{"id":22871015,"url":"https://github.com/rvats20/income-classification-using-ml","last_synced_at":"2026-05-11T02:25:53.008Z","repository":{"id":264739848,"uuid":"894258831","full_name":"rvats20/Income-Classification-using-ML","owner":"rvats20","description":"Model Training, Implementing various machine learning algorithms such as Logistic Regression, Decision Trees, Random Forests, and Gradient Boosting. Model Evaluation: Assessing model performance using metrics like accuracy, precision, recall, and F1-score. 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This repository contains code and resources for building a machine learning model to classify individuals' income levels based on various features.\n\n## Table of Contents\n\n- [Introduction](#introduction)\n- [Features](#features)\n- [Installation](#installation)\n- [Usage](#usage)\n- [Model Architecture](#model-architecture)\n- [Evaluation](#evaluation)\n- [Contributing](#contributing)\n- [License](#license)\n- [Contact](#contact)\n\n## Introduction\n\nThe goal of this project is to predict whether an individual's income exceeds a certain threshold based on demographic and employment-related features. This can be useful for various applications, including targeted marketing, financial analysis, and social studies.\n\n## Features\n\n- **Data Preprocessing:** Handling missing values, encoding categorical variables, and scaling numerical features.\n- **Model Training:** Implementing various machine learning algorithms such as Logistic Regression, Decision Trees, Random Forests, and Gradient Boosting.\n- **Model Evaluation:** Assessing model performance using metrics like accuracy, precision, recall, and F1-score.\n- **Hyperparameter Tuning:** Optimizing model parameters for better performance.\n- **Visualization:** Plotting feature importance, confusion matrix, and ROC curves.\n\n## Installation\n\nTo get started with the project, follow these steps:\n\n1. **Clone the repository:**\n   ```bash\n   git clone https://github.com/your-username/Income-Classification-using-ML.git\n   ```\n2. **Navigate to the project directory:**\n   ```bash\n   cd Income-Classification-using-ML\n   ```\n3. **Install the required dependencies:**\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n## Usage\n\nTo run the project, use the following command:\n```bash\npython main.py\n```\n\n## Model Architecture\n\nThe project explores various machine learning models, including:\n\n- **Logistic Regression:** A simple yet effective linear model for binary classification.\n- **Decision Trees:** A non-linear model that splits data based on feature values.\n- **Random Forests:** An ensemble method that combines multiple decision trees for better performance.\n- **Gradient Boosting:** An advanced ensemble method that builds models sequentially to correct errors of previous models.\n\n## Evaluation\n\nThe models are evaluated using the following metrics:\n\n- **Accuracy:** The proportion of correctly classified instances.\n- **Precision:** The proportion of true positive predictions among all positive predictions.\n- **Recall:** The proportion of true positive predictions among all actual positives.\n- **F1-Score:** The harmonic mean of precision and recall.\n\n## Contributing\n\nWe welcome contributions! If you'd like to contribute, please follow these steps:\n\n1. Fork the repository\n2. Create a new branch (`git checkout -b feature-branch`)\n3. Make your changes\n4. Commit your changes (`git commit -m 'Add some feature'`)\n5. Push to the branch (`git push origin feature-branch`)\n6. Open a pull request\n\n## License\n\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frvats20%2Fincome-classification-using-ml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frvats20%2Fincome-classification-using-ml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frvats20%2Fincome-classification-using-ml/lists"}