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","archived":false,"fork":false,"pushed_at":"2024-10-31T08:05:55.000Z","size":71,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-05-26T20:46:08.002Z","etag":null,"topics":["classification","jupyter-notebook","logistic-regression","machine-learning","matplotlib-pyplot","numpy","pandas","python","sklearn"],"latest_commit_sha":null,"homepage":"https://kaggle.com/code/ebadshabbir/logistic-regression-binomial","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/EbadShabbir.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":"2024-10-17T09:23:55.000Z","updated_at":"2024-10-31T08:05:58.000Z","dependencies_parsed_at":"2025-05-26T20:51:14.683Z","dependency_job_id":null,"html_url":"https://github.com/EbadShabbir/Logistic_Regression-Binomial-","commit_stats":null,"previous_names":["ebadshabbir/logistic_regression-binomial-"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/EbadShabbir/Logistic_Regression-Binomial-","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/EbadShabbir%2FLogistic_Regression-Binomial-","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/EbadShabbir%2FLogistic_Regression-Binomial-/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/EbadShabbir%2FLogistic_Regression-Binomial-/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/EbadShabbir%2FLogistic_Regression-Binomial-/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/EbadShabbir","download_url":"https://codeload.github.com/EbadShabbir/Logistic_Regression-Binomial-/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/EbadShabbir%2FLogistic_Regression-Binomial-/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":265701111,"owners_count":23813749,"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":["classification","jupyter-notebook","logistic-regression","machine-learning","matplotlib-pyplot","numpy","pandas","python","sklearn"],"created_at":"2024-10-31T11:07:23.088Z","updated_at":"2025-12-30T22:07:10.300Z","avatar_url":"https://github.com/EbadShabbir.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Logistic Regression on Social Network Ads Dataset\n\nThis project implements a **Logistic Regression** model using the Social Network Ads dataset to predict whether a user will purchase a product based on their age and estimated salary. The model is trained and tested on a split of the dataset, and its performance is visualized with confusion matrices and decision boundary plots for both the training and test sets.\n\n## Table of Contents\n- [Overview](#overview)\n- [Dataset](#dataset)\n- [Installation](#installation)\n- [Usage](#usage)\n- [Results](#results)\n- [Contributing](#contributing)\n- [License](#license)\n\n## Overview\nThis project demonstrates:\n- Loading and preprocessing the data.\n- Splitting the dataset into training and test sets.\n- Applying **feature scaling** to normalize the features.\n- Training a **Logistic Regression** model.\n- Visualizing decision boundaries for both the training and test sets.\n\n## Dataset\nThe dataset is the Social Network Ads dataset, which includes the following columns:\n- **User ID**: Unique identifier for each user.\n- **Gender**: Gender of the user.\n- **Age**: Age of the user.\n- **Estimated Salary**: Estimated salary of the user.\n- **Purchased**: Whether the user purchased the product (0 or 1).\n\nThe dataset can be found on Kaggle: [Social Network Ads](https://www.kaggle.com).\n\n## Installation\nTo run this code, follow these steps:\n\n1. Clone the repository:\n   ```bash\n    https://github.com/EbadShabbir/Logistic_Regression-Binomial-\n   \n   \npip install -r requirements.txt\npython logistic_regression.py\n\n### Notes:\n- Update the dataset download URL and any additional details if required.\n- Add a `requirements.txt` with the necessary Python packages:\n  ```text\n  pandas\n  matplotlib\n  numpy\n  scikit-learn\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Febadshabbir%2Flogistic_regression-binomial-","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Febadshabbir%2Flogistic_regression-binomial-","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Febadshabbir%2Flogistic_regression-binomial-/lists"}