MIT PhD | IITM | 100K+ LinkedIn | Co-founder Vizuara & Videsh | Making AI accessible for all
“Show me your data plot!” That was the first thing the professor said when I tried to explain my ML model in graduate school at MIT. Not the accuracy. Not the loss curve. Not the architecture. The plot. Over time, I realized, visualization is not the final step of machine learning. It is the first one. Before we build anything we need to understand what we are working with. And to understand it, we need to see it. This week, I taught a lecture on data visualization for ML using Matplotlib, Seaborn, and Plotly on Vizuara's YouTube channel: https://lnkd.in/dQTQYccT We walked through a complete exploratory data analysis (EDA) pipeline, starting with foundational charts and ending with interactive, dynamic visualizations. And through that, I was reminded of a principle I often forget: A good plot does not just summarize your data. A good plot changes what you believe about your data. You are not always building models for yourself. You are building for a client, reviewer or even a policymaker. They will not read your code. They may not understand your metrics. But they will look at your plots. Visualization is what makes machine learning interpretable - not only to others, but to you. And that matters more than ever. -A boxplot reveals whether a feature is skewed. -A scatterplot shows whether it separates your classes. -A correlation heatmap tells you what is redundant. -A violin plot raises questions about fairness. The stack: Matplotlib, Seaborn, Plotly Each of the three libraries plays a different role in the data visualization journey. 1) matplotlib: The bedrock. Sometimes verbose, but it gives you full control. Perfect for plotting model metrics, trends, and comparisons. Think of it as the NumPy of plotting. 2) seaborn: Statistical plotting done right. One-liner plots that look beautiful and convey distributions, relationships, and groups instantly. Use it for EDA - where every plot leads to a new hypothesis. 3) plotly: The bridge to interaction. If you want to share a story, demo a dataset, or explore it dynamically - this is the tool. Interactive histograms, 3D scatter plots, tooltips on hover. Especially powerful for explaining your work to non-technical stakeholders. Data visualization is not about being fancy. It is about being thoughtful. If you cannot explain your dataset visually, you are not ready to model it. If you cannot explain your model’s results visually, you are not ready to defend it. No one ever changed their mind because of an F1-score. But stunning plot? Those make people pause. Those change narratives. As ML gets more complex - with deeper models, larger datasets, and higher stakes - our ability to communicate clearly will matter more, not less. So if you are starting out in ML - start here. Learn to see before you try to predict. The plots will tell you where to go.