https://github.com/anshpg/vectorwave
The Jupyter Notebook (ipynb file) contained within this repository serves as the primary codebase for this project. It demonstrates how to preprocess the input data, train the logistic regression classifier, evaluate its performance, and save the trained model for future use.
https://github.com/anshpg/vectorwave
collab jupyter-notebook matplotlib-pyplot numpy pandas python sklearn
Last synced: 5 months ago
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The Jupyter Notebook (ipynb file) contained within this repository serves as the primary codebase for this project. It demonstrates how to preprocess the input data, train the logistic regression classifier, evaluate its performance, and save the trained model for future use.
- Host: GitHub
- URL: https://github.com/anshpg/vectorwave
- Owner: ANSHPG
- License: mit
- Created: 2024-03-19T19:42:47.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-03-19T19:49:46.000Z (over 1 year ago)
- Last Synced: 2025-01-21T11:32:15.027Z (10 months ago)
- Topics: collab, jupyter-notebook, matplotlib-pyplot, numpy, pandas, python, sklearn
- Language: Jupyter Notebook
- Homepage:
- Size: 65.4 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# VectorWave
## Overview
This repository contains a Jupyter Notebook (`ipynb` file) that implements a predictive model to determine whether a placement will occur based on 100 input features of CGPA (Cumulative Grade Point Average) and IQ scores. The model is developed using Python and various libraries including NumPy, Pandas, Matplotlib, Scikit-learn, MLxtend, and Pickle. Logistic Regression classification algorithm is employed for building the predictive model.
## Installation
To run the notebook locally, make sure you have Python installed on your system. Additionally, install the required libraries using the following command:
```
pip install numpy pandas matplotlib scikit-learn mlxtend
```
## Usage
1. Clone the repository to your local machine.
2. Navigate to the directory containing the Jupyter Notebook (`Placement_Predictor.ipynb`).
3. Open the notebook using Jupyter Notebook or JupyterLab.
4. Follow the instructions provided in the notebook to preprocess the data, train the logistic regression model, and evaluate its performance.
5. Experiment with different parameters and settings as needed.
6. Save the trained model using Pickle for future use.
## Contributing
Contributions to this repository are welcome! If you have any suggestions, bug fixes, or enhancements, feel free to open an issue or submit a pull request.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Acknowledgments
- This project was inspired by the need for accurate placement prediction models in educational institutions and recruitment agencies.
- Special thanks to the developers of the libraries used in this project for their contributions to the open-source community.
Feel free to explore the notebook and use the placement predictor for your own analysis or educational purposes. Happy predicting!