| Name | sxc JSON |
| Version |
0.1.8
JSON |
| download |
| home_page | None |
| Summary | For academic and educational use under St. Xavier's College only. |
| upload_time | 2025-10-30 02:59:54 |
| maintainer | None |
| docs_url | None |
| author | SOMA |
| requires_python | >=3.7 |
| license | MIT License
Copyright (c) 2025 SOMA
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. |
| keywords |
machine-learning
classification
regression
data-science
automated-ml
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
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|
SXC is a Python library designed for academic and educational use, providing intuitive access to machine learning algorithms, nonparametric smoothing techniques, and classical statistical learning workflows. Inspired by Scikit-Learn’s clean API principles, SXC ensures that users can perform data analysis with consistent, modular, and user-friendly interfaces.
---
## Features
### Supervised Learning
- Decision Tree Classifier & Regressor
- Random Forest Classifier & Regressor
- K-Nearest Neighbors Classification & Regression
- Naive Bayes Classifier
- Support Vector Machines (Classification & Regression)
- Bagging for both Regression and Classification
### Nonparametric Regression & Smoothing
Located under `sxc.mdts.adv_reg`:
- Bin Smoother
- KNN Smoother
- Kernel Weighted Regression
- LOWESS (Locally Weighted Regression)
- LWR (Local Weighted Regression variants)
### Clustering
- K-Means clustering
### Utility
- Indexing helper for dataset preprocessing
---
## Installation
```bash
pip install sxc
```
This will install all core dependencies required to run the machine learning modules inside SXC.
## Quick Start
```py
from sxc.mdts.decision_tree_classifier import DecisionTreeClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# Load toy dataset
X, y = load_iris(return_X_y=True)
# Train-test split
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.3, random_state=42
)
# Initialize the model
model = DecisionTreeClassifier()
# Fit and predict
model.fit(X_train, y_train)
preds = model.predict(X_test)
```
All algorithms included are intended for educational demonstrations and experiments. SXC facilitates rapid learning of:
Machine learning theory
Practical model building
Nonparametric regression foundations
For academic citation or publication:
## Patra, S. (2025).
SXC: Statistical & Explainable Computing (Version 0.1.x).
Python package available at PyPI.
# Contributing
Contributions are welcomed. Kindly create pull requests via GitHub or report issues to help improve stability, documentation, and performance.
## License
Licensed under the MIT License. Free to view, modify, and learn from.
## Acknowledgement
Developed with passion for the community of statisticians, data scientists, and research enthusiasts at St. Xavier’s College.
Keep experimenting. Keep learning. Keep computing elegantly.
Raw data
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"description": "SXC is a Python library designed for academic and educational use, providing intuitive access to machine learning algorithms, nonparametric smoothing techniques, and classical statistical learning workflows. Inspired by Scikit-Learn\u2019s clean API principles, SXC ensures that users can perform data analysis with consistent, modular, and user-friendly interfaces.\n\n\n---\n\n## Features\n\n### Supervised Learning\n- Decision Tree Classifier & Regressor\n- Random Forest Classifier & Regressor\n- K-Nearest Neighbors Classification & Regression\n- Naive Bayes Classifier\n- Support Vector Machines (Classification & Regression)\n- Bagging for both Regression and Classification\n\n### Nonparametric Regression & Smoothing\nLocated under `sxc.mdts.adv_reg`:\n- Bin Smoother\n- KNN Smoother\n- Kernel Weighted Regression\n- LOWESS (Locally Weighted Regression)\n- LWR (Local Weighted Regression variants)\n\n### Clustering\n- K-Means clustering\n\n### Utility\n- Indexing helper for dataset preprocessing\n\n---\n\n## Installation\n\n```bash\npip install sxc\n``` \nThis will install all core dependencies required to run the machine learning modules inside SXC.\n\n## Quick Start\n```py\nfrom sxc.mdts.decision_tree_classifier import DecisionTreeClassifier\nfrom sklearn.datasets import load_iris\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\n\n# Load toy dataset\nX, y = load_iris(return_X_y=True)\n\n# Train-test split\nX_train, X_test, y_train, y_test = train_test_split(\n X, y, test_size=0.3, random_state=42\n)\n\n# Initialize the model\nmodel = DecisionTreeClassifier()\n\n# Fit and predict\nmodel.fit(X_train, y_train)\npreds = model.predict(X_test)\n```\n\nAll algorithms included are intended for educational demonstrations and experiments. SXC facilitates rapid learning of:\n\nMachine learning theory\nPractical model building\nNonparametric regression foundations\nFor academic citation or publication:\n\n## Patra, S. (2025).\nSXC: Statistical & Explainable Computing (Version 0.1.x).\nPython package available at PyPI.\n\n# Contributing\nContributions are welcomed. Kindly create pull requests via GitHub or report issues to help improve stability, documentation, and performance.\n\n## License\nLicensed under the MIT License. Free to view, modify, and learn from.\n\n## Acknowledgement\nDeveloped with passion for the community of statisticians, data scientists, and research enthusiasts at St. Xavier\u2019s College.\n\nKeep experimenting. Keep learning. Keep computing elegantly.\n",
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