https://github.com/jelhamm/singular-value-decomposition-data-mining

"This repository hosts an implementation of the Singular Value Decomposition (SVD) algorithm tailored for data mining tasks. SVD is utilized for efficient dimensionality reduction, aiding in the extraction of key patterns and features from large and complex datasets."
https://github.com/jelhamm/singular-value-decomposition-data-mining

data-analysis dimension-reduction jyputer-notebook machine-learning matplotlib numpy-library pandas-library preprocessing python scipy-library singular-value-decomposition sklearn-library standardscaler svd svd-matrix-factorisation

Last synced: 21 days ago
JSON representation

"This repository hosts an implementation of the Singular Value Decomposition (SVD) algorithm tailored for data mining tasks. SVD is utilized for efficient dimensionality reduction, aiding in the extraction of key patterns and features from large and complex datasets."

• Host: GitHub
• URL: https://github.com/jelhamm/singular-value-decomposition-data-mining
• Owner: jElhamm
• License: apache-2.0
• Created: 2024-05-10T18:37:14.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-07-21T18:34:03.000Z (almost 2 years ago)
• Last Synced: 2025-04-04T01:18:07.737Z (about 1 year ago)
• Topics: data-analysis, dimension-reduction, jyputer-notebook, machine-learning, matplotlib, numpy-library, pandas-library, preprocessing, python, scipy-library, singular-value-decomposition, sklearn-library, standardscaler, svd, svd-matrix-factorisation
• Language: Jupyter Notebook
• Homepage: https://www.kaggle.com/datasets/mexwell/heart-disease-dataset
• Size: 271 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# Singular Value Decomposition (SVD) Data Mining

   This project demonstrates the implementation of [**Singular Value Decomposition (SVD)**](https://en.wikipedia.org/wiki/Singular_value_decomposition) using various popular libraries such as [NumPy](https://numpy.org/), [SciPy](https://scipy.org/), and [PyTorch](https://pytorch.org/). Each implementation is contained within its own [Jupyter Notebook](https://jupyter.org/), providing a comprehensive and detailed guide on how to perform SVD using these different tools.

## Rep

ository Structure

   - [Implementation of SVD using NumPy](SVD_Implement_With_NumPy.ipynb)

   - [Implementation of SVD using SciPy](SVD_Implement_With_Scipy_NumPy.ipynb)

   - [Implementation of SVD using PyTorch](SVD_Implement_With_PyTorch.ipynb)

## Dataset

   The dataset used in this project is the [`heart_statlog_cleveland_hungary_final.csv`](heart_statlog_cleveland_hungary_final.csv), which combines heart disease data from various sources.

   This dataset includes numerous attributes related to heart disease, and it is a common benchmark for evaluating classification algorithms.

## Requirements

   To run these notebooks, you will need the following libraries installed in your Python environment:

   | Library      | Version     | Implementation                |

   |--------------|-------------|-------------------------------|

   | NumPy        | >= 1.21.0   | All implementations           |

   | SciPy        | >= 1.7.0    | SVD_Implement_With_Scipy_NumPy|

   | PyTorch      | >= 1.9.0    | SVD_Implement_With_PyTorch    |

   | Pandas       | >= 1.3.0    | All implementations           |

   | Seaborn      | >= 0.11.0   | SVD_Implement_With_PyTorch    |

   | TensorFlow   | >= 2.5.0    | All implementations           |

   | Matplotlib   | >= 3.4.2    | All implementations           |

   | Scikit-Learn | >= 1.0.0    | All implementations           |

   You can install these dependencies using pip:

```bash

   pip install numpy scipy torch tensorflow pandas seaborn matplotlib scikit-learn

```

## Overview

   * Implement With NumPy

   This code details the step-by-step process of implementing SVD from scratch using NumPy.

      It covers the following steps:

      - Data preprocessing

      - Custom implementation of matrix transpose

      - Custom implementation of eigenvalue decomposition

      - Computing the singular values

      - Computing left and right singular vectors

      - Dimensionality reduction

   * Implement With SciPy and NumPy

   This code shows how to leverage SciPy's linear algebra capabilities to implement SVD.

      It includes:

      - Using SciPy for matrix operations

      - Performing SVD directly using SciPy's `svd` function

      - Verifying the results against the NumPy implementation

      - Dimensionality reduction

   * Implement With PyTorch

   Here, we utilize PyTorch for implementing SVD, which is particularly useful for those familiar with deep learning frameworks.

      This notebook covers:

      - Utilizing PyTorch tensors for data representation

      - Implementing SVD using PyTorch's linear algebra functions

      - Comparing performance and results with NumPy and SciPy implementations

      - Dimensionality reduction

## Results and Analysis

   Each code concludes with a section on results and analysis, where we evaluate the performance of the LDA implementations on the heart disease dataset.

   We visualize the transformed data and discuss the effectiveness of LDA in dimensionality reduction and classification.

## License

   This repository is licensed under the Apache License 2.0.

   See the [LICENSE](./LICENSE) file for more details.