Elementary Classification: Logistic Regression

More hands-on predictive analytic work, this time with 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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