{"id":21338623,"url":"https://github.com/zenitsu0509/machine-learning","last_synced_at":"2025-10-15T12:36:06.990Z","repository":{"id":247714824,"uuid":"826644896","full_name":"zenitsu0509/Machine-Learning","owner":"zenitsu0509","description":"This repo have Machine learning algo projects that i have made.","archived":false,"fork":false,"pushed_at":"2025-01-09T13:59:10.000Z","size":1934,"stargazers_count":2,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-28T14:22:49.032Z","etag":null,"topics":["database","machine-learning"],"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/zenitsu0509.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":"2024-07-10T05:30:23.000Z","updated_at":"2025-01-09T13:59:14.000Z","dependencies_parsed_at":"2024-08-25T14:10:31.322Z","dependency_job_id":null,"html_url":"https://github.com/zenitsu0509/Machine-Learning","commit_stats":null,"previous_names":["hackerx400/machine-learning","zenitsu0509/machine-learning"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zenitsu0509%2FMachine-Learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zenitsu0509%2FMachine-Learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zenitsu0509%2FMachine-Learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zenitsu0509%2FMachine-Learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/zenitsu0509","download_url":"https://codeload.github.com/zenitsu0509/Machine-Learning/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248996795,"owners_count":21195785,"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":["database","machine-learning"],"created_at":"2024-11-22T00:19:42.546Z","updated_at":"2025-10-15T12:36:01.966Z","avatar_url":"https://github.com/zenitsu0509.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n \u003ch1\u003eMachine Learning Algorithms Project\u003c/h1\u003e\n\n  \u003ch2\u003eOverview\u003c/h2\u003e\n        \u003cp\u003eThis project explores various machine learning algorithms to solve classification and regression problems. The algorithms implemented include:\u003c/p\u003e\n        \u003cul\u003e\n            \u003cli\u003eAdaBoost\u003c/li\u003e\n            \u003cli\u003eDecision Tree Classifier\u003c/li\u003e\n            \u003cli\u003eK-Nearest Neighbors (KNN)\u003c/li\u003e\n            \u003cli\u003eLinear Regression\u003c/li\u003e\n            \u003cli\u003eLogistic Regression\u003c/li\u003e\n            \u003cli\u003eNaive Bayes\u003c/li\u003e\n            \u003cli\u003eRandom Forest\u003c/li\u003e\n        \u003c/ul\u003e\n\n  \u003ch2\u003eTable of Contents\u003c/h2\u003e\n        \u003cul\u003e\n            \u003cli\u003e\u003ca href=\"#introduction\"\u003eIntroduction\u003c/a\u003e\u003c/li\u003e\n            \u003cli\u003e\u003ca href=\"#datasets\"\u003eDatasets\u003c/a\u003e\u003c/li\u003e\n            \u003cli\u003e\u003ca href=\"#algorithms\"\u003eAlgorithms\u003c/a\u003e\n                \u003cul\u003e\n                    \u003cli\u003e\u003ca href=\"#adaboost\"\u003eAdaBoost\u003c/a\u003e\u003c/li\u003e\n                    \u003cli\u003e\u003ca href=\"#decision-tree-classifier\"\u003eDecision Tree Classifier\u003c/a\u003e\u003c/li\u003e\n                    \u003cli\u003e\u003ca href=\"#k-nearest-neighbors\"\u003eK-Nearest Neighbors\u003c/a\u003e\u003c/li\u003e\n                    \u003cli\u003e\u003ca href=\"#linear-regression\"\u003eLinear Regression\u003c/a\u003e\u003c/li\u003e\n                    \u003cli\u003e\u003ca href=\"#logistic-regression\"\u003eLogistic Regression\u003c/a\u003e\u003c/li\u003e\n                    \u003cli\u003e\u003ca href=\"#naive-bayes\"\u003eNaive Bayes\u003c/a\u003e\u003c/li\u003e\n                    \u003cli\u003e\u003ca href=\"#random-forest\"\u003eRandom Forest\u003c/a\u003e\u003c/li\u003e\n                \u003c/ul\u003e\n            \u003c/li\u003e\n            \u003cli\u003e\u003ca href=\"#usage\"\u003eUsage\u003c/a\u003e\u003c/li\u003e\n            \u003cli\u003e\u003ca href=\"#results\"\u003eResults\u003c/a\u003e\u003c/li\u003e\n            \u003cli\u003e\u003ca href=\"#conclusion\"\u003eConclusion\u003c/a\u003e\u003c/li\u003e\n            \u003cli\u003e\u003ca href=\"#references\"\u003eReferences\u003c/a\u003e\u003c/li\u003e\n        \u003c/ul\u003e\n\n   \u003ch2\u003eIntroduction\u003c/h2\u003e\n        \u003cp\u003eThe aim of this project is to implement and compare various machine learning algorithms on different datasets to evaluate their performance. Each algorithm has been tested on the same datasets to ensure a fair comparison.\u003c/p\u003e\n\n  \n\n   \u003ch2 id=\"datasets\"\u003eDatasets\u003c/h2\u003e\n        \u003cp\u003eThe datasets used in this project are included in the \u003ccode\u003edata\u003c/code\u003e directory. They cover a variety of domains to test the versatility and robustness of the algorithms.\u003c/p\u003e\n \u003ch2 id=\"algorithms\"\u003eAlgorithms\u003c/h2\u003e\n\n   \u003ch3 id=\"adaboost\"\u003eAdaBoost\u003c/h3\u003e\n        \u003cp\u003eAdaBoost (Adaptive Boosting) is an ensemble learning method that combines multiple weak classifiers to create a strong classifier. It adjusts the weights of incorrectly classified instances to improve performance.