Classification of Pet's Face

This repository contains a project focused on the classification of pet faces using machine learning techniques. The goal of this project is to identify and classify images of pets (e.g., dogs and cats) into specific categories based on their facial features.

Project Overview

The project is implemented in a Jupyter Notebook (Classification_of_Pet's_Face.ipynb) and includes the following steps:

1. Data Preparation:

   • Loading and preprocessing a dataset of pet images.
   • Performing data augmentation to enhance model performance.

2. Model Development:

   • Designing and training a machine learning or deep learning model (e.g., CNN).
   • Using popular libraries such as TensorFlow or PyTorch.

3. Evaluation:

   • Analyzing model performance using metrics like accuracy, precision, recall, and confusion matrix.
   • Visualizing the results through graphs and plots.

4. Deployment (optional):

   • Preparing the model for deployment in real-world applications.

   Dependencies

To run the notebook, the following installed:

• Python 3.7+
• Jupyter Notebook
• NumPy
• Pandas
• Matplotlib
• TensorFlow or PyTorch (depending on the model used)
• Scikit-learn

Getting Started

1. Clone this repository:

   git clone https://github.com/Ananya48/Classification-of-Pet-s-Face/blob/main/Classification_of__Pet's__Face.ipynb

Dataset

Ensure you have a dataset of pet images. The dataset should be organized in the following structure:

/dataset
    /class_1
        image1.jpg
        image2.jpg
        ...
    /class_2
        image1.jpg
        image2.jpg
        ...

You can either use an existing dataset (e.g., Kaggle) or prepare your own.