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The **Wafer Fault Detection** project involves developing a machine learning model to accurately identify manufacturing defects in semiconductor wafers, ensuring quality control and reducing production losses. It leverages data-driven insights to detect anomalies and optimize the fabrication process.

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Kumbhakarn/WaferFaultPrediction

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📄✏ Sensor Fault Detection Project

The aim of this project is to develop an automated sensor fault detection system for Scania trucks that can identify and diagnose sensor faults in real-time. The system should be able to detect faults in a wide range of sensors, including those used to monitor engine performance, fuel efficiency, and safety features. The goal is to improve the overall reliability and safety of Scania trucks by quickly identifying and addressing sensor faults, reducing the risk of accidents and downtime caused by equipment failure.

Dataset is taken from Kaggle and stored in mongodb

💿 Installing

  1. Environment setup.
conda create --prefix venv python==3.10 -y
conda activate venv/
  1. Install Requirements and setup
pip install -r requirements.txt
  1. Run Application
python app.py

🔧 Built with

  • flask
  • Python 3.10
  • Machine learning
  • Scikit learn
  • 🏦 Industrial Use Cases

About

The **Wafer Fault Detection** project involves developing a machine learning model to accurately identify manufacturing defects in semiconductor wafers, ensuring quality control and reducing production losses. It leverages data-driven insights to detect anomalies and optimize the fabrication process.

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