Project Overview: Image Classification with ANN and Tkinter This project demonstrates an image classification application leveraging an Artificial Neural Network (ANN) for predictive modeling, coupled with a user-friendly graphical interface built using Tkinter. The backend of the application employs scikit-learn for efficient machine learning workflows and NumPy for robust numerical computations.

Key Features:

Artificial Neural Network (ANN): Utilizes a neural network model to classify images into predefined categories, trained on a dataset of choice. This ANN is optimized for accuracy and efficiency. Tkinter Interface: Provides an intuitive graphical user interface (GUI) that allows users to upload images and view classification results seamlessly. scikit-learn Integration: Implements data preprocessing, model training, and evaluation with scikit-learn, ensuring a streamlined machine learning pipeline. NumPy for Data Handling: Employs NumPy for handling image data and performing numerical operations crucial for preprocessing and model evaluation. How to Use:

Clone the repository from GitHub. Install the required dependencies using pip. Run the Tkinter application to interact with the ANN model. This project is ideal for those looking to explore the integration of neural networks with graphical interfaces and practical applications in image classification.