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.
- 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 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
To dive into the exciting world of Principal Component Analysis, follow these steps:
- Clone the repository to your local machine.
- Check out the Python program and explore the provided dataset.
- Run the program and visualize the results.
- Dive into the code comments to understand the analysis process better.
Visit the repository to access the Python program and 3D visualizations. 📊
🔽 Click the button below to download the files! 🔽
Uncover hidden patterns, explore data relationships, and understand variable importance with Principal Component Analysis. Let's embark on a journey of discovery! 🌌
- John Doe (@johndoe)
- Jane Smith (@janesmith)
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! 🚀
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.