Metadata-Version: 2.4
Name: motorfaultsvm
Version: 2.0.1
Summary: A Machine Learning library for Motor Fault Detection using Support Vector Machine (SVM)
Author: Abdul Rafeul Mallick
Author-email: Abdul Rafeul Mallick <arafeul97@gmail.com>
Maintainer-email: Abdul Rafeul Mallick <arafeul97@gmail.com>
License: MIT
Project-URL: Homepage, https://github.com/Rafeul1997/MotorFaultSVM
Project-URL: Repository, https://github.com/Rafeul1997/MotorFaultSVM
Project-URL: Documentation, https://github.com/Rafeul1997/MotorFaultSVM#readme
Project-URL: Issues, https://github.com/Rafeul1997/MotorFaultSVM/issues
Keywords: machine-learning,svm,motor-fault,fault-detection,predictive-maintenance,industrial-ai,python-ml
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scikit-learn
Dynamic: author
Dynamic: license-file

# \# MotorFaultSVM ⚙️🤖

# 

# A \*\*Machine Learning library for Motor Fault Detection\*\* using Support Vector Machine (SVM).

# 

# This package enables predictive maintenance by analyzing sensor data and classifying motor conditions as \*\*Normal\*\* or \*\*Faulty\*\* using trained ML models.

# 

# \---

# 

# \## 📌 Key Features

# 

# \- ⚡ SVM-based fault classification

# \- 📊 Works with numerical sensor datasets

# \- 🧠 Built on Scikit-learn ML pipeline

# \- 🔍 Simple Python API for predictions

# \- 🏭 Designed for industrial predictive maintenance

# \- 📦 Ready for research and production use

# 

# \---

# 

# \## 📥 Installation

# 

# \### Install from PyPI

# 

# ```bash

# pip install motorfaultsvm





from motorfaultsvm import predict



\# Example sensor data (vibration, temperature, current, etc.)

data = \[

&#x20;   \[0.12, 0.45, 0.67, 0.89],

&#x20;   \[0.20, 0.50, 0.70, 0.95]

]



\# Run prediction

result = predict(data)



print("Prediction Result:")

print(result)

