This project aims to create a Handwritten Digit Recognizer using deep learning techniques. We will leverage the MNIST dataset, a widely recognized database of handwritten digits.

Our goal is to develop a Convolutional Neural Network (CNN) model that can accurately classify and identify handwritten digits.

The MNIST dataset comprises a training set of 60,000 handwritten digit examples and a test set of 10,000 examples. Each image in the dataset is associated with a label that represents the correct digit it depicts. For example, an image of the handwritten digit "3" is labeled as "3."

• Language: python
• Libraries: tensorflow , numpy , pandas, matplotlib, seaborn

1. Data Preparation: We will preprocess the dataset, including reshaping the data, converting labels to categorical format, and performing feature scaling.

2. Model Creation: A Convolutional Neural Network (CNN) model will be constructed for digit recognition. The model architecture includes convolutional layers, max-pooling, dropout layers, and fully connected layers.

3. Training: We will train the CNN model using the training set, allowing it to learn and recognize patterns in handwritten digits.

4. Evaluation: The model's performance will be evaluated on the test set, and metrics such as loss and accuracy will be assessed.

5. Prediction: The trained model will be used to make predictions on new, unseen handwritten digit images.

To run this project locally, you will need Python, TensorFlow, Keras, and other relevant libraries.

Clone this repository and follow the instructions in the code comments for data preprocessing, model creation, training, and evaluation.