This repository contains a Jupyter Notebook for a hand-written digit prediction project using classification analysis. The notebook explores the popular MNIST dataset and implements a machine learning model to predict the handwritten digits.
The Hand Written Digit Prediction - Classification Analysis project aims to demonstrate how to build a machine learning model that can accurately predict handwritten digits. It uses the MNIST dataset, which is a widely used benchmark dataset for image classification tasks.
The Jupyter Notebook in this repository provides step-by-step instructions and code snippets to guide you through the process of analyzing the dataset, preprocessing the images, training the model, and evaluating its performance.
The project utilizes the MNIST dataset, which is included in the sklearn.datasets module. The dataset consists of 70,000 images of handwritten digits from 0 to 9, with each image represented as a 28x28 grayscale array.
To run the notebook and experiment with the code, follow these steps:
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Clone this repository to your local machine using the following command: git clone https://github.com/alzx1/Hand-Written-Digit-Prediction.git
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Ensure you have the necessary dependencies installed. You can install them using pip: pip install -r requirements.txt
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Launch Jupyter Notebook: jupyter notebook
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Open the
Hand Written Digit Prediction - Classification Analysis.ipynbnotebook in Jupyter.
The notebook provides detailed explanations of each step involved in the classification analysis, including:
- Data loading and exploration
- Data preprocessing and feature scaling
- Splitting the dataset into training and testing sets
- Training different classification models (e.g., logistic regression, support vector machines)
- Evaluating and comparing the model performance
- Making predictions on new handwritten digits
Follow the instructions in the notebook cells to execute the code and observe the results. Feel free to modify the code and experiment with different approaches or models.
Contributions to this project are welcome. If you have any suggestions, bug reports, or improvements, please open an issue or submit a pull request. We appreciate your contributions!
This project is licensed under the MIT License.