classic-ID3-DecisionTree 2.0.3
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.
Requires Python: >=3.0

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
* Version 2.0.2 - All Sorted

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Installation
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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
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## * X_train 
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The Training Set array consisting of Features.

## * y_train
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The Training Set array consisting of Outcome.

## * dataset
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The Entire DataSet.


Attributes
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## * DecisionTreeClassifier()
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Initialise the instance of Decision Tree Classifier class.

## * add_features(dataset, result_col_name)
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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)
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To build the decision tree using Information Gain

## * gini_index(X_train, y_train)
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To build the decision tree using Gini Index

## * predict(y_test)
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Predict the Test Set Results


<i class="icon-file"></i> Documentation
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### 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 = id3.predict(X_test)

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Example Code
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### 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)


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Footnotes
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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
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* [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/)