BeginnerPythonAI

Train Your First Machine Learning Model in Python

Build, evaluate and interpret a regression model on a small engineering dataset.

Estimated completion: 35 min

Objective

Train a baseline regression model and evaluate it honestly with a held-out test set.

1
Load and inspect the data
Read the CSV with pandas and check dtypes, missing values and obvious outliers before modelling.
2
Split the data
Hold out 20% of the data for testing before doing anything else, so preprocessing choices cannot leak information.
3
Fit a baseline
Start with linear regression. A baseline tells you whether a complex model is actually earning its complexity.
4
Try a stronger model
Fit a random forest and compare cross-validated error against the baseline.
5
Evaluate and interpret
Report MAE and RMSE, plot residuals against predictions and inspect feature importances.

A model with documented test error and a residual plot showing no obvious structure.

Problem	How to fix it
Test error much worse than training error	Reduce model complexity or gather more data; you are overfitting.
Suspiciously perfect scores	Check for target leakage in your features.