Elementary Classification: Logistic Regression

This course provides hands-on predictive analytic work.

This time we will look at classification methods which, though also used in Time Series Analysis (binary classification/prediction of upticks vs. downticks) extends to other areas such as Credit Risk Analysis and Fraud Prediction.


This course is suitable for: Tech Analysts, S&T Analysts (optionally).

Learning Outcomes

By the end of this course, participants will be able to:

  • Explain how logistic regression can be used as a signal for trade decisions
  • Demonstrate how it can be more efficient than linear regression to this end
  • Implement a logistic regression model in Python and compare with linear regression techniques

Course Content

  • Introduction to classification methods
  • What is Logistic Regression and what are its applications in Finance?
  • Implement a Logistic Regression model on a single price data variable in TensorFlow using the above concepts
  • How does the predictive power compare to Linear Regression we have already done
  • Multivariate logistic regression using Keras and TensorFlow in a very few lines of code: handwritten character recognition from images


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