ML Pipeline
A machine learning pipeline for Exploratory Data Analysis (EDA), data preprocessing, model training, and evaluation. This package helps to quickly analyze datasets, preprocess data, and train models like Random Forest or XGBoost with detailed evaluation metrics.
Features
-Upload any csv dataset and run ML Pipeline from EDA to Model validation
- Perform EDA and visualize data
- Handle missing values and scale data
- Encode categorical data
- Train Random Forest or XGBoost models
- Evaluate models using F1 score, R², MSE, etc.
Installation
You can install the package via pip:
pip install ml_pipeline_dashboard
> launch_ml_pipeline
1 maintainer
Metadata
1 maintainer
Release files for ml-pipeline-dashboard 0.1.15
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| ml_pipeline_dashboard-0.1.15.tar.gz | 7.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| ml_pipeline_dashboard-0.1.15-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 16.8 kB
Release files / ml_pipeline_dashboard-0.1.15.tar.gz
| Download URL | ml_pipeline_dashboard-0.1.15.tar.gz |
|---|---|
| Size | 7.7 kB |
| 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 | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.1 CPython/3.12.7
|
Release files / ml_pipeline_dashboard-0.1.15-py3-none-any.whl
| Download URL | ml_pipeline_dashboard-0.1.15-py3-none-any.whl |
|---|---|
| Size | 9.1 kB |
| 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 | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.1 CPython/3.12.7
|
