Metadata-Version: 2.1
Name: classic-ID3-DecisionTree
Version: 2.0.1
Summary: ID3 is a Machine Learning Decision Tree Classification Algorithm that uses two methods to build the model. The two methods are Information Gain and Gini Index.
Home-page: https://github.com/safir72347/ML-ID3-Decision-Tree-Classification-Library-PyPi
Author: Safir Motiwala
Author-email: safirmotiwala@gmail.com
License: UNKNOWN
Description: ID3 Decision Tree Algorithm 
        ===================
        
        
        ID3 is a Machine Learning Decision Tree Classification Algorithm that uses two methods to build the model. The two methods are Information Gain and Gini Index.
        * Version 1.0.0 - Information Gain Only
        * Version 2.0.0 - Gini Index added
        * Version 2.0.1 - Documentation Sorted
        
        ----------
        
        
        Installation
        -------------
        Install directly from my [PyPi](https://pypi.org/project/classic-ID3-DecisionTree/)
        
        > pip install classic-ID3-DecisionTree
        
        Or Clone the [Repository](https://github.com/safir72347/ML-ID3-Decision-Tree-Classification-Library-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.
        
        ## * dataset
        -------------
        The Entire DataSet.
        
        
        Attributes
        -------------
        
        ## * DecisionTreeClassifier()
        -------------
        Initialise the instance of Decision Tree Classifier class.
        
        ## * add_features(dataset, result_col_name)
        -------------
        Add the features to the model by sending the dataset. The model will fetch the column features. The second parameter is the column name of outcome array.
        
        ## * information_gain(X_train, y_train)
        -------------
        To build the decision tree using Information Gain
        
        ## * gini_index(X_train, y_train)
        -------------
        To build the decision tree using Gini Index
        
        ## * predict(y_test)
        -------------
        Predict the Test Set Results
        
        
        <i class="icon-file"></i> Documentation
        -------------
        
        ### 1.  Install the package
        >  pip install classic-ID3-DecisionTree
        
        ### 2. Import the library
        >  from classic_ID3_decision_tree import DecisionTreeClassifier
        
        ### 3. Create an object for Decision Tree Classifier class
        > id3 = DecisionTreeClassifier()
        
        ### 4. Add Column Features to the model
        > id3.add_features(dataset, result_col_name)
        
        ### 5. Build the Decision Tree Model using Information Gain
        > id3.information_gain(X_train, y_train)
        
        ### OR
        
        ### 5. Build the Decision Tree Model using Gini Index
        > id3.gini_index(X_train, y_train)
        
        ### 6. Predict the Test Set Results
        > y_pred = ig.predict(X_test)
        
        ----------
        
        
        
        Example Code
        -------------
        
        ### 0. Download the dataset
        Download dataset from [here](https://drive.google.com/file/d/1qjh3SnbrOY3ROXFYYMbJqQ7SvTbI6iqe/view?usp=sharing)
        
        ### 1. Import the dataset and Preprocess
        > * import numpy as np
        > * import matplotlib.pyplot as plt
        > * import pandas as pd
        
        > * dataset = pd.read_csv('house-votes-84.csv')
        > * rawdataset = pd.read_csv('house-votes-84.csv')
        > * party = {'republican':0, 'democrat':1}
        > * vote = {'y':1, 'n':0, '?':0}
        
        > * for col in dataset.columns:
        >     * if col != 'party':
        >         * dataset[col] = dataset[col].map(vote)
        > * dataset['party'] = dataset['party'].map(party)
        
        > * X = dataset.iloc[:, 1:17].values
        > * y = dataset.iloc[:, 0].values
        
        > * from sklearn.model_selection import KFold
        > * kf = KFold(n_splits=5)
        
        > * 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 ID3 Library
        > * from classic_ID3_decision_tree import DecisionTreeClassifier
        > * id3 = DecisionTreeClassifier()
        > * id3.add_features(dataset, 'party')
        > * print(id3.features)
        
        > * id3.information_gain(X_train, y_train)
        > * OR
        > * id3.gini_index(X_train, y_train)
        > * y_pred = ig.predict(X_test)
        
        
        ----------
        
        
        
        Footnotes
        -------------
        
        You can find the code at my [Github](https://github.com/safir72347/ML-ID3-Decision-Tree-Classification-Library-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
