Folder Structure and How to Run Code Files

In this course, you have learned to create a support vector classifier (SVC) algorithm on S&P 500 data. You also learnt how to import the libraries, read the data, define the explanatory and predictor variables, split the data into train and test before implementing the SVC strategy.

In this folder, you will find the data files, code files and necessary information needed to run the code files successfully.

Before you start running the files on your local machine, please check whether your local machine is set up correctly.

You can go through the steps in the ReadMe.html file to set up the python environment for running Quantra code files.

How to run the code files provided in the zip file?

For an in-depth explanation of how to run the python codes included in the zip files, you can head to the link here.

List of files available to you

Folder contains two subfolders divided based on the course sections and one data folder that stores the data required to run them.

data

SPY.csv

BAC_2010_2021.csv

Supervised Learning

Linear Regression.ipynb

Logistic Regression.ipynb

KNN Classification.ipynb

Support Vector Machine.ipynb

Random Forest.ipynb

Artificial Neural Network.ipynb

Predict Trend Using Classification

Support Vector Classifier Strategy.ipynb

If you are new to Jupyter notebook, want to learn the structure or need help to run the files, then refer to the article Jupyter Notebook Tutorial: Installation, Components and Magic Commands for detailed steps.