Recognizing Handwritten Digits

Recognizing Handwritten Digits

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In this project, our primary objective will be to delve into the identification of handwritten digits through the application of machine learning algorithms. This endeavor aims to harness the power of machine learning to effectively interpret and understand handwritten digits, showcasing the potential of these algorithms in processing and analyzing complex visual information.

Visualization

Index Distribution

Train Test Split

Classification Report

Confusion Matrix

Radius Neighbors Classifier

Course Content

Recognizing Handwritten Digits

Recognizing Handwritten Digits

Introduction Dataset Import Visualization Class Balance Index Distribution Train Test Split KNN Classification Report Confusion Matrix Radius Neighbors Classifier

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Visualization

Our dataset consists of various rows, each depicting pixel values on a grey scale ranging from 0 to 255. Let's display a few of these images.

Task

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Extract the feature data (images) from the MNIST dataset and store it in variable X.
Loop through the first 7 images in the dataset and visualize each image using seaborn's heatmap function. Also you need to reshape each image to a 2D array of 28x28 pixels.

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Section 1. Chapter 3

AVAILABLE TO ULTIMATE ONLY

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