Scikit Learn Machine Learning Tutorial for investing with Python p. 12

TL;DR

In this tutorial, Sid demonstrates how to add additional data to a machine learning model and provides insights on handling missing or unavailable data.

Transcript

hello everybody and what is going on Welcome to the 12th tutorial video in our machine learning with Sid learn for investing with as an example tutorial video in this video we're going to so in the last video I showed you guys a really basic example of just using numbers and uh showing how we can use linear svm in this video we're going to kind of ... Read More

Key Insights

• 👶 Adding new features to a machine learning model can improve its performance and accuracy.
• 😑 Regular expressions can be used to handle formatting issues and extract relevant data from text.
• 🍵 Missing or unavailable data can be handled by assigning a specific label and deciding whether to include or exclude it in the model.
• 🖼️ It is important to update the data frame definition and append the new values to the existing data frame for proper analysis.
• 👨‍💻 The efficiency and readability of the code can be improved by eliminating repetitive lists and using more efficient coding techniques.
• 🥠 Testing and experimenting with different approaches to handling missing data can help fine-tune the machine learning model.
• 🙃 Sid emphasizes the importance of continuous learning and improvement in machine learning techniques.

Questions & Answers

Q: How does Sid suggest adding new features to a machine learning model?

Sid recommends copying and pasting a list of new features into the code and updating the data frame definition accordingly.

Q: How does Sid handle missing or unavailable data in the model?

Sid uses regular expressions to identify missing or unavailable data and appends it to the data frame with a specific label, such as "not available."

Q: What are some examples of new features that can be added to the model?

Some examples of new features that can be added include outstanding shares, market value, return on assets, and trailing annual dividend.

Q: How does Sid ensure that the data is properly formatted and converted into numeric values?

Sid uses regular expressions to handle formatting issues such as subscript or superscript characters, percentages, and different notation for billions or millions. He also converts the values to floats and multiplies them by the appropriate factor (e.g., 1 billion or 1 million).

Summary & Key Takeaways

• The video introduces the concept of adding more data to the existing machine learning model and tracking various features of interest.

• Sid explains the process of copying and pasting a list of new features into the code and updating the data frame definition.

• The video also includes a demonstration of how to handle missing or unavailable data using regular expressions and append the values to the data frame.