My First Step into Machine Learning: Setting Up and Building a Predictor When I first decided to dive into Machine Learning (ML), I thought the hardest part would be the complex math. It turns out, the real "Step 1" is much more practical: setting up a clean workspace. In this post, I’m sharing the exact workflow I used to set up my Python environment and build a simple linear regression model to predict house prices. Whether you are a student or a developer, this is the foundational "Hello World" of the ML world. The Goal: From Zero to Prediction The code I’ve shared today accomplishes two main things: Environment Stability: It uses a virtual environment to ensure our ML libraries (like Scikit-Learn and Pandas) don't crash into other Python projects on our system. Smart Prediction: It builds a Linear Regression model. By looking at a list of house sizes and their prices, the code "learns" the relationship between them. Once trained, you can feed it...