{"id":34851451,"url":"https://github.com/shiningflash/machine-learning","last_synced_at":"2026-05-06T04:07:39.453Z","repository":{"id":111949848,"uuid":"281857299","full_name":"shiningflash/Machine-Learning","owner":"shiningflash","description":"Explore practical machine learning projects, from predicting taxi fares to visualizing neural networks, making AI concepts simple and accessible for everyone!","archived":false,"fork":false,"pushed_at":"2024-11-26T00:27:19.000Z","size":1942,"stargazers_count":0,"open_issues_count":1,"forks_count":1,"subscribers_count":0,"default_branch":"master","last_synced_at":"2025-12-27T04:19:42.522Z","etag":null,"topics":["data-science","deep-","machine-learning","neural-network","python","visualization","webapp"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/shiningflash.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2020-07-23T05:16:16.000Z","updated_at":"2024-12-01T01:37:53.000Z","dependencies_parsed_at":null,"dependency_job_id":"de4a60c9-2108-4d61-aa8a-8e965404115d","html_url":"https://github.com/shiningflash/Machine-Learning","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/shiningflash/Machine-Learning","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shiningflash%2FMachine-Learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shiningflash%2FMachine-Learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shiningflash%2FMachine-Learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shiningflash%2FMachine-Learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/shiningflash","download_url":"https://codeload.github.com/shiningflash/Machine-Learning/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shiningflash%2FMachine-Learning/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32677958,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-06T02:33:58.958Z","status":"ssl_error","status_checked_at":"2026-05-06T02:33:39.611Z","response_time":117,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-science","deep-","machine-learning","neural-network","python","visualization","webapp"],"created_at":"2025-12-25T19:20:01.040Z","updated_at":"2026-05-06T04:07:39.430Z","avatar_url":"https://github.com/shiningflash.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Machine Learning Projects\n\nWelcome to my collection of machine learning projects! These repositories showcase a range of machine learning and deep learning techniques applied to solve real-world problems, visualize data, and create robust models. Each project is detailed below with its purpose and relevant technologies.\n\n## Projects Overview\n\n### [Neural Network Visualizer and Digit Prediction](https://github.com/shiningflash/Neural-Network-Visualizer-And-Digit-Prediction)\nA dynamic visualizer for understanding how neural networks operate, with an implementation to predict handwritten digits using a trained model. The project combines visualization techniques with interactive tools to demystify neural network layers.\n\n### [NASA Space Apps COVID-19 Challenge](https://github.com/shiningflash/NASA-Space-Apps-Covid-19-Challenge-2020)\nDeveloped for NASA's Space Apps Hackathon 2020, this project explores innovative solutions to COVID-19-related challenges, leveraging machine learning models and data analytics for meaningful insights.\n\n### [Sentiment Analysis using Deep Learning](https://github.com/BONDHU-BOT/Sentiment-Analysis-using-Deep-Learning)\nA deep learning-based approach to analyze sentiment in text data, classifying emotions as positive, negative, or neutral. Techniques include LSTM and GRU models for improved sentiment prediction accuracy.\n\n### [Emotion Detection using Deep Learning](https://github.com/BONDHU-BOT/Emotion-Detection-using-Deep-Learning)\nThis project focuses on detecting emotions such as happiness, anger, and sadness in text using advanced deep learning architectures. Practical applications include chatbots and customer feedback analysis.\n\n### [Intent Classification using Deep Learning](https://github.com/BONDHU-BOT/Intent-Classification-using-Deep-Learning)\nA robust deep learning pipeline for classifying user intent from text inputs. This project is ideal for enhancing conversational AI systems by improving intent detection and response accuracy.\n\n### [Named Entity Recognition using Deep Learning](https://github.com/BONDHU-BOT/Named-Entity-Recognition-using-Deep-Learning)\nAn implementation of Named Entity Recognition (NER) models to extract entities like names, locations, and organizations from text. The project demonstrates how deep learning can streamline NER tasks for NLP applications.\n\n### [Machine Learning Web App using Streamlit](https://github.com/shiningflash/Machine-Learning/tree/master/ML_WebApp)\nA user-friendly web application built with Streamlit to demonstrate various machine learning models in action. It provides an intuitive interface for model selection, predictions, and performance visualization.\n\n### [New York City Taxi Fare Prediction](https://github.com/shiningflash/New-York-City-Taxi-Fare-Prediction)\nA regression-based model predicting taxi fares in New York City using datasets with geospatial and temporal features. This project showcases data preprocessing, feature engineering, and model optimization techniques.\n\n### [Neural Networks and Deep Learning - Coursera Assignment](https://github.com/shiningflash/Machine-Learning/tree/master/Neural%20Networks%20and%20Deep%20Learning%20-%20Coursera/Assignments)\nCoursework assignments from the \"Neural Networks and Deep Learning\" specialization on Coursera. These assignments provide hands-on implementation of neural network concepts, including forward and backward propagation.\n\n---\n\nFeel free to explore the repositories and gain insights into the methodologies and technologies used. Contributions, feedback, and discussions are always welcome!\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshiningflash%2Fmachine-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshiningflash%2Fmachine-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshiningflash%2Fmachine-learning/lists"}