Another intro project to ML is the California Housing dataset. Below is a thorough guide on what a Linear Regression model can look like 👇

Curious about the first ML project most people try?

Here’s a step-by-step breakdown of how to apply classification to the Iris dataset — one of the most used datasets in machine learning

1) A mini ML-project using linear regression on predicting the price of silver

Building on this. Predicted stock value for Amazon using test data from 25-04-2023 to 03-03-2025. Input features: Lagging 1, 2 and 5 days - regression model predicts stock price based on 1, 2 and 5 previous days.
MAPE = 1.37%
R^ = 0.9919

Will try to post sideprojects more regularly, so feel free to follow me if you have any interest in coding, data viz, and analytics within random fields

While I'm skeptical of technical analysis for stocks, I recently wrote some code exploring Amazon's stock data from 2015-todays date

Whole thing is done in VsCode using Pandas (+ div libraries), so the viz and graphics are meh 👇