This project aims to classify handwritten digits using a random forest classifier algorithm. By analyzing the provided dataset of handwritten digits, the model can accurately predict the digit represented in the image.
This repository contains the code and resources necessary to build and train the random forest classifier model for digits classification.
- Random Forest Classifier: The project utilizes the random forest classifier algorithm, a popular machine learning algorithm used for classification tasks, to classify handwritten digits.
- Feature Extraction: The dataset of handwritten digits is preprocessed and features are extracted to ensure optimal performance of the model.
- Model Training and Evaluation: The random forest classifier model is trained on a subset of the dataset and evaluated using appropriate evaluation metrics such as accuracy and F1 score.
- Model Testing: The trained model is then used to classify new, unseen images of handwritten digits.
- Model Deployment: The trained model can be deployed in a production environment or integrated into an application to classify handwritten digits based on input images.
- Python: The primary programming language used for building and training the random forest classifier model.
- Scikit-learn: A popular machine learning library in Python used for implementing the random forest classifier algorithm and performing model evaluation.
- NumPy: A library for numerical computations used for handling and manipulating numerical data.
- Matplotlib: A plotting library used for data visualization and creating graphical representations of the model's performance.
To get started with the Digits Classification using Random Forest Classifier project, follow these steps:
- Clone this repository:
git clone https://github.com/shaadclt/Digits-Classification.git - Navigate to the project directory:
cd Digits-Classification - Open the jupyter notebook file:
Digits Classification.ipynb - Explore the generated results, including evaluation metrics and visualizations.
Note: Make sure you have Python and pip installed on your system before proceeding with the above steps.
The dataset used for this project is the MNIST dataset, which consists of 60,000 training images and 10,000 testing images of handwritten digits (0-9).
Contributions to this project are welcome and encouraged. If you have any ideas for improvements or would like to add new features, feel free to open an issue or submit a pull request. Make sure to follow the project's code style and adhere to best practices.
This project is licensed under the MIT License. You are free to use and modify the code as per the terms of the license.
This project was inspired by the need to classify handwritten digits accurately. Special thanks to the open-source community for providing the necessary tools and libraries for machine learning and data analysis.
If you have any questions or inquiries about this project, feel free to contact the project maintainer at shaadclt@gmail.com.