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---
title: Manufacturing Downtime Prediction API
emoji: 📉
colorFrom: green
colorTo: red
sdk: docker
pinned: false
license: apache-2.0
---
# Manufacturing Downtime Prediction
## Project Links:
* **[Deployed FastAPI](https://omdena-jakarta-traffic-system.streamlit.app/)**
* **[Detailed Kaggle Notebook](https://www.kaggle.com/code/sudhanshu2198/machine-defect-prediction)**
## Background
- The Manufacturing Downtime Dataset contains information about the operational parameters of various machines and their downtime records.
- Analyze machine performance, predict potential failures, and develop predictive maintenance strategies based on operational parameters.
- Features
- Torque(Nm)
- Hydraulic_Pressure(bar)
- Cutting(kN)
- Coolant_Pressure(bar)
- Spindle_Speed(RPM)
- Coolant_Temperature
- Target
- Downtime
## Directory Tree
```bash
├── app
│ ├── __init__.py
│ ├── main.py
│ ├── modelling.py
│ └── inference.py
├── README.md
├── requirements.txt
├── Manufacturing_Downtime_Dataset.csv
└── .gitignore
```
## Run Webapp Locally
Clone the project
```bash
git clone https://github.com/sudhanshu2198/Manufacturing-Downtime-Prediction-API
```
Change to project directory
```bash
cd Manufacturing-Downtime-Prediction-API
```
Create Virtaul Environment and install dependencies
```bash
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
```
Run Locally
```bash
uvicorn app.main:app
```
cURL Commands
1) Upload
```bash
Request
curl -X 'POST' \
'http://127.0.0.1:8000/upload/' \
-H 'accept: application/json' \
-H 'Content-Type: multipart/form-data' \
-F 'uploaded_file=@Manufacturing_Downtime_Dataset.csv;type=text/csv'
Response
{
"file": "Manufacturing_Downtime_Dataset.csv",
"content": "text/csv",
"path": "dataset.csv"
}
```
2) Train
```bash
Request
curl -X 'POST' \
'http://127.0.0.1:8000/train/' \
-H 'accept: application/json' \
-d ''
Response
{
"Accuracy": 0.9897750511247444,
"F1_Score": 0.9896049896049895
}
```
3) Predict
```bash
Request 1
curl -X 'POST' \
'http://127.0.0.1:8000/predict/' \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"Torque": 28.38124,
"Hydraulic_Pressure": 131.265854,
"Cutting": 2.01,
"Coolant_Pressure": 4.982836,
"Spindle_Speed": 20033.0,
"Coolant_Temperature": 20.1
}'
Response 1
{
"Downtime": "No",
"Confidence": 0.87
}
Request 2
curl -X 'POST' \
'http://127.0.0.1:8000/predict/' \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"Torque": 25.614444,
"Hydraulic_Pressure": 98.7,
"Cutting": 3.49,
"Coolant_Pressure": 6.839413,
"Spindle_Speed": 18638.0,
"Coolant_Temperature": 24.4
}'
Response 2
{
"Downtime": "Yes",
"Confidence": 0.98
}
``` |