Regression forecasting and predicting - Practical Machine Learning Tutorial with Python p.5

TL;DR

In this tutorial, the speaker demonstrates how to use a linear regression algorithm to predict stock prices using unknown data.

Transcript

What's going on Everybody Welcome [to] the fifth machine Learning and Fourth Regression tutorial in This Tutorial? We're Going to be Building on the last one Where We created This Linear Regression Algorithm We Found that it's got great Accuracy and all that And now we're Ready to actually Predict like out Into the Unknown right [so] it Turns out w... Read More

Key Insights

• ❓ Linear regression can be used to predict stock prices using historical data.
• ❓ The accuracy of the algorithm can be measured by comparing predicted values to actual values.
• ⌛ Using unknown data in the algorithm can help make predictions for future time periods.

Questions & Answers

Q: How is the unknown data used in the linear regression algorithm?

The unknown data is used as the x values for predicting the y values (stock prices). By using this data, the algorithm can make predictions for the next 30 days.

Q: How is the accuracy of the algorithm determined?

The accuracy of the algorithm is calculated by comparing the predicted values to the actual values. In this case, the algorithm achieves 96% accuracy.

Q: Can the algorithm handle single value predictions as well as array predictions?

Yes, the algorithm can handle both single value predictions and array predictions. Simply pass a single value or an array of values to the predict function, and it will output the corresponding predictions.

Q: How is the predicted data displayed on a graph?

The predicted data is plotted on a graph using matplotlib. The graph shows the known data as well as the predicted data for the next 30 days.

Summary & Key Takeaways

• The speaker builds on a previous tutorial to create a linear regression algorithm for predicting stock prices.

• The algorithm achieves 96% accuracy and is tested on unknown data.

• The predicted stock prices for the next 30 days are shown on a graph.