{"id":21470939,"url":"https://github.com/saadarazzaq/logistic-regression","last_synced_at":"2025-08-30T17:43:51.130Z","repository":{"id":195888479,"uuid":"693887835","full_name":"SaadARazzaq/Logistic-Regression","owner":"SaadARazzaq","description":"Code without built in ML libraries =\u003e ML ASSIGNMENT 1 Q2","archived":false,"fork":false,"pushed_at":"2024-03-01T14:26:14.000Z","size":489,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-23T16:14:22.538Z","etag":null,"topics":["algorithm-implementation","logistic-regression","python"],"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/SaadARazzaq.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":"2023-09-19T23:13:15.000Z","updated_at":"2024-03-01T14:26:54.000Z","dependencies_parsed_at":null,"dependency_job_id":"72663bc8-31cf-45ec-a389-62c8a210a135","html_url":"https://github.com/SaadARazzaq/Logistic-Regression","commit_stats":null,"previous_names":["saadarazzaq/logistic-regression"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SaadARazzaq%2FLogistic-Regression","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SaadARazzaq%2FLogistic-Regression/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SaadARazzaq%2FLogistic-Regression/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SaadARazzaq%2FLogistic-Regression/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/SaadARazzaq","download_url":"https://codeload.github.com/SaadARazzaq/Logistic-Regression/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243988956,"owners_count":20379649,"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":["algorithm-implementation","logistic-regression","python"],"created_at":"2024-11-23T09:29:49.495Z","updated_at":"2025-03-17T06:45:12.889Z","avatar_url":"https://github.com/SaadARazzaq.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Logistic Regression Model 📊\n\n## Problem Statement 🎯\n\n## Introduction 🚀\n\nLogistic regression is a fundamental technique in machine learning used for binary classification tasks. It models the probability that a given input belongs to a particular class.\n\n## Data Preparation and Preprocessing 🛠️\n\n- Read the data files 'DataX.dat' and 'ClassY.dat'.\n- Standardize features for better convergence.\n- Ensure proper preprocessing to handle missing values and outliers.\n\n## Training Logistic Regression Model 🧠\n\n- Implemented the sigmoid function to model the probability.\n- Utilized gradient descent optimization to minimize the cost function.\n- Monitored the cost to ensure convergence.\n- Tuned hyperparameters such as learning rate and number of iterations.\n\n## Evaluation Metrics 📏\n\n- Assess model performance using appropriate evaluation metrics such as accuracy, precision, recall, and F1-score.\n- Utilize techniques like cross-validation to estimate the generalization error.\n\n## Results and Analysis 📈\n\n- Interpret the model coefficients to understand feature importance.\n- Visualize decision boundaries and predictions to gain insights into model behavior.\n- Compare performance with other classification algorithms if applicable.\n\n## Conclusion 📝\n\n- Logistic regression is a powerful tool for binary classification tasks.\n- Proper data preprocessing and hyperparameter tuning are crucial for model performance.\n- Continuous evaluation and refinement are essential for maintaining model effectiveness.\n\n## Further Improvements 🌟\n\n- Experiment with different feature engineering techniques to enhance model performance.\n- Explore advanced optimization algorithms for faster convergence.\n- Consider ensemble methods or deep learning approaches for more complex datasets.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsaadarazzaq%2Flogistic-regression","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsaadarazzaq%2Flogistic-regression","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsaadarazzaq%2Flogistic-regression/lists"}