semvec/pypi-packages huggingface dataset:
islanders 2022.1.18
module designed to make your data preprocessing experience easier
Keywords: Python,Machine Learning,Artificial Intelligence,Data Science,Data Cleaning,isladner robotics

# Islanders
this is a python packaged dessigned to make artificial Intelligence engineers, machine learning engineers, and Data 
Sciencist lives easier to do there job. within this package you will have the ability to use irdatacleanings  methods as well as

- datasets: this method is for those part of the islander community where you will be able to run code like,
```python
import islanders as ir
gifts = ir.dataset("amazon electronic")
titanic = ir.dataset("titanic")
movie = ir.datasets("movie")
```
## DT
this class is designed to work with scikit-learns DecisionTreeClassifier
by making it so that all you have to do when you initiate this class is set the X and Y values
DT(X values,Y values, test_size=.2)
once you have initialized the class you can build the model by calling the build merthod there are two ways to call this methodthe first way is
dt = self.build() which will just return the model itself, the next way is a bit more useful I think,
dt,X_test,y_test = dec.build(True) this method will return not just the model but the X_test data and the y_test allowing you to run test on the model. 
This class is designed to return the most optimized model you can possible have
the last method allows you to see what your tree looks like you can call this method by running
self.show()
IslanderDataPreprocessing 2022.1.18
module designed to make your data preprocessing experience easier
Keywords: python,machine Learning,Artificial Intelligence,Data Science,Data Cleaning

# IslanderDataPreprocessing

This python package was designed to help python developers
who want to get in to machine Learning have an easier way to do so.
currently there are 6 modules in this package, this number will grow
as the Islander Robotics Community works more and more with machine
Learning and Artificial Intelligence.

#DataBase

DataBase(API_command)
currently this module only works with downloading and installing
datasets from the Kagle website.

API_command: this is where you will insert the designated API command from the
Kagle website

datasets: this list is where all of the datasets you downloaded at the current
time of running the module will be stored as a pandas DataFrame
allowing you the ability to save time by not having to uploaded the datasets again.

filename: this module will allow you the ability to see what index each file is stored in
the datasets list. as well as you will have the ability to know what files where downloaded
from Kagle.

#DataCorrelation

DataCorrelation(df)
This module is designed to allow you the ability to see what parts of your data have high correlation between them
from there you will be able to use the information to combine those columns for the best results