MNIST Handwritten Digit Classification using Machine Learning

Built a machine learning model to classify handwritten digits with high accuracy (~95%)

:Steps

Loaded and preprocessed the data

Applied feature scaling techniques

Trained a neural network classifier

Evaluated model performance

Application: Demonstrates ability to build and optimize ML models, which can be applied to agricultural datasets such as crop classification or disease detection