Metadata-Version: 2.4
Name: VKPyKit
Version: 0.3.13
Summary: Packaged functions for Machine Learning and Data Science tasks.
Author: Vishal Khapre
Author-email: Vishal Khapre <assignarc@gmail.com>
Maintainer-email: Vishal Khapre <assignarc@gmail.com>
License-Expression: MIT
Project-URL: Homepage, https://github.com/assignarc/VKPyKit
Project-URL: Bug Reports, https://github.com/assignarc/VKPyKit/issues
Project-URL: Funding, https://donate.pypi.org
Project-URL: Source, https://github.com/assignarc/VKPyKit
Keywords: Python,Machine Learning,data science,utilities
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: matplotlib
Requires-Dist: seaborn
Requires-Dist: openpyxl
Provides-Extra: dev
Requires-Dist: check-manifest; extra == "dev"
Provides-Extra: test
Requires-Dist: coverage; extra == "test"
Dynamic: author
Dynamic: license-file

# VKPyKit

This package contains several functions used for Exploratory Data Analysis, Linear Regression, Decision Trees. Instead of writing those everytime, just wanted to put them in a package and reuse myself. 

Never liked to write same code over and over, PyPI allows to reuse your code. 

~~~
## Using my own Pythn libraries for Exploratory Data Analysis and Decision Tree Modeling.
## 1. EDA - Exploratory Data Analysis
## 2. DT - Decision Tree
## Easier to write once and use as packaged functions. 

from VKPyKit.EDA import *
from VKPyKit.DT import *
from VKPyKit.LR import *

EDA= EDA()
DT = DT()
LR = LR()

# Example Model Performance Classification
DT.model_performance_classification(
            myDecisionTreeClassifierModel,
            myPredictors,
            myExpected,
            printall=True,
            title='My Decision Tree Model')

# Example Plot Confusion Matrix
DT.plot_confusion_matrix(
            myDecisionTreeClassifierModel,
            myPredictors,
            myExpected,
            title='My Decision Tree Model')

# Example Decision Tree Tuning
DT.tune_decision_tree(
            X_train=myX_train,
            y_train=myY_train,
            X_test=myX_test,
            y_test=myY_test,
            max_depth_v=(2, 11, 2),
            max_leaf_nodes_v=(10, 51, 10),
            min_samples_split_v=(10, 51, 10),
            printall=True,
            sortresultby=['F1Difference'],
            sortbyAscending=False)

# Example Histogram Boxplot All
EDA.histogram_boxplot_all(data=myData,
                              figsize=(15, 10),
                              bins=10,
                              kde=True)

LR.linear_regression_model(data=myData,
                              predictors=myPredictors,
                              target=myTarget,
                              printall=True,
                              title='My Linear Regression Model')

~~~
Use it as is, if you find issues or have more such functions, please contribute on github. 


[Source on Github](https://github.com/assignarc/VKPyKit)
