Welcome to the Machine Learning Algorithms Implementation repository! This repository focuses on practical implementations of various regression algorithms using Python. Whether you are a machine learning enthusiast or a data science practitioner, this repository provides valuable insights and hands-on experience to enhance your understanding of regression algorithms.
The repository contains the following notebooks:
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ML_Algorithms.ipynb: Explore different types of regression algorithms and their implementations, including:
- Linear Regression: Understand the foundational algorithm for modeling linear relationships.
- Multiple Linear Regression: Extend linear regression to handle multiple independent variables.
- Decision Tree Regression: Explore decision tree-based regression for non-linear relationships.
- Random Forest Regression: Harness the power of ensemble methods with random forest regression.
- Support Vector Regression (SVR): Use support vector machines for regression tasks.
- Polynomial Regression: Learn how to model polynomial relationships between variables.
- Lasso and Ridge Regression: Dive into regularization techniques for improved model performance.
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Untitled.ipynb: This notebook contains additional content or experiments related to regression algorithms. Feel free to explore and discover more insights!
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/ml-algorithms-implementation.git
cd ml-algorithms-implementation- Install the required Python libraries:
pip install scikit-learn pandas numpy matplotlib- Launch Jupyter notebook:
jupyter notebook- Navigate to the notebook of your choice and start exploring the exciting world of regression algorithms!
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Regression Algorithms: Learn about a variety of regression algorithms and their practical implementations.
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Hands-on Experience: Gain valuable hands-on experience by running and modifying the provided notebooks.
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Experimentation: Feel free to experiment with different datasets, parameters, and techniques to deepen your understanding.
This repository is licensed under the MIT License.
Your contributions are highly appreciated! Whether it's improving existing content, adding new algorithms, or fixing bugs, feel free to open issues or submit pull requests. Together, we can create a valuable resource for the machine learning community.
Explore the fascinating world of regression algorithms through practical implementations. Enhance your skills, gain practical experience, and discover the diverse possibilities of machine learning algorithms.
Happy learning and happy experimenting!
Project Author: Muhammed Thahseer CK
Self-taught Data Scientist