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model-evaluation-metrics

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text-to-image-eval

Evaluate custom and HuggingFace text-to-image/zero-shot-image-classification models like CLIP, SigLIP, DFN5B, and EVA-CLIP. Metrics include Zero-shot accuracy, Linear Probe, Image retrieval, and KNN accuracy.

  • Updated Jul 29, 2024
  • Jupyter Notebook

This repository contains a project showcasing Federated Learning using the EMNIST dataset. Federated Learning is a privacy-preserving machine learning approach that allows a model to be trained across multiple decentralized devices or servers holding local data samples, without exchanging them.

  • Updated Sep 24, 2023
  • Jupyter Notebook

This breast cancer diagnosis project evaluates various machine learning models to effectively classify breast masses as benign or malignant. SVM and Logistic Regression excel in identifying positive cases, leveraging their robust performance metrics, while Neural Networks show promising results and offer opportunities for further enhancement!

  • Updated Jun 21, 2024
  • Jupyter Notebook

Successfully established a machine learning model which can determine whether an individual is vulnerable to the Cirrhosis disease or not by predicting its corresponding stage based on a unique set of medical features such as Cholesterol, Prothrombin, etc. pertaining to that person.

  • Updated Jun 8, 2022
  • Jupyter Notebook

A spam detection model built to handle imbalanced data using small pipelines. This project walks through text preprocessing, model tuning, and performance evaluation with ROC-AUC curves and classification reports, focusing on practical steps like using XGBoost and TFIDF for spam classification.

  • Updated Sep 7, 2024
  • Jupyter Notebook

🗣️ Speech Type Detection is a Flask app to classifies text into categories like "Hate Speech," "Offensive Language," or "No Hate or Offensive Language" with 87.3% accuracy. It offers a user-friendly interface for text input and prediction, using machine learning algorithms. Idea for managing online inappropriate language. 🌐🔍.

  • Updated Jun 5, 2024
  • Python

This project explores the use of machine learning for fungi classification, developed for the Brisbane Flora and Fauna Society. It combines a DINOv2 Vision Transformer for feature extraction with a custom linear classifier, leveraging transfer learning to classify edible and poisonous fungi species from images.

  • Updated Nov 13, 2024
  • Jupyter Notebook

This project focuses on building a model to predict house prices in California using various features such as location, size, and number of bedrooms. The project includes data cleaning, feature engineering, and model training with Linear Regression and Random Forest algorithms.

  • Updated Jul 8, 2024
  • Jupyter Notebook

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