MNIST-Handwritten-Digit-Clasification

This project demonstrates the implementation of a neural network for classifying handwritten digits from the MNIST dataset using TensorFlow and Keras

Dependencies

• Python 3.12.5
• NumPy
• Matplotlib
• Seaborn
• OpenCV
• PIL
• TensorFlow
• Keras

Installation

1. Clone the repository:

   git clone <repository-url>
   cd <repository-directory>
   

2. Install the required packages
3. Run the cells sequentially to execute the code
4. Enter the path of the image to be predicted when prompted

Notebook Overview

1. Importing Dependencies:

• Import necessary libraries such as NumPy, Matplotlib, Seaborn, OpenCV, PIL, TensorFlow, and Keras

2. Loading the MNIST Dataset:

• Load the MNIST dataset using Keras' built-in dataset loader

3. Data Preprocessing:

• Normalize the pixel values of the images to the range [0, 1]
• Reshape the data as required by the neural network

4. Building the Neural Network:

• Define a sequential model with layers including Flatten, Dense, and Activation layers

5. Compiling the Model:

• Compile the model using the Adam optimizer and sparse categorical cross-entropy loss function

6. Training the Model:

• Train the model on the training data for a specified number of epochs

7. Evaluating the Model:

• Evaluate the model's performance on the test data
• Display the accuracy and loss

8. Making Predictions:

• Use the trained model to make predictions on the test data
• Convert prediction probabilities to class labels

9. Visualizing Results:

• Display sample images from the test set along with their predicted and true labels.
• Plot the confusion matrix to visualize the model's performance

The model achieves a high accuracy on both the training and test datasets