Developing a Model for Egyptian Money Classification Using Machine Learning 95% accuracy

This project focuses on developing a machine learning model specifically tailored for Egyptian money classification. By leveraging state-of-the-art algorithms and a comprehensive dataset of Egyptian currency, the model aims to achieve high precision in distinguishing different denominations. The model not only addresses the practical challenges faced in real-world applications but also contributes to the growing field of financial technology in Egypt.

Through this project, we aim to:

Gather and preprocess a diverse dataset of Egyptian currency notes and coins.

Explore various machine learning techniques to identify the most effective approach for currency classification.

Implement and evaluate the performance of the developed model using standard metrics.

Provide insights and recommendations for future improvements and potential applications.

The successful completion of this project will demonstrate the feasibility and benefits of applying machine learning to currency classification, paving the way for more advanced and automated financial systems in Egypt.

Overview of Data

Data Source- Get it From Kaggle

Data Collection and Preprocessing:

Gathered images of various Egyptian currency notes and coins.

Labeled the images according to their respective denominations.

Applied preprocessing techniques such as resizing, normalization, and data augmentation to enhance the dataset.

Model Development:

Utilized the InceptionV3 architecture, a deep convolutional neural network known for its effectiveness in image recognition tasks.

Fine-tuned the pre-trained InceptionV3 model on the collected dataset.

Implemented techniques such as transfer learning to improve the model’s performance.

Evaluation:

Split the dataset into training and validation sets.

Evaluated the model using metrics such as accuracy, precision, recall, and F1-score.

Analyzed the confusion matrix to understand the model’s performance across different classes.