Predicting Stock Prices with Machine Learning
Build a Linear Regression Model with SKLearn, load and analyze Stock Price data, and predict stock prices 30 days in the future!
TECHNOLOGIES
- Machine Learning
- SKLearn
WATCH TIME
30 minutes
LEVEL
All
Overview
This project is a beginner-friendly Python and Machine Learning application focused on building a linear regression model to analyze and predict future stock prices. We’ll learn how to use the Quandl package to read Stock Data, create our testing and training datasets and standardize our input data, fit the linear regression model and visualize the predicted results of our prices with MatPlotLib!
Project Tasks
Welcome to the project!
2 min
Applying Linear Regression
Build a Linear Regression Model with SKLearn, load and analyze Stock Price data, and predict stock prices 30 days in the future!
7 min
Visualizing our Predicted Results
Build a Linear Regression Model with SKLearn, load and analyze Stock Price data, and predict stock prices 30 days in the future!
6 min
Gathering our Financial Data
Build a Linear Regression Model with SKLearn, load and analyze Stock Price data, and predict stock prices 30 days in the future!
8 min
Processing our Train and Test Data
Build a Linear Regression Model with SKLearn, load and analyze Stock Price data, and predict stock prices 30 days in the future!
9 min
Hey! I’m Avi, and I’ll teach you how to build!
I'm Avi, your new Python instructor and founder of The Codex.
Three years ago I was stuck. I wasn’t learning anything in my theory-filled CS classes, my friends took pictures of me sleeping in lecture, and I was questioning why I was even learning CS to begin with. I had ideas but I was clueless on execution.
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UC Berkeley, 2024
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