Particle Classifier Using Machine Learning.

Table of Contents

• About the Project
  • Introduction
  • Built With
  • Algorithms/Classification Models Used
  • Data Set
• Implemention
• Finding The Best Classification Model
• Conclusion
• Author

About The Project

Introduction

This Project is Based On Particle Classification.

In order to analyse these data is necessary understand some basic concepts.

• The Standard Model - The building blocks of matter are elementary particles. The Standard Model also studies the interaction of these particles through fundamental forces (strong, weak and electromagnetic). For every type of particle there also exists a corresponding antiparticle.
• Pion In particle physics, a pion (or a pi meson, denoted with the Greek letter pi: π) is any of three subatomic particles: π0, π+, and π−. Each pion consists of a quark and an antiquark and is therefore a meson.
• So in this project We are Classifying whether the particle is Pion or not.

Built With

Python

Python Libraries Used

• MatplotLib
• Pandas
• Numpy
• Seaborn
• Scikit-Learn

Algorithms/Classification Models Used

Logistic Regression

K-Nearest Neighbors

Decision Tree Learning

Random forest

Data Set

This Large dataset is from Kaggle ,a simulation of electron-proton inelastic scattering measured by a particle detector system.

Implementation

This Project is Implementated on Python Notebook Jupyter Link

Finding The Best Classification Model

So for Model With Best Accuracy Score will be the best classification model

Classification Model Name	Accuracy Score (in %)
Logistic Regression	94.98
K-Nearest Neighbors	94.01
Decision Tree	96.31
Random Forest	97.43

Conclusion

So,We conclude that Random Forest Classification with Accuracy of 97.43% is Best model that can be used for Classification.

Author

Venkatesh Tripathi