Regression Features and Labels - Practical Machine Learning Tutorial with Python p.3

TL;DR

Adjusting close column is a feature, not a label; forecast future prices using regression.

Transcript

Alright hello everybody and welcome to the third machine learning and second regression tutorial video. Where we left off, I was asking whether or not the adjusted close column would be a feature or a label. And the answer is really a feature and possibly none of the above. Um... It could be a label if we hadn't already kind of decided that we ar... Read More

Key Insights

• 😚 The adjusted close column is unsuitable as a label due to its dependency on future information.
• 😚 Creating a label column involves shifting adjusted close prices into the future for forecasting.
• 🍵 Handling missing data is crucial in machine learning to prevent biased model performance.
• ⚾ The forecast period for regression can be adjusted based on the desired prediction timeframe.
• ⚾ Regression algorithms aim to predict future prices based on historical data and features.
• 🏷️ Understanding feature and label selection is essential for accurate regression modeling.
• 😚 Adjusting close prices and forecast labels are pivotal in preparing data for machine learning algorithms.

Questions & Answers

Q: Why is the adjusted close column considered a feature and not a label?

The adjusted close column lacks predictive value for future prices due to being dependent on information not known in advance.

Q: How is the label column created for forecasting future prices?

The label column is generated by shifting the adjusted close prices into the future to predict price changes over a specified period.

Q: Why is it necessary to handle missing data in machine learning?

Missing data can adversely impact model performance, so it is crucial to either replace missing values or remove them to maintain dataset integrity.

Q: How is the forecast period determined for regression forecasting?

The forecast period, known as forecast out, is calculated as a percentage of the dataset length to determine how far into the future the algorithm will attempt to predict prices.

Summary & Key Takeaways

• Adjusted close column serves as a feature, not a label, due to its predictive limitations.

• Forecasting future prices involves creating a label column based on adjusted close prices shifted into the future.

• Preparing the dataset for regression involves handling missing data and defining the forecast period.