{"id":25056543,"url":"https://github.com/ascender1729/collegeadmissionlogisticregression","last_synced_at":"2025-08-02T11:08:10.900Z","repository":{"id":233186162,"uuid":"786239177","full_name":"ascender1729/CollegeAdmissionLogisticRegression","owner":"ascender1729","description":"Predictive analysis using logistic regression to determine college admissions based on CET scores. 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It showcases the setup and execution of a machine learning model in a Google Colab environment using Python's sklearn library.\n\n## Table of Contents\n\n- [Project Overview](#project-overview)\n- [Features](#features)\n- [Data Description](#data-description)\n- [Installation](#installation)\n- [Usage](#usage)\n- [Contributing](#contributing)\n- [License](#license)\n- [Contact](#contact)\n\n## Project Overview\n\nThe \"College Admission Logistic Regression\" project predicts student admissions utilizing logistic regression techniques based on standardized test scores from a dataset. The project demonstrates comprehensive steps from data loading and preprocessing to model training and evaluation.\n\n## Features\n\n- **Data Loading and Preparation**: Seamless integration with Google Colab for data handling.\n- **Model Training**: Utilization of logistic regression for predictive analysis.\n- **Performance Evaluation**: Metrics such as precision, recall, and F-score are used to evaluate the model's effectiveness.\n\n## Data Description\n\nThe dataset primarily includes:\n- **CET_score**: The score obtained by students.\n- **Admitted**: Binary indicator (1 for admitted, 0 for not admitted).\n\n## Installation\n\nSet up the project environment using Google Colab for efficient execution:\n\n```bash\nfrom google.colab import drive\ndrive.mount('/content/drive')\n```\n\nClone the repository:\n```bash\ngit clone https://github.com/ascender1729/CollegeAdmissionLogisticRegression.git\ncd CollegeAdmissionLogisticRegression\n```\n\n## Usage\n\nBefore running the notebook, install the required Python libraries. Execute the following commands to ensure all dependencies are installed:\n\n```bash\npip install pandas numpy matplotlib seaborn scikit-learn\n```\n\nThe libraries you will use in this project include:\n- `pandas`: For data manipulation and analysis.\n- `numpy`: To work with arrays and perform mathematical operations.\n- `matplotlib.pyplot`: For creating static, animated, and interactive visualizations in Python.\n- `seaborn`: A Python data visualization library based on matplotlib, providing a high-level interface for drawing attractive statistical graphics.\n- `sklearn.model_selection`: Specifically `train_test_split`, to split the data into training and testing sets.\n- `sklearn.linear_model`: Specifically `LogisticRegression`, to perform the logistic regression analysis.\n- `sklearn.metrics`: Includes `classification_report`, `confusion_matrix`, and `precision_recall_fscore_support`, for model evaluation.\n\nExecute the notebook within Google Colab to follow the detailed steps from data preprocessing to model evaluation.\n\n## Contributing\n\nContributions to enhance the model or improve methodologies are highly appreciated. Please fork the repository and submit pull requests for review.\n\n## License\n\nThis project is licensed under the MIT License - see the `LICENSE` file for details.\n\n## Contact\n\nPavan Kumar - pavankumard.pg19.ma@nitp.ac.in\n\nLinkedIn: [@ascender1729](https://www.linkedin.com/in/im-pavankumar)\n\nProject Link: [College Admission Logistic Regression](https://github.com/ascender1729/CollegeAdmissionLogisticRegression)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fascender1729%2Fcollegeadmissionlogisticregression","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fascender1729%2Fcollegeadmissionlogisticregression","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fascender1729%2Fcollegeadmissionlogisticregression/lists"}