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.