https://github.com/ascender1729/collegeadmissionlogisticregression
Predictive analysis using logistic regression to determine college admissions based on CET scores. This project includes detailed steps for data preparation, model training, and evaluation using Python and scikit-learn in a Jupyter Notebook environment.
https://github.com/ascender1729/collegeadmissionlogisticregression
college-admission data-science education jupyter-notebook logistic-regression machine-learning predictive-analytics python sklearn
Last synced: 12 months ago
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Predictive analysis using logistic regression to determine college admissions based on CET scores. This project includes detailed steps for data preparation, model training, and evaluation using Python and scikit-learn in a Jupyter Notebook environment.
- Host: GitHub
- URL: https://github.com/ascender1729/collegeadmissionlogisticregression
- Owner: ascender1729
- License: mit
- Created: 2024-04-13T20:54:51.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-04-13T22:12:38.000Z (over 2 years ago)
- Last Synced: 2025-04-14T11:47:52.989Z (over 1 year ago)
- Topics: college-admission, data-science, education, jupyter-notebook, logistic-regression, machine-learning, predictive-analytics, python, sklearn
- Language: Jupyter Notebook
- Homepage: https://github.com/ascender1729/CollegeAdmissionLogisticRegression
- Size: 120 KB
- Stars: 3
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# College Admission Logistic Regression
This project demonstrates the application of logistic regression to predict college admissions based on student scores. It showcases the setup and execution of a machine learning model in a Google Colab environment using Python's sklearn library.
## Table of Contents
- [Project Overview](#project-overview)
- [Features](#features)
- [Data Description](#data-description)
- [Installation](#installation)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)
- [Contact](#contact)
## Project Overview
The "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.
## Features
- **Data Loading and Preparation**: Seamless integration with Google Colab for data handling.
- **Model Training**: Utilization of logistic regression for predictive analysis.
- **Performance Evaluation**: Metrics such as precision, recall, and F-score are used to evaluate the model's effectiveness.
## Data Description
The dataset primarily includes:
- **CET_score**: The score obtained by students.
- **Admitted**: Binary indicator (1 for admitted, 0 for not admitted).
## Installation
Set up the project environment using Google Colab for efficient execution:
```bash
from google.colab import drive
drive.mount('/content/drive')
```
Clone the repository:
```bash
git clone https://github.com/ascender1729/CollegeAdmissionLogisticRegression.git
cd CollegeAdmissionLogisticRegression
```
## Usage
Before running the notebook, install the required Python libraries. Execute the following commands to ensure all dependencies are installed:
```bash
pip install pandas numpy matplotlib seaborn scikit-learn
```
The libraries you will use in this project include:
- `pandas`: For data manipulation and analysis.
- `numpy`: To work with arrays and perform mathematical operations.
- `matplotlib.pyplot`: For creating static, animated, and interactive visualizations in Python.
- `seaborn`: A Python data visualization library based on matplotlib, providing a high-level interface for drawing attractive statistical graphics.
- `sklearn.model_selection`: Specifically `train_test_split`, to split the data into training and testing sets.
- `sklearn.linear_model`: Specifically `LogisticRegression`, to perform the logistic regression analysis.
- `sklearn.metrics`: Includes `classification_report`, `confusion_matrix`, and `precision_recall_fscore_support`, for model evaluation.
Execute the notebook within Google Colab to follow the detailed steps from data preprocessing to model evaluation.
## Contributing
Contributions to enhance the model or improve methodologies are highly appreciated. Please fork the repository and submit pull requests for review.
## License
This project is licensed under the MIT License - see the `LICENSE` file for details.
## Contact
Pavan Kumar - pavankumard.pg19.ma@nitp.ac.in
LinkedIn: [@ascender1729](https://www.linkedin.com/in/im-pavankumar)
Project Link: [College Admission Logistic Regression](https://github.com/ascender1729/CollegeAdmissionLogisticRegression)