{"id":23828583,"url":"https://github.com/imnotamr/ai","last_synced_at":"2025-10-03T21:51:41.532Z","repository":{"id":259994089,"uuid":"878502762","full_name":"imnotamr/AI","owner":"imnotamr","description":"A collection of machine learning and AI projects implemented in Jupyter notebooks, covering regression, classification, and neural networks","archived":false,"fork":false,"pushed_at":"2025-02-04T11:08:31.000Z","size":21429,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-04T12:22:12.671Z","etag":null,"topics":["ai","classification","colab-notebook","data-analysis","data-preprocessing","data-preprocessing-and-cleaning","data-visualization","deep-learning","deep-neural-networks","jupyter-notebook","machine-learning","model-evaluation","predictive-modeling","project-based-learning","python","supervised-learning","supervised-learning-algorithms","supervised-learning-classifiers","unsupervised-learning","unsupervised-learning-algorithms"],"latest_commit_sha":null,"homepage":"https://github.com/imnotamr/AI","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/imnotamr.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}},"created_at":"2024-10-25T14:08:17.000Z","updated_at":"2025-02-04T11:08:34.000Z","dependencies_parsed_at":"2024-10-29T01:22:08.188Z","dependency_job_id":"91fcf044-f986-4616-8fb9-b3b45d05c5b2","html_url":"https://github.com/imnotamr/AI","commit_stats":null,"previous_names":["imnotamr/ai"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/imnotamr%2FAI","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/imnotamr%2FAI/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/imnotamr%2FAI/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/imnotamr%2FAI/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/imnotamr","download_url":"https://codeload.github.com/imnotamr/AI/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":240115846,"owners_count":19750087,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":["ai","classification","colab-notebook","data-analysis","data-preprocessing","data-preprocessing-and-cleaning","data-visualization","deep-learning","deep-neural-networks","jupyter-notebook","machine-learning","model-evaluation","predictive-modeling","project-based-learning","python","supervised-learning","supervised-learning-algorithms","supervised-learning-classifiers","unsupervised-learning","unsupervised-learning-algorithms"],"created_at":"2025-01-02T13:18:55.687Z","updated_at":"2025-10-03T21:51:36.512Z","avatar_url":"https://github.com/imnotamr.png","language":"Jupyter Notebook","readme":"# AI Projects Repository\nWelcome to my AI Projects repository! This collection includes various machine learning and artificial intelligence projects demonstrating different algorithms, techniques, and applications. Each project is implemented in Jupyter notebooks and showcases practical uses of AI in real-world scenarios.\n\n## 📁 Project Structure ##\nThis repository contains the following projects:\n\n• Player's Name Prediction\nA project focused on predicting player names using ML techniques. This project may involve NLP techniques or other supervised learning methods.\n\n• Adult (Full Project)\nAn extensive analysis of the UCI Adult dataset to predict income levels based on various demographic factors. Techniques include data preprocessing, feature engineering, and model evaluation.\n\n• Classification \u0026 Regression Projects\nA combination of supervised learning projects involving both classification and regression tasks. Examples include predicting house prices, categorizing images, etc.\n\n• K-Nearest Neighbors (KNN) Project\nA dedicated project exploring the K-Nearest Neighbors algorithm and its applications. This project includes data preprocessing, model training, and parameter tuning.\n\n• Linear Regression\nA classic approach to regression analysis focusing on predicting continuous values. This project demonstrates linear regression techniques, model evaluation metrics, and visualization of results.\n\n• Regression \u0026 Classification (Multiple Projects)\nA compilation of additional regression and classification projects showcasing different datasets and approaches, each with a unique problem statement and solution.\n\n• Support Vector Machine (SVM) Project\nA project based on the SVM algorithm, demonstrating its use in classification tasks with feature scaling, hyperparameter tuning, and model evaluation.\n\n• COVID-19 Prediction using MLP-CNN\nAn advanced project combining Multi-Layer Perceptron (MLP) and Convolutional Neural Networks (CNN) for predicting COVID-19 cases or outcomes using medical imaging or time-series data.\n\n• Deep Learning - Iris and Titanic Datasets\nA project showcasing the use of deep learning for classification tasks on well-known datasets (Iris and Titanic). It includes techniques like data preprocessing, model building, and evaluation.\n\n## 🛠️ Technologies \u0026 Tools Used ##\nPython: Core programming language for all projects.\nJupyter Notebook: Interactive notebooks for code and visualization.\nMachine Learning Libraries: Scikit-learn, TensorFlow, and Keras for model building and evaluation.\nData Visualization: Matplotlib and Seaborn for plotting and visual analysis.\nPandas \u0026 NumPy: Data manipulation and analysis.\n\n## 📈 Project Highlights ##\nExploratory Data Analysis (EDA): Each project includes EDA to uncover insights and patterns in the data.\nModel Training \u0026 Evaluation: Projects involve training various models and evaluating their performance using metrics like accuracy, precision, recall, MAE, and MSE.\nFeature Engineering: Custom feature extraction techniques to improve model performance.\nParameter Tuning: Hyperparameter optimization using techniques such as GridSearchCV and RandomizedSearchCV.\n\n## 🤖 Future Improvements ##\nAdd more advanced deep learning models.\nEnhance data preprocessing techniques for better performance.\nExplore additional datasets for broader applications.\n## 💡 Contributing ##\nContributions are welcome! If you have improvements, bug fixes, or new project ideas, feel free to submit a pull request.\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fimnotamr%2Fai","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fimnotamr%2Fai","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fimnotamr%2Fai/lists"}