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.

Suitability

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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