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4-animals-classification

6-places-classification

Our project focuses on leveraging state-of-the-art machine learning techniques, specifically MobileNet transfer learning, to develop a robust image classification model capable of distinguishing between six distinct environmental categories. The targeted classes include 'buildings,' 'forest,' 'glacier,' 'mountain,' 'sea,' and 'street.'

binary-classification-bike-buyer

This Project demonstrates the classification of bike buyers using various machine learning models. It includes data preprocessing, model implementation (Decision Tree, Logistic Regression, and Neural Network), and evaluation of model accuracies. The notebook provides a comprehensive guide for bike buyer classification

dcars

Diagnostic Center Automated Reception System

esssyjr

Config files for my GitHub profile.

multi-class-classification-of-dry-beanss

This project aims to classify different types of dry beans using a Neural Network model trained on the Dry Bean Dataset. The Dry Bean Dataset contains various features of dry beans, such as area, perimeter, major and minor axis lengths, and shape factors. By leveraging the power of TensorFlow, the model achieves an accuracy of 92% on test data.

sports_classification

Sports Classifier, a cutting-edge computer vision task, harnesses the power of MobileNet transfer learning to accurately classify images across 100 sports categories.

surface_crack_classifications

Welcome to the Surface Crack Classifier project, a sophisticated computer vision model designed to address the critical task of classifying surface images into two distinct categories: surfaces with cracks and those without.