Testing machine learning algorithms based on options features to see whether they are more accurate than Black-Scholes formula and creating a classifier to determine whether Black-Scholes will over- or under-estimate the price of the option.
In this project, I tested various algorithms such as XGBoost, Random Forest, Support Vector Machines, Decision Trees, Muliple Linear Regression, and Binomial Logistic Regression for different aspects of the prediction. I also feature engineered one feature and performed routine data pre-processing to make sure that the data that the analysis was performed on sound data.
- Data Cleaning
- Data Analysis
- Exploratory Data Analysis
- Descriptive Statistics
- Data Visualization
- Machine Learning
- Statistical Analysis
In this project, I achieved the highest score within the entire class with the combined accuracy of both of my models.