Scikit Learn Machine Learning Tutorial for investing with Python p. 22

TL;DR

Learn how to build a data set for machine learning stock prediction using Python and support vector machines.

Transcript

what's up everybody Welcome to the 22nd ssit learn with python for machine learning with our investing example using a support Vector machine or otherwise supervised learning tutorial video anyway moving on uh in our last video what we did was we pulled this Yahoo finance you know current data and we want to make a prediction based on this data and... Read More

Key Insights

• 😫 In order to build a data set for machine learning stock prediction, it is important to modify the script to handle changes in data formatting and file locations.
• 📁 The process involves accessing individual files (tickers) from a directory and extracting relevant data values for analysis.
• 😫 By training a machine learning model with the data set, accurate predictions can be made for stock investments.
• 😫 The script can be used to pull current data and update the data set periodically for more accurate predictions.
• 💁 The modified script removes unnecessary information, such as S&P 500 data, and focuses on the values required for stock prediction.
• 😫 The resulting data set can be saved as a CSV file and used for training and making predictions.
• 😒 The video tutorial mentions the potential use of a stop-loss strategy based on fundamental features of the companies, rather than just stock prices.

Questions & Answers

Q: What is the purpose of modifying the script in the tutorial?

The script needs to be modified to handle the new formatting of the data and account for changes in the file location and directory structure.

Q: How can we access individual files (tickers) in the data set?

By listing the contents of the directory and iterating through each file, the script extracts the tickers and their corresponding file paths.

Q: What data values are extracted from the files?

The script extracts only the necessary values, such as ticker, date, and stock price, while disregarding other information like percentage change or S&P 500 data.

Q: How can the resulting data set be used for stock prediction?

The data set can be used for training a machine learning model, such as a support vector machine, to predict future stock prices and make investment decisions.

Summary & Key Takeaways

• The video tutorial demonstrates how to modify a script to handle the formatting of new data for stock prediction.

• The script involves listing the contents of a directory, accessing individual files (tickers), and extracting necessary data values.

• The resulting data set can be used to train and make predictions for stock investments.