\u003c/p\u003e\n\n   \u003ch3 id=\"decision-tree-classifier\"\u003eDecision Tree Classifier\u003c/h3\u003e\n        \u003cp\u003eA Decision Tree is a non-parametric supervised learning method used for classification and regression. It splits the data into subsets based on the value of input features.\u003c/p\u003e\n\n  \u003ch3 id=\"k-nearest-neighbors\"\u003eK-Nearest Neighbors\u003c/h3\u003e\n        \u003cp\u003eK-Nearest Neighbors (KNN) is a simple, instance-based learning algorithm used for classification and regression. It classifies instances based on the majority label of their nearest neighbors.\u003c/p\u003e\n\n   \u003ch3 id=\"linear-regression\"\u003eLinear Regression\u003c/h3\u003e\n        \u003cp\u003eLinear Regression is a regression algorithm that models the relationship between a dependent variable and one or more independent variables by fitting a linear equation.\u003c/p\u003e\n\n  \u003ch3 id=\"logistic-regression\"\u003eLogistic Regression\u003c/h3\u003e\n        \u003cp\u003eLogistic Regression is a classification algorithm used to model the probability of a certain class or event. It is particularly useful for binary classification problems.\u003c/p\u003e\n\n  \u003ch3 id=\"naive-bayes\"\u003eNaive Bayes\u003c/h3\u003e\n        \u003cp\u003eNaive Bayes is a probabilistic classifier based on Bayes' theorem with the assumption of independence between features. It is particularly effective for text classification problems.\u003c/p\u003e\n\n   \u003ch3 id=\"random-forest\"\u003eRandom Forest\u003c/h3\u003e\n        \u003cp\u003eRandom Forest is an ensemble learning method that constructs multiple decision trees during training and outputs the mode of the classes for classification or mean prediction for regression.\u003c/p\u003e\n\n  \u003ch2 id=\"usage\"\u003eUsage\u003c/h2\u003e\n        \u003cp\u003eTo run the algorithms, use the following command:\u003c/p\u003e\n        \u003cpre\u003e\u003ccode\u003epython main.py\u003c/code\u003e\u003c/pre\u003e\n        \u003cp\u003eYou can modify the datasets and parameters in the \u003ccode\u003econfig.py\u003c/code\u003e file.\u003c/p\u003e\n\n  \u003ch2 id=\"results\"\u003eResults\u003c/h2\u003e\n        \u003cp\u003eThe results of the algorithms are saved in the \u003ccode\u003eresults\u003c/code\u003e directory. Each algorithm's performance is evaluated based on metrics such as accuracy, precision, recall, F1-score for classification, and mean squared error (MSE) for regression.\u003c/p\u003e\n\n   \u003ch2 id=\"conclusion\"\u003eConclusion\u003c/h2\u003e\n        \u003cp\u003eThis project demonstrates the implementation and comparison of various machine learning algorithms. The performance of each algorithm varies depending on the dataset and problem type. Future work could include exploring more advanced algorithms and techniques to improve performance further.\u003c/p\u003e\n\n  \u003ch2 id=\"references\"\u003eReferences\u003c/h2\u003e\n        \u003cul\u003e\n            \u003cli\u003e\u003cstrong\u003eAdaBoost\u003c/strong\u003e: \u003ca href=\"https://www.sciencedirect.com/science/article/pii/S002200009791504X\"\u003eFreund, Y., \u0026 Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119-139.\u003c/a\u003e\u003c/li\u003e\n            \u003cli\u003e\u003cstrong\u003eDecision Trees\u003c/strong\u003e: \u003ca href=\"https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm\"\u003eBreiman, L. (1984). Classification and Regression Trees. Belmont, CA: Wadsworth.\u003c/a\u003e\u003c/li\u003e\n            \u003cli\u003e\u003cstrong\u003eKNN\u003c/strong\u003e: \u003ca href=\"https://ieeexplore.ieee.org/document/1053964\"\u003eCover, T. M., \u0026 Hart, P. E. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21-27.\u003c/a\u003e\u003c/li\u003e\n            \u003cli\u003e\u003cstrong\u003eLinear Regression\u003c/strong\u003e: \u003ca href=\"https://www.wiley.com/en-us/Linear+Regression+Analysis%2C+2nd+Edition-p-9780471616530\"\u003eSeber, G. A. F., \u0026 Lee, A. J. (2012). Linear Regression Analysis (2nd ed.). John Wiley \u0026 Sons.\u003c/a\u003e\u003c/li\u003e\n            \u003cli\u003e\u003cstrong\u003eLogistic Regression\u003c/strong\u003e: \u003ca href=\"https://rss.onlinelibrary.wiley.com/doi/10.1111/j.2517-6161.1958.tb00292.x\"\u003eCox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society: Series B (Methodological), 20(2), 215-242.\u003c/a\u003e\u003c/li\u003e\n            \u003cli\u003e\u003cstrong\u003eNaive Bayes\u003c/strong\u003e: \u003ca href=\"https://dl.acm.org/doi/10.1145/321075.321084\"\u003eMaron, M. E. (1961). Automatic Indexing: An Experimental Inquiry. Journal of the ACM, 8(3), 404-417.\u003c/a\u003e\u003c/li\u003e\n            \u003cli\u003e\u003cstrong\u003eRandom Forest\u003c/strong\u003e: \u003ca href=\"https://link.springer.com/article/10.1023/A:1010933404324\"\u003eBreiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32.\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzenitsu0509%2Fmachine-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fzenitsu0509%2Fmachine-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzenitsu0509%2Fmachine-learning/lists"}