Metadata-Version: 2.1
Name: MLutilities_udea
Version: 0.0.8
Summary: Library to teach Machine Learning
Author: omarcastano
Author-email: omar.castano@udea.edu.co
Requires-Python: >=3.9.0,<=3.11.0
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Dist: feature-engine (>=1.6.2,<2.0.0)
Requires-Dist: ipywidgets (>=8.1.1,<9.0.0)
Requires-Dist: jinja2 (>=3.1.2,<4.0.0)
Requires-Dist: lofo-importance (>=0.3.3,<0.4.0)
Requires-Dist: mlxtend (>=0.23.0,<0.24.0)
Requires-Dist: nbformat (>=5.9.2,<6.0.0)
Requires-Dist: pandas (<2.0.0)
Requires-Dist: plotly (>=5.18.0,<6.0.0)
Requires-Dist: scikit-plot (>=0.3.7,<0.4.0)
Requires-Dist: seaborn (>=0.13.0,<0.14.0)
Requires-Dist: statsmodels (>=0.14.0,<0.15.0)
Requires-Dist: yellowbrick (>=1.5,<2.0)
Description-Content-Type: text/markdown

# MLutilities
This python library aims to provide modules that can be useful to teach Data Analysis and Machine Learning.



Installation: to install this package simple use the following command
```
pip install mlutilities-udea
```
# Basi Usage
Using the mlutilities library for Exploratory Data Analysis (EDA)

### Univariant Analysis
In this example, we demonstrate how to use the mlutilities library to load a dataset, perform the Kolmogorov-Smirnov goodness-of-fit test, and visualize the data.

```python
from mlutilities.datasets import load_dataset
from mlutilities.eda import kolmogorov_test

# First, we load the "penguins" dataset into a Pandas DataFrame.
data = load_dataset(data_set="penguins", load_as="dataframe", n=-1)

# We print the description of the dataset to provide some information about it.
print(data["DESC"])

# We display an image associated with the dataset.
display(data['image'])

# Next, we extract the data from the dataset for further analysis.
df = data["data"]

# We perform the Kolmogorov-Smirnov test on the "bill_depth_mm" variable and plot its histogram.
kolmogorov_test(dataset=df, variable="bill_depth_mm", plot_histogram=True)
```

You can find more example on the [notebooks](./notebooks/) folder.

