{"id":30277066,"url":"https://github.com/tasninanika/coded_data_prediction-knn","last_synced_at":"2026-04-07T08:32:29.437Z","repository":{"id":301702958,"uuid":"1009736648","full_name":"tasninanika/Coded_Data_Prediction-KNN","owner":"tasninanika","description":"K-Nearest Neighbors (KNN) is a supervised machine learning algorithm","archived":false,"fork":false,"pushed_at":"2025-06-28T09:12:04.000Z","size":37,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-08-16T11:22:57.455Z","etag":null,"topics":["knn","pandas","python3","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/tasninanika.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}},"created_at":"2025-06-27T16:14:16.000Z","updated_at":"2025-06-28T09:12:07.000Z","dependencies_parsed_at":"2025-06-28T10:21:44.256Z","dependency_job_id":"285b2377-f5b8-4669-b52c-09f32544514c","html_url":"https://github.com/tasninanika/Coded_Data_Prediction-KNN","commit_stats":null,"previous_names":["tasninanika/coded_data_prediction-knn"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/tasninanika/Coded_Data_Prediction-KNN","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tasninanika%2FCoded_Data_Prediction-KNN","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tasninanika%2FCoded_Data_Prediction-KNN/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tasninanika%2FCoded_Data_Prediction-KNN/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tasninanika%2FCoded_Data_Prediction-KNN/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/tasninanika","download_url":"https://codeload.github.com/tasninanika/Coded_Data_Prediction-KNN/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tasninanika%2FCoded_Data_Prediction-KNN/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31506562,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-07T03:10:19.677Z","status":"ssl_error","status_checked_at":"2026-04-07T03:10:13.982Z","response_time":105,"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":["knn","pandas","python3","scikit-learn"],"created_at":"2025-08-16T11:13:14.463Z","updated_at":"2026-04-07T08:32:29.416Z","avatar_url":"https://github.com/tasninanika.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🤖 Coded Data Prediction using K-Nearest Neighbors (KNN) Classifier\n\nThis project demonstrates how the **K-Nearest Neighbors (KNN)** algorithm can be used to build a simple and effective machine learning classifier. KNN is an easy-to-understand and powerful algorithm used for **classification** and **regression** problems.\n\n---\n\n## 📌 What is KNN?\n\n**K-Nearest Neighbors (KNN)** is a **supervised machine learning** algorithm that predicts the output class of a new data point by looking at the **'K' closest data points** in the training dataset. It works on the principle that similar data points are near each other.\n\nFor classification tasks:\n- It checks the **K nearest neighbors** of a data point.\n- Takes a **majority vote** from the neighbors' classes.\n- Assigns the most common class as the prediction.\n\n---\n\n## 📈 Key Steps in This Project\n\n1. Import necessary libraries (`sklearn`, `pandas`, etc.)\n2. Load and explore the dataset\n3. Split data into training and testing sets\n4. Train the KNN model using `KNeighborsClassifier`\n5. 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