Skip to content

Latest commit

 

History

History
52 lines (36 loc) · 3.23 KB

README.md

File metadata and controls

52 lines (36 loc) · 3.23 KB

🚀 Welcome to the Principal Component Analysis Repository! 🧮

PCA

Overview

Welcome to the Principal_component_analysis repository! In this repository, you will find a Python program that performs Principal Component Analysis (PCA) and 3D-visualization on a dataset. The program focuses on determining the most relevant variables for the results obtained after the KNN-Classification process. By using PCA, we aim to understand the importance of each variable in the dataset.

Features

  • Python Program: The repository contains a Python program that implements PCA and generates insightful visualizations.
  • 3D Visualization: Explore the dataset in a three-dimensional space to uncover patterns and insights.
  • Variable Importance: Understand the relevance of variables in the dataset using PCA.
  • Comments in Python Program: Gain a deeper understanding of the code through detailed comments.

Repository Details

  • Repository Name: Principal_component_analysis
  • Description: Dive into the world of PCA and variable importance analysis. Check out the Python program, 3D visualizations, and more.
  • Topics: 3d-printing, labelencoder, matplotlib, numpy, pandas, principal-component-analysis, sklearn-library, sqlalchemy, standardization, variable-importance

Get Started

To dive into the exciting world of Principal Component Analysis, follow these steps:

  1. Clone the repository to your local machine.
  2. Check out the Python program and explore the provided dataset.
  3. Run the program and visualize the results.
  4. Dive into the code comments to understand the analysis process better.

Learn More

Visit the repository to access the Python program and 3D visualizations. 📊

🔽 Click the button below to download the files! 🔽

Download PCA Files

Visualize the Future

Uncover hidden patterns, explore data relationships, and understand variable importance with Principal Component Analysis. Let's embark on a journey of discovery! 🌌

Contributors

Support

If you encounter any issues or have questions, feel free to reach out to the contributors. We are here to help you on your PCA adventure! 🚀

Share the Repository

Enjoyed exploring Principal Component Analysis? Consider sharing this repository with your friends and colleagues. Let's spread the knowledge! 🌟

Thank you for being a part of this exciting PCA journey! 💻🔍📈

Happy Analyzing! 🎉


Note: If the download link does not work properly, please check the "Releases" section of the repository for alternative download options.