Metadata-Version: 2.1
Name: mlglass
Version: 0.0.1
Summary: MLglass: A Transparency with models
Home-page: https://github.com/Nikeshbajaj/mlglass
Author: Nikesh Bajaj
Author-email: bajaj.nikey@gmail.com
License: MIT
Download-URL: https://github.com/Nikeshbajaj/mlglass/tarball/0.0.1
Keywords: Machine Learning,Visualizations,Weights,Decision Tree,Naive Bayes
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Development Status :: 5 - Production/Stable
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: matplotlib
Requires-Dist: scipy
Requires-Dist: scikit-learn
Requires-Dist: python-picard

# Machine Learning Models with glass (Transparency)

### Links: **[Github](https://github.com/Nikeshbajaj/mlglass)**  |  **[PyPi - project](https://pypi.org/project/mlglass/)**
### Installation: *[pip install spkit](https://pypi.org/project/mlglass/)*


-----
## Table of contents
-[Logistic Regression](#logistic-regression---view-in-notebook)
-[Naive Bayes](#naive-bayes---view-in-notebook)
-[Decision Trees](#decision-trees---view-in-notebook)
-----


## Installation

**Requirement**:  numpy, matplotlib

### with pip

```
pip install mlglass
```

### Build from the source
Download the repository or clone it with git, after cd in directory build it from source with

```
python setup.py install
```

#### Machine Learning models - with visualizations
* Logistic Regression
* Naive Bayes
* Decision Trees
* DeepNet (to be updated)


## Machine Learning
### [Logistic Regression](https://nbviewer.jupyter.org/github/Nikeshbajaj/spkit/blob/master/notebooks/2.1_LogisticRegression_examples.ipynb) - *View in notebook*
<p align="center"><img src="https://raw.githubusercontent.com/Nikeshbajaj/MachineLearningFromScratch/master/LogisticRegression/img/example5.gif" width="600"/></p>

### [Naive Bayes](https://nbviewer.jupyter.org/github/Nikeshbajaj/spkit/blob/master/notebooks/2.2_NaiveBayes_example.ipynb) - *View in notebook*
<p align="center"><img src="https://raw.githubusercontent.com/Nikeshbajaj/MachineLearningFromScratch/master/Probabilistic/img/FeatureDist.png" width="600"/></p>

### [Decision Trees](https://nbviewer.jupyter.org/github/Nikeshbajaj/spkit/blob/master/notebooks/2.3_Tree_Example_Classification_and_Regression.ipynb) - *View in notebook*

[**[source code]**](https://github.com/Nikeshbajaj/spkit/blob/master/examples/trees_example.py) | [**[jupyter-notebook]**](https://nbviewer.jupyter.org/github/Nikeshbajaj/spkit/blob/master/notebooks/2.3.1_Trees_Classification_Example.ipynb)
<p align="center">
<img src="https://raw.githubusercontent.com/Nikeshbajaj/spkit/master/figures/tree_sinusoidal.png" width="800"/>
<img src="https://raw.githubusercontent.com/Nikeshbajaj/spkit/master/figures/trees.png" width="800"/>
</p>


#### Plottng tree while training

<p align="center"><img src="https://raw.githubusercontent.com/Nikeshbajaj/MachineLearningFromScratch/master/Trees/img/a123_nik.gif" width="600"/></p>

[**view in repository **](https://github.com/Nikeshbajaj/spkit/tree/master/notebooks)

______________________________________

# Contacts:

* **Nikesh Bajaj**
* http://nikeshbajaj.in
* n.bajaj@qmul.ac.uk
* n.bajaj@uel.ac.uk
* bajaj.nikkey@gmail.com
### PhD from Queen Mary University of London, Postdoctoral at University of East London
______________________________________


