You have also been requested to check whether there are any anomalies in the given sales dataset. The objective of this task being fraud detection. Anomaly Detection is used for different applications. It is a commonly used technique for fraud detection. It is also used in manufacturing to detect anomalous systems such as aircraft engines. It can also be used to identify anomalous medical devices and machines in a data center.

Dimensionality Reduction: This section of the project entails reducing your dataset to a low dimensional dataset using the t-SNE algorithm or PCA. You will be required to perform your analysis and provide insights gained from your analysis. Feature Selection: This section requires you to perform feature selection through the use of the unsupervised learning methods learned earlier this week. You will be required to perform your analysis and provide insights on the features that contribute the most information to the dataset.

This section will require that you create association rules that will allow you to identify relationships between variables in the dataset. You are provided with a separate dataset that comprises groups of items that will be associated with others. Just like in the other sections, you will also be required to provide insights for your analysis.

This is a continuation of the previous document on Exploratory Data Analysis using R for the likelihood of clicking on ads on a blog website. In this project, I was tasked to use supervised learning for modeling.

This is my project on online shopping analysis of Kira Plastinina, a Russian brand.

This file contains my Exploratory Data Analysis of the chances of clicking on ads using R programming.