{"id":23119373,"url":"https://github.com/goldsharon/logistic-regression-from-scratch","last_synced_at":"2026-05-03T02:37:17.949Z","repository":{"id":260270998,"uuid":"880832717","full_name":"GoldSharon/logistic-regression-from-scratch","owner":"GoldSharon","description":"A Logistic Regression model built from scratch in Python using NumPy, without ML libraries. 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The code demonstrates a basic understanding of gradient descent, probability predictions, and binary classification. Logistic Regression is a foundational algorithm in supervised machine learning, particularly used for binary classification problems.\n\n## Features\n- Built without any machine learning libraries (like scikit-learn) to illustrate fundamental concepts\n- Includes functions for model training, predictions, and weight updates using gradient descent\n- Implements sigmoid activation and binary cross-entropy loss calculation\n- Customizable learning rate and number of iterations\n\n\n## Getting Started\n\n### Prerequisites\n- Python 3.x\n- NumPy\n- Pandas (if using a dataset for testing)\n\n### Installation\n1. Clone the repository:\n   ```bash\n        git clone https://github.com/your-username/logistic-regression-from-scratch.git\n2. Install the dependencies:\n   ```bash\n      pip install numpy pandas\n\n### Usage\n\n1.Import the Logistic Regression class and load your dataset.\n2.Fit the model on your data.\n3.Use the model to make predictions.\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgoldsharon%2Flogistic-regression-from-scratch","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgoldsharon%2Flogistic-regression-from-scratch","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgoldsharon%2Flogistic-regression-from-scratch/lists"}