data Statistics
Data Science
Machine Learning
Prerequisites
Intermediate understanding of R
Basic understanding of machine learning models
Basic understanding of logistic regression, decision trees, and neural networks
Technology
Rlang
Project Description
Predictive data analytics employs statistical methods on recorded data to predict future events. R language’s strong focus on statistics provides the necessary tools to create a predictive model, including libraries that enable the application of techniques like regression and time series analysis on any dataset. Furthermore, R provides robust support for data visualization.
In this project, we’ll use credit card data to detect fraud. We’ll complete the following steps in this project:
- Exploration of the dataset using the modules available in R to view and plot the information.
- Creating a logistic regression for credit card fraud detection.
- Creating a decision tree model for the same purpose.
- Use the Receiver Operating Characteristic (ROC) curve to compare the models.
Project Tasks
1
Data Preprocessing
Task 0: Getting Started
Task 1: Import Packages
Task 2: Load the Data
Task 3: Explore the Data
Task 4: Manipulate the Data
Task 5: Split the Data
2
Logistic Regression
Task 6: Fit the Logistic Regression
Task 7: Plot the Logistic Regression
Task 8: Plot the Receiver Operating Characteristic Curve
3
Decision Tree
Task 9: Create and Fit the Decision Tree
Task 10: Calculate the Accuracy
Task 11: Plot the Decision Tree
4
Neural Network
Task 12: Create and Fit the Neural Network
Task 13: Calculate the Accuracy of the Neural Network
Task 14: Plot the Comparison
Congratulations!
Relevant Courses
Use the following content to review prerequisites or explore specific concepts in detail.