https://github.com/adi3042/sensor-fault-detection

🔍⚙️ Ensure Reliable Operations! Detect anomalies and prevent disruptions with our Sensor Fault Detection system. Explore advanced classification and regression techniques to identify and address sensor faults effectively. Your path to robust and accurate sensor data begins here! 🚨🔧 SensorFaultTech
https://github.com/adi3042/sensor-fault-detection

classification css datetime fault-detection flask functools html ipykernel jupternotebook machine-learning numpy pandas python3 readme regression scikit-learn sensor setuptools venv

Last synced: about 19 hours ago
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🔍⚙️ Ensure Reliable Operations! Detect anomalies and prevent disruptions with our Sensor Fault Detection system. Explore advanced classification and regression techniques to identify and address sensor faults effectively. Your path to robust and accurate sensor data begins here! 🚨🔧 SensorFaultTech

• Host: GitHub
• URL: https://github.com/adi3042/sensor-fault-detection
• Owner: Adi3042
• License: mit
• Created: 2024-02-12T18:30:02.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2024-08-23T09:03:18.000Z (3 months ago)
• Last Synced: 2024-08-23T10:24:57.193Z (3 months ago)
• Topics: classification, css, datetime, fault-detection, flask, functools, html, ipykernel, jupternotebook, machine-learning, numpy, pandas, python3, readme, regression, scikit-learn, sensor, setuptools, venv
• Language: Jupyter Notebook
• Homepage:
• Size: 1.21 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# 📄✏ Sensor Fault Detection Project

**Brief:** In electronics, a **wafer** (also called a slice or substrate) is a thin slice of semiconductor, such as a crystalline silicon (c-Si), used for the fabrication of integrated circuits and, in photovoltaics, to manufacture solar cells. The wafer serves as the substrate(serves as foundation for contruction of other components) for microelectronic devices built in and upon the wafer. 

It undergoes many microfabrication processes, such as doping, ion implantation, etching, thin-film deposition of various materials, and photolithographic patterning. Finally, the individual microcircuits are separated by wafer dicing and packaged as an integrated circuit.

#### Dataset is taken from Kaggle and stored in mongodb

💿 Installing

1. Environment setup.

```

conda create --prefix venv python==3.8 -y

```

```

conda activate venv/

````

2. Install Requirements and setup

```

pip install -r requirements.txt

```

5. Run Application

```

python app.py

```

🔧 Built with

- flask

- Python 3.8

- Machine learning

- Scikit learn

- 🏦 Industrial Use Cases