Metadata-Version: 2.1
Name: classic-CandidateElimination
Version: 2.0.0
Summary: Candidate-Elimination Algorithm is a Machine Learning Algorithm that builds the version space from Specific Hypothesis and General Hypothesis.
Home-page: https://github.com/safir72347/ML-CandidateElimination-PyPi
Author: Safir Motiwala
Author-email: safirmotiwala@gmail.com
License: UNKNOWN
Description: Candidate-Elimination Algorithm
        ===================
        
        
        Candidate-Elimination Algorithm is a Machine Learning Algorithm that builds the version space from Specific Hypothesis and General Hypothesis.
        
        ----------
        
        
        Installation
        -------------
        Install directly from my [PyPi](https://pypi.org/project/classic-CandidateElimination/)
        
        > pip install classic-CandidateElimination
        
        Or Clone the [Repository](https://github.com/safir72347/ML-CandidateElimination-PyPi) and install
        
        > python3 setup.py install
        
        Parameters
        -------------
        
        ## * X_train 
        -------------
        The Training Set array consisting of Features.
        
        ## * y_train
        -------------
        The Training Set array consisting of Outcome.
        
        
        Attributes
        -------------
        
        ## * fit(X_train, y_train)
        -------------
        Fit the Training Set to the model.
        
        ## * predict(y_test)
        -------------
        Predict the Test Set Results.
        
        
        
        <i class="icon-file"></i> Documentation
        -------------
        
        ### 1.  Install the package
        >  pip install classic_FindS
        
        ### 2. Import the library
        >  from classic_CandidateElimination import Candidate_Elimination
        
        ### 3. Create an object for FindS class
        > ce = Candidate_Elimination()
        
        ### 4. Fit your Training Set to the model
        > fs.fit(X_train, y_train)
        
        ### 5. Predict your Test Set results
        > y_pred = fs.predict(y_test)
        
        ----------
        
        
        
        Example Code
        -------------
        
        ### 1. Import the dataset and Preprocess
        > * import numpy as np
        > * import pandas as pd
        > * dataset = pd.read_csv('Covid-19_Data.csv')
        > * result = {'Yes':1, 'No':0}
        > * dataset['Covid_19'] = dataset['Covid_19'].map(result)
        > * X = dataset.iloc[:, 0:5].values
        > * y = dataset.iloc[:, -1].values
        
        > * from sklearn.model_selection import KFold
        > * kf = KFold(n_splits=10)
        > * for train_index, test_index in kf.split(X,y):
        >	 * X_train, X_test = X[train_index], X[test_index]
        >	 * y_train, y_test = y[train_index], y[test_index]
        
        ### 2. Use the Find-S Library
        > * from classic_CandidateElimination import Candidate_Elimination
        > * ce = Candidate_Elimination()            
        > * ce.fit(X_train, y_train)
        > * y_pred = ce.predict(X_test) 
        
        
        ----------
        
        
        
        Footnotes
        -------------
        
        You can find the code at my [Github](https://github.com/safir72347/ML-CandidateElimination-PyPi).
        
        
        
        Connect with me on Social Media
        -------------
        
        * [https://www.github.com/safir72347](www.github.com/safir72347)
        * [https://www.linkedin.com/in/safir72347/](https://www.linkedin.com/in/safir72347/)
        * [https://www.instagram.com/safir_12_10/](https://www.instagram.com/safir_12_10/)
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.0
Description-Content-Type: text/markdown
