Research Vexp Tool
An offline, GUI-based machine learning application for predicting Vexp (kN) using engineering feature ratios and a trained ensemble model.
Features
- Offline prediction (no internet required)
- GUI-based input (14 engineering parameters)
- Encrypted ML model and scaler
- Consistent preprocessing using trained StandardScaler
- Easy installation via pip
Installation
>>> pip install research-vexp-tool
>>> main
1 maintainer
Metadata
1 maintainer
Release files for Research-Tool 0.1.2.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| research_tool-0.1.2.3.tar.gz | 42.4 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| research_tool-0.1.2.3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 84.7 MB
Release files / research_tool-0.1.2.3.tar.gz
| Download URL | research_tool-0.1.2.3.tar.gz |
|---|---|
| Size | 42.4 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.12.6
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Release files / research_tool-0.1.2.3-py3-none-any.whl
| Download URL | research_tool-0.1.2.3-py3-none-any.whl |
|---|---|
| Size | 42.4 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.12.6
|
