Confusion Matrix & Heat map//Explanation// making in code //

In this video, I explain the Confusion Matrix in a very simple and practical way using a real Dog vs Not Dog example.
We start from the basics and understand how a model makes correct and wrong predictions.

You will clearly learn what True Positive, False Positive, True Negative, and False Negative actually mean instead of just memorizing definitions.
I also show how a confusion matrix is created using Python, scikit-learn, Seaborn, and how to visualize it using a heatmap.

This video is perfect for beginners who are learning Machine Learning, Data Science, or AI and want to understand model evaluation without confusion.
If you ever felt confused by accuracy, precision, recall, or the confusion matrix itself, this video will clear your concepts step by step.

Watch till the end to fully understand how classification models are evaluated in real projects.

Timestamp-
00:00 Introduction
00:55 About confusion matrix
08:15 Confusion matrix code
25:44 Heatmap

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