develop a ml model to classify handwritten digits in given dataset

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Develop a machine learning model to classify handwritten digits in a given dataset.

Preprocess the dataset by resizing images, normalizing pixel values, and splitting into training and testing sets.
Choose a suitable classification algorithm such as Support Vector Machine (SVM) or Convolutional Neural Network (CNN).
Train the model on the training set and evaluate its performance using the testing set.
Tune hyperparameters to improve model accuracy and generalization.
Use techniques like data augmentation to enhance model performance.
Example: Use the MNIST dataset which consists of 28x28 pixel images of handwritten digits.
Example: Implement a CNN with multiple convolutional and pooling layers for better feature extraction.

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