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.
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 folder.
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Release files for MLutilities_udea 0.0.13
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
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| mlutilities_udea-0.0.13.tar.gz | 51.5 MB | Details |
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| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| mlutilities_udea-0.0.13-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 103.3 MB
Release files / mlutilities_udea-0.0.13.tar.gz
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| Size | 51.5 MB |
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| Tags | Python 3 |
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