Sign Language Classification

🏆 Accuracy: %93.54

Overview

This project focuses on classifying sign language images representing the numbers 0 and 1. The model achieves an impressive accuracy rate of 93.54%. This project has applications in sign language recognition and communication.

Dataset

The dataset used for this project contains images of hand signs for the numbers 0 and 1 in sign language. It includes two categories, one for each number. Data Link: Kaggle Link**

Project Structure

The project is organized into the following sections:

1. Library and Input Files: In this section, we import the necessary Python libraries and load the dataset.

2. Data Loading and Visualization: We load the sign language dataset and provide visualizations of the data.

3. Data Preprocessing: The dataset is preprocessed and organized for model training.

4. Train-Test Split: We split the data into training and testing sets for model evaluation.

5. Flatten Operation: Images are flattened to prepare them for training.

6. Transpose Operation: Data is transposed to match the required dimensions.

7. Initialize Weights and Bias: We initialize the weights and bias for logistic regression.

8. Sigmoid Function: The sigmoid function is defined, which is a crucial part of logistic regression.

9. Forward-Backward Propagation: Forward and backward propagation steps are explained for logistic regression.

10. Updating Parameters: The model parameters, including weights and bias, are updated using the gradient descent algorithm.

11. Prediction: We make predictions using the trained model.

12. Logistic Regression Algorithm: The logistic regression algorithm is executed, and results are displayed.

Requirements

To run the project, make sure you have the following Python libraries installed:

• NumPy
• matplotlib
• pandas
• scikit-learn

You can install these libraries using pip:

pip install numpy
pip install matplotlib
pip install pandas
pip install scikit-learn

Getting Started

Installation

1. Clone the project repository:

git clone [https://github.com/Prometheussx/Sign-Language-Classification-Tutorial]
cd Sign-Language-Classification

2. Ensure you have Python and the required libraries installed.

Data Preparation

1. Download the sign language dataset containing images of the numbers 0 and 1 in sign language.

2. Follow the code in the "Data Loading and Visualization" section to load and visualize the dataset.

3. Data Link: Kaggle Link

Usage

Training the Model

logistic_regression(x_train, y_train, x_test, y_test,learning_rate=0.01 ,num_iterations = 150)

Evaluation

The model's performance is evaluated with metrics such as accuracy, and a sample image with the predicted number is displayed.

Results

The project achieves an accuracy of 93.54% in classifying sign language images of numbers 0 and 1. You can monitor the training progress and results by running the provided code.

Author

• Email: Your_Email_Address
• LinkedIn Profile: Your LinkedIn Profile
• GitHub Profile: Your GitHub Profile
• Kaggle Profile: @erdemtaha

Feel free to reach out if you have any questions or need further information about the project.