MaiChaiMouseButMe

Analyze and visualize gaming mouse specs using PCA (Principal Component Analysis).

Project Overview

MaiChaiMouseButMe is a web app for exploring, searching, and analyzing gaming mouse data. It uses PCA to help you find the best mouse for your needs, compare models, and understand feature importance. Built for the course 01076032 Elementary Differential Equations and Linear Algebra.

Team Members

Name	Student ID
Nethitorn Suthumwaraporn	67010497
Paramed Rojlorsakul	67010534
Patcharapol Kumpichit	67010621
Teepob Mahasuk	67011464

Features

• Search and view mouse specs from a curated dataset
• Add new mouse models to your collection
• See detailed mouse info in a modal popup
• Select features for PCA analysis
• Visualize PCA results and recommendations
• Step-by-step PCA math explanation
• Interactive Plotly graphs

Dataset

Source: Kaggle - Gaming Mouse Specs

Getting Started

1. Clone the repository

git clone https://github.com/PluemDontKnowToCode/MaiChaiMouseButMe
cd MaiChaiMouseButMe

2. Create and activate a virtual environment (optional)

# Windows
python -m venv myenv
myenv\Scripts\activate
# Linux/Mac
python3 -m venv myenv
source myenv/bin/activate

3. Install dependencies

pip install pandas scikit-learn plotly numpy flask jupyter notebook

4. Run the web app

python app.py

Visit http://localhost:5000 in your browser.

5. (Optional) Run Jupyter Notebook

# Windows
run run.bat
# Linux/Mac
jupyter notebook main.ipynb

Usage

• Search for mouse models and view details
• Add your own mouse to the collection
• Select features and run PCA analysis
• View recommendations and PCA math steps

Credits

• Dataset: ellimaaac (Kaggle)
• Author: PluemDontKnowToCode

License

For educational use in 01076032 course.