This repository contains a collection of key machine learning algorithms and data preprocessing techniques implemented in Python. Each notebook provides a detailed example of how the algorithm works, complete with visualizations and explanations.
This repository provides implementations of some of the most important machine learning algorithms, including regression, classification, clustering, and dimensionality reduction techniques. The examples are presented in Jupyter Notebooks, making it easy to understand and experiment with each algorithm.
The repository covers topics like:
- Regression Analysis
- Clustering Techniques
- Dimensionality Reduction
- Frequent Pattern Mining
Here is a list of algorithms and techniques included in this repository:
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Regression Algorithms
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Clustering Algorithms
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Dimensionality Reduction
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Frequent Pattern Mining
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Other Concepts
The datasets used in the notebooks are either built-in datasets from libraries like Scikit-learn or custom data files. If any specific dataset is required, it will be mentioned within the notebook itself.
To use the algorithms and explore the examples:
- Clone the repository:
git clone https://github.com/KhushAgrawal001/Some_Important_Algo.git
- Navigate into the repository:
cd Some_Important_Algo - Open the Jupyter notebooks to explore each algorithm:
jupyter notebook
To run the code, you need Python 3.x and the following libraries:
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Scikit-learn
You can install the required libraries by running:
pip install numpy pandas matplotlib seaborn scikit-learnContributions are welcome! If you have an improvement or a new algorithm to add, feel free to submit a pull request.
Explore the algorithms, learn how they work, and apply them to your own projects!