🖋️ MNIST Digit Classification (Keras + CNN)

This project implements a Convolutional Neural Network (CNN) to classify handwritten digits (0–9) from the MNIST dataset.
The model is built using TensorFlow/Keras and achieves high accuracy on the test set.

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📂 Project Structure

├── MNIST_Digit_Recognition.ipynb # Main Jupyter Notebook ├── README.md # Project documentation

⚙️ Requirements

Make sure you have the following installed:

• Python 3.8+
• TensorFlow / Keras
• NumPy
• Matplotlib
• Jupyter Notebook

Install dependencies with: pip install tensorflow numpy matplotlib jupyter

How to Run

Open the notebook Run all cells step by step: Step 1: Import libraries Step 2: Load dataset Step 3: Preprocess & normalize data Step 4: Build CNN model Step 5: Train the model Step 6: Evaluate on test data Step 7: Save/Load model (optional) Step 8: Final predictions

Model Architecture

Input: 28 x 28 x 1 grayscale image Conv2D → ReLU → MaxPooling Flatten → Dense(128, ReLU) Dense(10, Softmax)

Results

Accuracy on test set: ~98% The model successfully classifies handwritten digits with high accuracy.