Handwritten Digit Classification with Keras and TensorFlow

**What the Project Does **Data Utilization: Utilizes the MNIST dataset, which contains 70,000 grayscale images of handwritten digits (0-9), each 28x28 pixels. Model Building: Constructs a neural network model using Keras. The model includes layers such as convolutional layers, activation functions, and dense layers to process and classify the images. Training and Evaluation: Trains the model on the MNIST training dataset and evaluates its performance on the test dataset to measure accuracy. Performance Optimization: Implements techniques to enhance the model’s performance, such as adjusting hyperparameters and using dropout regularization to prevent overfitting. Results: Achieves high accuracy in digit classification, demonstrating the effectiveness of the neural network in recognizing and categorizing handwritten digits.