https://github.com/kts-o7/aiml-lab
Contains 5th Semester AIML Lab programs
https://github.com/kts-o7/aiml-lab
a-star-algorithm ai alphabeta-pruning hillclimbingalgorithm kmeans-clustering knn-classification logistic-regression ml naivebayesclassifier sklearn tic-tac-toe
Last synced: 3 days ago
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Contains 5th Semester AIML Lab programs
- Host: GitHub
- URL: https://github.com/kts-o7/aiml-lab
- Owner: KTS-o7
- Created: 2023-12-18T16:03:23.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-04-28T11:52:30.000Z (over 1 year ago)
- Last Synced: 2025-04-23T12:52:17.860Z (6 months ago)
- Topics: a-star-algorithm, ai, alphabeta-pruning, hillclimbingalgorithm, kmeans-clustering, knn-classification, logistic-regression, ml, naivebayesclassifier, sklearn, tic-tac-toe
- Language: Python
- Homepage:
- Size: 127 KB
- Stars: 20
- Watchers: 1
- Forks: 6
- Open Issues: 1
-
Metadata Files:
- Readme: ReadMe.md
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README
# Project Name
## Description
Contains Laboratory Programs and explaination.
## Table of Contents
- [Installation](#installation)
- [Usage](#usage)
- [Contributing](#contributing)
## Installation
Run the following command to create a virtual env and activate it
```bash
python -m venv env
source ./env/bin/activate
```
To install the packages use the following command
```bash
pip install -r requirements.txt
```
## Usage
1. Can be used for the AI_ML Lab for the course 21AI52 at RVCE
## Contributing
Thank you for considering contributing to this project! To ensure a smooth collaboration, please follow these guidelines:
1. Fork the repository and create your branch from `main`.
2. Make sure your code follows the project's coding style and conventions.
3. Keep your commits concise and descriptive, following the [Git commit guidelines](https://git-scm.com/book/en/v2/Distributed-Git-Contributing-to-a-Project#_commit_guidelines).
4. Check out our [Contribution Guide](Contribution.md)
5. Write clear and comprehensive documentation for any changes or new features.
6. Test your changes thoroughly and ensure they do not introduce any regressions.
7. Submit a pull request, clearly explaining the purpose and scope of your changes.
We appreciate your contributions and look forward to your involvement in making this project better!