Using Matplotlib in Machine Learning Projects for Visual Insights
Plots are useful before and after modelling: first to understand the data, then to explain what the model is doing and where it fails.
Useful Visualisations Before Modelling
- Histograms for distributions
- Scatter plots for relationships
- Bar charts for class counts
- Box plots for outliers
- Missing-value summaries
Useful Visualisations After Modelling
- Confusion matrix visualisation
- Residual plots for regression
- Feature-importance charts when the model supports them
- Prediction-vs-actual plots
- Learning curves where appropriate
Avoid Misleading Charts
- Label axes and units
- Do not hide relevant ranges
- Use consistent scales when comparing charts
- Explain sample size and missing data
- Do not imply causation from a visual correlation alone