{"id":23582659,"url":"https://github.com/alphacrypto246/zoo-animal-classifier","last_synced_at":"2026-05-20T05:32:51.092Z","repository":{"id":269410440,"uuid":"907330392","full_name":"alphacrypto246/ZOO-Animal-Classifier","owner":"alphacrypto246","description":"A project that uses machine learning to classify animals into categories like Mammals, Birds, and Reptiles based on their characteristics.","archived":false,"fork":false,"pushed_at":"2024-12-23T10:54:17.000Z","size":1425,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-05-16T22:13:52.954Z","etag":null,"topics":["machine-learning","machine-learning-algorithms","random-forest","scikit-learn"],"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/alphacrypto246.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":"2024-12-23T10:44:51.000Z","updated_at":"2024-12-24T10:00:25.000Z","dependencies_parsed_at":"2024-12-23T11:33:23.563Z","dependency_job_id":"668239f1-4806-4036-b81b-c31fb1e271be","html_url":"https://github.com/alphacrypto246/ZOO-Animal-Classifier","commit_stats":null,"previous_names":["alphacrypto246/zoo-animal-classifier"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/alphacrypto246/ZOO-Animal-Classifier","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alphacrypto246%2FZOO-Animal-Classifier","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alphacrypto246%2FZOO-Animal-Classifier/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alphacrypto246%2FZOO-Animal-Classifier/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alphacrypto246%2FZOO-Animal-Classifier/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/alphacrypto246","download_url":"https://codeload.github.com/alphacrypto246/ZOO-Animal-Classifier/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alphacrypto246%2FZOO-Animal-Classifier/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":271405572,"owners_count":24753799,"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","status":"online","status_checked_at":"2025-08-20T02:00:09.606Z","response_time":69,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"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":["machine-learning","machine-learning-algorithms","random-forest","scikit-learn"],"created_at":"2024-12-27T01:12:27.285Z","updated_at":"2026-05-20T05:32:51.041Z","avatar_url":"https://github.com/alphacrypto246.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# ZOO Animal Classifier\n\n## Overview\nThis project is designed to classify different animals based on various features such as habitat, physical characteristics, and behaviors. The goal is to build a machine learning model using the Random Forest classifier to predict the animal type from a set of predefined features.\n\n## Dataset\nThe dataset contains 101 rows and 17 columns, with each row representing an animal. The features include information about the animal's habitat, physical traits, and behavior.\n\n### Features\n- `name`: Name of the animal\n- `hair`: Indicates if the animal has hair (1 for yes, 0 for no)\n- `features`: Various other binary features describing the animal\n- `eggs`: Whether the animal lays eggs\n- `milk`: Whether the animal produces milk\n- `airborne`: Whether the animal can fly\n- `aquatic`: Whether the animal lives in water\n- `predator`: Whether the animal is a predator\n- `toothed`: Whether the animal has teeth\n- `backbone`: Whether the animal has a backbone\n- `breathes`: Whether the animal breathes air\n- `venomous`: Whether the animal is venomous\n- `fins`: Whether the animal has fins\n- `legs`: Number of legs the animal has\n- `tail`: Whether the animal has a tail\n- `domestic`: Whether the animal is domestic\n- `catsize`: Size of the animal (small, medium, large)\n- `type`: The animal type (e.g., mammal, bird, fish, etc.)\n\n## Usage\n\n### 1. Load the Dataset\nThe dataset is loaded and preprocessed using pandas.\n\n### 2. Data Preprocessing\nThe dataset is cleaned by filling missing values and performing feature encoding. The target variable (`type`) is separated from the features.\n\n### 3. Model Training\nA Random Forest classifier is used to train the model on the preprocessed data. Cross-validation is performed to evaluate the model's performance.\n\n### 4. Model Evaluation\nThe model is evaluated using various metrics such as accuracy, precision, recall, and F1-score. The model is also tested using cross-validation for a more robust evaluation.\n\n### 5. Model Predictions\nThe trained model is used to predict the animal type based on new input data.\n\n## Cross-validation\nThe model uses cross-validation to assess its performance. The dataset is split into multiple folds, and the model is trained on each fold to provide a more reliable accuracy score.\n\n## Results\nThe model achieves high accuracy in classifying animals based on the features. The cross-validation scores demonstrate a stable and consistent performance across different splits of the dataset.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falphacrypto246%2Fzoo-animal-classifier","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Falphacrypto246%2Fzoo-animal-classifier","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falphacrypto246%2Fzoo-animal-classifier/lists"}