Handwritten Digit Prediction with Machine Learning

Welcome to the Handwritten Digit Prediction repository! This project demonstrates how to build and train a machine learning model to classify handwritten digits from the MNIST dataset.

Overview

This repository contains a Jupyter Notebook (.ipynb file) which walks through the process of building a machine learning model for handwritten digit classification. The notebook utilizes popular libraries and techniques to preprocess the data, train a model, and evaluate its performance.

Contents

• Handwritten_Digit_Prediction.ipynb: A Jupyter Notebook containing the complete workflow for digit prediction, including data loading, preprocessing, model training, and evaluation.

Getting Started

To get started with this project, you'll need to set up your environment and install the necessary dependencies. Follow the instructions below:

Prerequisites

You'll need to install the following Python packages:

• numpy
• pandas
• matplotlib
• seaborn
• scikit-learn
• tensorflow (or keras if you're using an older version)
• jupyter (for running the notebook)

You can install these dependencies using pip. Here's a sample command to install them:

bash pip install numpy pandas matplotlib seaborn scikit-learn tensorflow jupyter

Running the Notebook

1. Clone this repository to your local machine:

   bash git clone https://github.com/ch-ankit679/handwritten_digit_prediction.git

2. Navigate to the repository directory:

   bash cd handwritten_digit_prediction

3. Launch Jupyter Notebook:

   bash jupyter notebook

4. In the Jupyter Notebook interface, open the Handwritten_Digit_Prediction.ipynb file.

5. Follow the instructions in the notebook to run the code cells and explore the handwritten digit prediction model.

Notebook Overview

The Handwritten_Digit_Prediction.ipynb notebook covers the following steps:

1. Data Loading: Import the MNIST dataset.
2. Data Preprocessing: Normalize and prepare the data for training.
3. Model Building: Create a machine learning model (e.g., a neural network) to predict digits.
4. Model Training: Train the model on the training data.
5. Evaluation: Evaluate the model's performance on the test data and visualize results.
6. Prediction: Use the trained model to make predictions on new data.

Contributing

If you'd like to contribute to this project, please fork the repository and create a pull request with your proposed changes. Make sure to follow the coding standards and include relevant tests.

Contact

If you have any questions or need further assistance, please feel free to open an issue in this repository or contact me directly at ch.ankit679@gmail.com.

Happy coding!