Metadata-Version: 2.1
Name: pyevals
Version: 1.3.8
Summary: A simple Python Package for Model Evalutaion
Home-page: UNKNOWN
Author: Gokul and Anand
Author-email: adsp.tsgkr@gmail.com
License: UNKNOWN
Description: <!-- PROJECT SHIELDS -->
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        <!-- TABLE OF CONTENTS -->
        ## Table of Contents
        
        
        * [About the Project](#about-the-project)
        * [Installation](#installation)
        * [Usage](#usage)
        * [Future Work](#Futurework)
        * [Contact](#contact)
        
        
        <!-- ABOUT THE PROJECT -->
        ## About The Project
        
        
        A very elegant and simple library to evaluate models.
        
        This module builds the BarPlot, BoxPlot, CountPlot, DistPlot, HeatMap, PairPlot and ViolinPlot only with one line of code. A folder is created 'Plots' where the pdf files of all the plots are stored. Along with this, a pdf file will be generated 'FinalPlots.pdf' which contains all the plots with which EDA can be performed easily.
        
        This module will evaulate the Classification problems and Regression problems with 12 and 6 algorithms respectively. 
        
        The Classification algorithms are KNN,LogisticRegression,DecisionTreeClassifier, RandomForestClassifier, SupportVectorClassifier, QuadraticDiscriminantSnalysis, SGDClassifier, AdaBoost, CalibratedClassifier, MultinomialNB, BernoulliNB, GaussianNB.
        
        The Regression algorithms are LinearRegression, PolynomialRegression, RidgeRegression, LassoRegression, SupportVectorRegressor, GradientBoostingRegression.
        
        We also have implemented the Adjusted R Squared method as the Regression Metric Evaluation.
        
        In Classification , Highest Accuracy is Highlighted in Yellow colour.
        
        In Regression , Least Error is Highlighted in Yellow colour.
        
        
        ## Installation
        
        
        1. Clone the repo
        ```
        gh repo clone Anand-gokul/pyevals
        ```
        
        2. Install using pip or pip3
        ```commandline
        
        pip3 install pyevals
        
        (or)
        
        pip install pyevals
        
        ```
        
        <!-- USAGE EXAMPLES -->
        ## Usage
        
        
        ```python
        import pyevals
        
        # For Exploratory Data Analysis (or) For building the plots
        
        pyevals.BuildPlots(data,CategoricalFeatures,ContinuousFeatures)
        
        '''CategoricalFeatures and the ContinuousFeatures are the lists of the Categorical
        and Continuous Features of the dataset respectively. '''
        
        
        # For Classification
        
        Object = pyevals.build(x_train,x_test,y_train,y_test,'classification')
        Object.evaluate()
        
        # For Regression
        
        Object = pyevals.build(x_train,x_test,y_train,y_test,'regression')
        Object.evaluate()
        
          
        ```
        
        ## Future Work
        
        
        In this version we are only providing the reports and the plots as many as possible. We are working on improving the plots for better EDA.We will try to implement hyperparameter optimization techniques to get the better results. We will also try to implement other algorithms in classification and regression soon. 
        
        <!-- CONTACT -->
        ## Contact
        
        Githublink - https://github.com/Anand-gokul/pyevals
        
        Sai Gokul Krishna Reddy Talla - [@Krish](https://www.linkedin.com/in/gokul-talla) - gokulkrishna.talla@gmail.com
        
        Ananda Datta Sai Phanindra Tangirala - [@Anand](https://www.linkedin.com/in/ananda-datta-sai-phanindra-tangirala-62a4b5185) - tangiralaphanindra@gmail.com
        
        Anirudh Palaparthi - [@Anirudh](https://www.linkedin.com/in/pnbbanirudh/) - aniruddhapnbb@gmail.com
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
