https://github.com/coding-enthusiast9857/ai_ml_projects

This repository contains AI ML projects. It demonstrates examples of applying machine learning techniques to solve real-world problems. Key objectives of this projects are Accurate Predictions, User-Friendly Interface, Data Exploration and Visualization, Model Selection, Feature Engineering, Model Evaluation & Deployment.
https://github.com/coding-enthusiast9857/ai_ml_projects

ai aids aiml artificial-intelligence cv data-science dsml housepriceprediction machine-learning machine-learning-algo machine-learning-algorithms machinelearning ml nlp predictions

Last synced: 3 months ago
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This repository contains AI ML projects. It demonstrates examples of applying machine learning techniques to solve real-world problems. Key objectives of this projects are Accurate Predictions, User-Friendly Interface, Data Exploration and Visualization, Model Selection, Feature Engineering, Model Evaluation & Deployment.

• Host: GitHub
• URL: https://github.com/coding-enthusiast9857/ai_ml_projects
• Owner: CODING-Enthusiast9857
• License: mit
• Created: 2023-09-11T08:25:21.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-03-26T11:20:13.000Z (about 1 year ago)
• Last Synced: 2025-01-14T04:12:32.217Z (5 months ago)
• Topics: ai, aids, aiml, artificial-intelligence, cv, data-science, dsml, housepriceprediction, machine-learning, machine-learning-algo, machine-learning-algorithms, machinelearning, ml, nlp, predictions
• Language: Jupyter Notebook
• Homepage:
• Size: 9.85 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Machine Learning Projects

![Python](https://img.shields.io/badge/Python-3.8%2B-blue)

[![NumPy](https://img.shields.io/badge/NumPy-1.21.2-red)](https://numpy.org/)

[![Pandas](https://img.shields.io/badge/Pandas-1.3.3-orange)](https://pandas.pydata.org/)

[![Matplotlib](https://img.shields.io/badge/Matplotlib-3.4.3-blue)](https://matplotlib.org/)

[![Seaborn](https://img.shields.io/badge/Seaborn-0.11.2-yellow)](https://seaborn.pydata.org/)

![Scikit-Learn](https://img.shields.io/badge/Scikit--Learn-0.24.2-green)

[![GitHub license](https://img.shields.io/github/license/CODING-Enthusiast9857/MachineLearningProjects)](https://github.com/CODING-Enthusiast9857/MachineLearningProjects/blob/main/LICENSE)

[![GitHub stars](https://img.shields.io/github/stars/CODING-Enthusiast9857/MachineLearningProjects)](https://github.com/CODING-Enthusiast9857/MachineLearningProjects/stargazers)

[![GitHub issues](https://img.shields.io/github/issues/CODING-Enthusiast9857/MachineLearningProjects)](https://github.com/CODING-Enthusiast9857/MachineLearningProjects/issues)

Welcome to the "Machine Learning Projects" repository! Here, you will find a collection of machine learning projects that I have worked on. Each project explores different aspects of machine learning, including data analysis, model development, and evaluation.

## Technologies and Platforms

These projects are primarily developed using the following technologies and platforms:

- **Python**: The core programming language for all machine learning code.

- **Jupyter Notebook**: Used for creating interactive and well-documented project notebooks.

- **Scikit-Learn**: A powerful machine learning library for model development and evaluation.

- **Pandas**: Used for data manipulation and analysis.

- **NumPy**: Essential for numerical computations.

- **Matplotlib** and **Seaborn**: For data visualization.

- **GitHub**: The platform where you can find the project repositories and source code.

- **Kaggle**: To download datasets.

## Projects

You will find list of machine learning projects available in this repository. Each project directory contains a detailed README with project-specific information.

## Getting Started

To get started with any project, you can follow the instructions in the respective project's README. Typically, you will need to:

1. Clone the repository to your local machine.

2. Install any required dependencies (if not already done).

3. Open the project in a Jupyter Notebook or any IDE of your choice.

4. Run the code and explore the project.

## Contributions

If you'd like to contribute to any of these projects or have suggestions for improvements, feel free to:

- Fork the repository.

- Create a new branch.

- Make your modifications.

- Submit a pull request.

Your contributions and ideas are always welcome!

## License

This project is licensed under the [MIT License](LICENSE) - see the LICENSE file for details.

Please don't hesitate to reach out if you have any questions or need further information. Enjoy exploring the world of machine learning!

## Created by

Created with 🤍 by Madhavi Sonawane.

Follow Madhavi Sonawane for more such contents. 


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### Happy CODING...!! 💻