The Hello World of ML: Building Your First Linear Regression Model
The Core Concept In traditional programming (like app development), you write the rules to get an answer: Input + Rules = Output In Machine Learning, you flip this. You give the computer the answer, and it figures out the rules: Input + Output = Rules Today, we will build a model that predicts a salary based on years of experience. We aren't going to write the math; we are going to let the machine learn the pattern. The Mission We have a dataset of employees with two columns: Years of Experience (The Feature / Input) Salary (The Label / Output) Our goal is to train a model that can take a new number (e.g., 5.5 years) and predict the salary, even if that specific number wasn't in our original data. The Setup Ensure you have your environment ready (as discussed in the previous post). Create a new file named salary_predictor.py or open a new Jupyter Notebook. The Code Here is the complete, minimal code to train your first model. Deconstructing the Magic 1. Data Preparation (...