{"id":20338291,"url":"https://github.com/idaraabasiudoh/knn-customer-classification","last_synced_at":"2026-05-07T12:34:05.286Z","repository":{"id":251900274,"uuid":"838783909","full_name":"idaraabasiudoh/knn-customer-classification","owner":"idaraabasiudoh","description":"Labels telecommunication customer base to respective groups to determine service type required for each customer. ","archived":false,"fork":false,"pushed_at":"2024-08-06T10:46:41.000Z","size":63,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-04T14:33:51.883Z","etag":null,"topics":["data-analysis","jupyter-notebook","machine-learning","pyhton3","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":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/idaraabasiudoh.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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":"2024-08-06T10:35:45.000Z","updated_at":"2024-08-19T21:33:42.000Z","dependencies_parsed_at":"2024-08-06T12:54:58.367Z","dependency_job_id":null,"html_url":"https://github.com/idaraabasiudoh/knn-customer-classification","commit_stats":null,"previous_names":["idaraabasiudoh/knn_classifier_telecommunication_test_project"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/idaraabasiudoh/knn-customer-classification","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/idaraabasiudoh%2Fknn-customer-classification","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/idaraabasiudoh%2Fknn-customer-classification/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/idaraabasiudoh%2Fknn-customer-classification/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/idaraabasiudoh%2Fknn-customer-classification/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/idaraabasiudoh","download_url":"https://codeload.github.com/idaraabasiudoh/knn-customer-classification/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/idaraabasiudoh%2Fknn-customer-classification/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32737903,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-07T02:14:30.463Z","status":"ssl_error","status_checked_at":"2026-05-07T02:14:29.405Z","response_time":62,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 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-analysis","jupyter-notebook","machine-learning","pyhton3","scikit-learn"],"created_at":"2024-11-14T21:12:20.234Z","updated_at":"2026-05-07T12:34:05.267Z","avatar_url":"https://github.com/idaraabasiudoh.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# KNN_classifier_telecommunication_test_project\nLabels telecommunication customer base to respective groups to determine service type required for each customer. \n\n## Table of Contents\n- [Introduction](#introduction)\n- [Objectives](#objectives)\n- [Dataset](#dataset)\n- [Installation](#installation)\n- [Usage](#usage)\n- [Modeling](#modeling)\n- [Evaluation](#evaluation)\n- [Contributions](#contributions)\n- [Acknowledgments](#acknowledgments)\n- [Change Log](#change-log)\n- [License](#license)\n\n## Introduction\nThis repository contains a machine learning project focused on classifying customers for a telecommunication company using the K-Nearest Neighbors (KNN) algorithm. The project leverages Python and popular data science libraries such as scikit-learn, pandas, and matplotlib.\n\n## Objectives\nThe primary objectives of this project are:\n- To implement a K-Nearest Neighbors model using scikit-learn to classify customers.\n- To train, test, and evaluate the model on a dataset of customer demographics and usage patterns.\n- To explore the relationship between different customer features and their classification.\n\n## Dataset\nThe dataset used in this project contains demographic data and service usage patterns of customers, segmented into four groups. The target field, `custcat`, has four possible values that correspond to the four customer groups:\n1. Basic Service\n2. E-Service\n3. Plus Service\n4. Total Service\n\nThe dataset includes the following columns:\n- `region`\n- `tenure`\n- `age`\n- `marital`\n- `address`\n- `income`\n- `ed`\n- `employ`\n- `retire`\n- `gender`\n- `reside`\n\n[Dataset Source](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/teleCust1000t.csv)\n\n## Installation\nTo run this project locally, you need to have Python installed along with the required libraries. You can install the necessary packages using the following command:\n\nClone the repository and install the necessary dependencies:\n\ngit clone https://github.com/idaraabasiudoh/knn-customer-classification.git\ncd knn-customer-classification\npip install -r requirements.txt\n\n## Usage\nTo use this repository, follow these steps:\n1. Clone the repository:\n    ```bash\n    git clone https://github.com/idaraabasiudoh/knn-customer-classification.git\n    ```\n2. Navigate to the project directory:\n    ```bash\n    cd knn-customer-classification\n    ```\n3. Run the classification script:\n    ```bash\n    python knn-customer-classification.py\n    ```\n\n## Modeling\nThe modeling process involves the following steps:\n1. **Data Exploration**: Understanding the dataset by visualizing and summarizing the data.\n2. **Data Preparation**: Cleaning and splitting the data into training and testing sets.\n3. **Model Training**: Using the training set to train a K-Nearest Neighbors model.\n4. **Model Evaluation**: Evaluating the model using metrics such as accuracy.\n\n## Evaluation\nThe performance of the model is evaluated using the test dataset. The key metrics used for evaluation include:\n\n- **Accuracy**: This metric indicates how well the model's predictions match the actual classifications.\n\n### Example Code\nHere is an example of how to evaluate the model using these metrics:\n```python\nfrom sklearn.metrics import accuracy_score\n\n# Assuming y_test contains the actual values and y_pred contains the predicted values\naccuracy = accuracy_score(y_test, y_pred)\nprint(\"Accuracy:\", accuracy)\n```\n## Contributions\nWe welcome contributions from the community to improve this project. To contribute, please follow these steps:\n\n1. **Fork the Repository**: Click the \"Fork\" button at the top right of the repository page to create a copy of this repository on your GitHub account.\n2. **Clone the Repository**: Clone your forked repository to your local machine.\n    ```bash\n    git clone https://github.com/idaraabsiudoh/knn-customer-classification.git\n    ```\n3. **Create a New Branch**: Create a new branch for your feature or bug fix.\n    ```bash\n    git checkout -b feature-name\n    ```\n4. **Make Changes**: Make your changes to the codebase.\n5. **Commit Your Changes**: Commit your changes with a clear and descriptive commit message.\n    ```bash\n    git commit -m \"Description of your changes\"\n    ```\n6. **Push to Your Branch**: Push your changes to your forked repository.\n    ```bash\n    git push origin feature-name\n    ```\n7. **Open a Pull Request**: Open a pull request to merge your changes into the main repository. Provide a detailed description of your changes in the pull request.\n\nWe appreciate your contributions and will review your pull request as soon as possible. Thank you for helping improve this project!\n\n## Acknowledgments \n\u003ca href=\"http://www.linkedin.com/in/idaraabasiudoh\" target=\"_blank\"\u003eIdara-Abasi Udoh\u003c/a\u003e\n\nSaeed Aghabozorgi\n\n### Other Contributors\n\u003ca href=\"https://www.linkedin.com/in/joseph-s-50398b136/\" target=\"_blank\"\u003eJoseph Santarcangelo\u003c/a\u003e\n\nAzim Hirjani\n\n## Change Log\n| Date (YYYY-MM-DD) | Version | Changed By         | Change Description                |\n|-------------------|---------|--------------------|-----------------------------------|\n| 2024-07-02        | 2.2     | Idara-Absi Udoh    | Project completion                |\n| 2020-11-03        | 2.1     | Lakshmi Holla      | Changed URL of the csv            |\n| 2020-08-27        | 2.0     | Lavanya            | Moved lab to course repo in GitLab |\n\n## License\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fidaraabasiudoh%2Fknn-customer-classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fidaraabasiudoh%2Fknn-customer-classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fidaraabasiudoh%2Fknn-customer-classification/lists"}