Fruit Classification using PCA and Various Classifiers

[Varunshiyam]

Related Issues or bug

The project aims to address the challenges associated with high-dimensional data in image classification, such as:

• High Computational Cost: Processing large image datasets can be computationally intensive.
• Feature Redundancy: High-dimensional data often includes redundant features that do not contribute to model accuracy.
• Model Overfitting: Increased complexity can lead to overfitting, where the model performs well on training data but poorly on unseen data.

Fixes: #872

Proposed Changes

This project is centered around the classification of different types of fruit images using Principal Component Analysis (PCA) for dimensionality reduction and applying various machine learning algorithms for classification, such as:

• Support Vector Machine (SVM)
• k-Nearest Neighbors (KNN)
• Decision Tree Classifier

[vercel]

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Name	Status	Preview	Comments	Updated (UTC)
ml-nexus	✅ Ready (Inspect)	Visit Preview	💬 Add feedback	Nov 10, 2024 11:02am

[github-actions]

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[github-actions]

🎉🎉 Thank you for your contribution! Your PR #875 has been merged! 🎉🎉