Summary

In this chapter, we explored several key concepts in the field of data analysis and predictive modeling. We started by discussing the basics of time series data, which refers to data that is collected over a certain period and contains a sequential order. The extraction of statistics from such sequential data is then highlighted as an important step in analyzing and understanding patterns within the data.

This chapter also emphasized the implementation of a model to predict stock market data. This involves using various techniques and algorithms to analyze historical stock market data, identify patterns and trends, and make predictions about future stock prices.

Lastly, this chapter addressed the challenge of dealing with imbalanced datasets in MATLAB. An imbalanced dataset refers to a situation where the distribution of classes within the dataset is significantly skewed, making it difficult to train a model accurately. We discussed methods and strategies to handle imbalanced...