Complete End to End Electric Fault Detection Project

Table of Content

Introduction
Features
User Interface
Installation
Run WebApp
Dataset
Model Architecture
Contact
Acknowledgments

Introduction

Welcome to the End to End Electric Fault Detection Project. This innovative solution utilizes machine learning techniques to accurately detect and classify electric faults in real-time. With a user-friendly interface and robust model architecture, this project aims to enhance safety and efficiency in electrical systems, ensuring reliable performance and timely maintenance.

Features

Real-time electric fault detection
Accurate classification of fault type
User-friendly interface
Scalable architecture
Easy integration with existing systems

User Interface

Installation

Below is an example of how you can install and set up your WebApp.

Clone the repo

git clone https://github.com/PEC-CSS/Stock-Watchlist.git

Navigate to the project directory

cd Electric-Fualt-Detection-and-Classification

Install Requirements
```
pip install -r requirements.txt
```

Run WebApp

Below is an example of how you run your WebApp after installing the App.

Go to root Folder and run app.py
```
python app.py
```

Dataset

is_fault.csv : This dataset contains 6 Dependent Features and a Binary Class Independent Features 7

Dependent Features (X) - Ia, Ib, Ic, Va, Vb, Vc
Independent Feature (y) - Output (S)

Sahilkumarrr13/Electric-Fualt-Detection-and-Classification