Welcome to the Machine Learning Repository! This repository is a collection of notebooks showcasing various machine learning projects and implementations. Whether you are a machine learning enthusiast or a data science practitioner, this repository offers a diverse set of projects to explore and learn from.
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Employee_Attrition.ipynb: In this notebook, we explore an employee attrition dataset and implement various classification algorithms to predict employee attrition. Learn about decision tree, random forest, and support vector classifiers.
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Image_classification.ipynb: This notebook delves into image classification using machine learning techniques. We explore different datasets, preprocess images, and build image classification models.
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ML_Algorithms.ipynb: This notebook covers essential machine learning algorithms, including decision tree, random forest, and support vector classifiers. These algorithms are implemented on datasets from the 'files' folder.
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Rain_Prediction.ipynb: In this project, we aim to predict rain using machine learning. Explore the concepts of data preprocessing and classification algorithms.
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Saving_A_Model.ipynb: This notebook showcases how to save machine learning models using both Pickle and Joblib libraries. Discover the importance of model persistence.
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model.pickle: This file contains a model saved using Pickle for future use.
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model_joblib: This file contains a model saved using Joblib for future use.
To access and run the notebooks in this repository, follow these simple steps:
- Clone the repository to your local machine:
git clone https://github.com/your-username/machine-learning-repo.git
cd machine-learning-repo- Install the required Python libraries:
pip install scikit-learn pandas numpy matplotlib- Launch Jupyter notebook:
jupyter notebook- Explore the notebooks, run the code, and dive into the fascinating world of machine learning!
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Diverse Projects: Explore various machine learning projects covering classification, image classification, and rain prediction.
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Practical Implementations: Gain hands-on experience by running and modifying the provided notebooks.
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Model Saving: Learn how to save machine learning models for future use using Pickle and Joblib.
This repository is licensed under the MIT License.
Your contributions are highly appreciated! Whether it's improving existing content, adding new projects, or fixing bugs, feel free to open issues or submit pull requests. Together, we can create a valuable resource for the machine learning community.
Delve into the exciting world of machine learning with a diverse set of projects. Enhance your skills, gain practical experience, and explore the limitless possibilities of machine learning.
Happy learning and happy experimenting!
Project Author: Muhammed Thahseer CK
Self-taught Data Scientist