https://github.com/vignesh010101/cats-and-dogs-classifier

Implementation of support vector machine (SVM) to classify images of cats and dogs from the Kaggle dataset.
https://github.com/vignesh010101/cats-and-dogs-classifier

Last synced: 3 days ago
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Implementation of support vector machine (SVM) to classify images of cats and dogs from the Kaggle dataset.

• Host: GitHub
• URL: https://github.com/vignesh010101/cats-and-dogs-classifier
• Owner: Vignesh010101
• Created: 2024-04-19T02:22:49.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-05-24T16:03:26.000Z (7 months ago)
• Last Synced: 2024-11-06T12:18:19.335Z (about 2 months ago)
• Language: Jupyter Notebook
• Size: 362 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Cats and Dogs Classifier

Implementation of support vector machine (SVM) to classify images of cats and dogs from the Kaggle dataset.

Project Description: Using the Kaggle dataset to use a Support Vector Machine (SVM) to classify images of cats and dogs. The goal is to develop a robust image classification model that can distinguish between these two groups.

Full instructions:

Data Analysis: Identify and understand patterns in the Kaggle dataset containing cat and dog images.

Data preprocessing: Perform preprocessing operations such as image resizing, normalization, and classification of datasets for training and testing.

Feature extraction: Extract relevant features from the image as input to the SVM model.

Model implementation: Design and train a support vector machine model for image classification using feature selection.

Model evaluation: Evaluate the model's performance in a separate test by determining parameters such as accuracy, precision, recall, and F1 score.

Fine-tuning: Explore hyperparameter tuning to optimize the SVM model for better performance.

Knowledge gained:

Image Classification: Learn how to use machine learning to create image classification models.

Support Vector Machine (SVM): An SVM that can perform efficient and effective binary classification function.

Data Preprocessing: Improve capabilities to prepare image data for machine learning, including resizing, normalization, and dataset segmentation.

Model Evaluation: Learn how to use basic metrics to evaluate model performance and interpret results for image classification.

Kaggle dataset: Learn real data on Kaggle construction and get a deep understanding of the issues and precautions in making datasets.

Work done during the machine learning internship at Prodigy Infotech.