Metadata-Version: 2.1
Name: lucifer-ml
Version: 0.0.44
Summary: Automated ML by d4rk-lucif3r
Home-page: https://github.com/d4rk-lucif3r/LuciferML
Author: Arsh Anwar
Author-email: lucifer78908@gmail.com
License: MIT
Description: <p align="center" ><img align='center' alt="https://github.com/d4rk-lucif3r/LuciferML/blob/master/assets/img/LUCIFER-ML.gif"  src="https://github.com/d4rk-lucif3r/LuciferML/blob/master/assets/img/LUCIFER-ML.gif"></p>
        
        # LuciferML a Semi-Automated Machine Learning Library by d4rk-lucif3r
        
        ## About
        
        The LuciferML is a Semi-Automated Machine Learning Python Library that works with tabular data. It is designed to save time while doing data analysis. It will help you right from data preprocessing to Data Prediction.
        
        ### The LuciferML will help you with
        
        1. Preprocessing Data:
            - Encoding
            - Splitting
            - Scaling
            - Dimensionality Reduction
            - Resampling
        2. Trying many different machine learning models with hyperparameter tuning,
        
        ## Installation
        
            pip install lucifer-ml
        
        ## Available Preprocessing Techniques
        
        1) Skewness Correction
        
            Takes Pandas Dataframe as input. Transforms each column in dataset except the columns given as an optional parameter.
            Returns Transformed Data.
        
            Example:
        
             1) All Columns
        
                 from luciferml.preprocessing import Preprocess as prep
        
                 import pandas as pd
        
                 dataset = pd.read_csv('/examples/Social_Network_Ads.csv')
        
                 dataset = prep.skewcorrect(dataset)
        
             2) Except column/columns
        
                 from luciferml.preprocessing import Preprocess as prep
        
                 import pandas as pd
        
                 dataset = pd.read_csv('/examples/Social_Network_Ads.csv')
        
                 dataset = prep.skewcorrect(dataset,except_columns=['Purchased'])
        
            More about Preprocessing [here](https://github.com/d4rk-lucif3r/LuciferML/blob/master/luciferml/supervised/README/Preprocessing.md)
        
        ## Available Modelling Techniques
        
        1) Classification
        
            Available Models for Classification
        
                - 'lr' : 'Logistic Regression',
                - 'svm': 'Support Vector Machine',
                - 'knn': 'K-Nearest Neighbours',
                - 'dt' : 'Decision Trees',
                - 'nb' : 'Naive Bayes',
                - 'rfc': 'Random Forest Classifier',
                - 'xgb': 'XGBoost Classifier',
                - 'ann' : 'Artificial Neural Network',
        
            Example:
        
                from luciferml.supervised.classification import Classification
                dataset = pd.read_csv('Social_Network_Ads.csv')
                X = dataset.iloc[:, :-1]
                y = dataset.iloc[:, -1]
                classifier = Classification(predictor = 'lr')
                classifier.fit(X, y)
                result = classifier.result()
        
            More About [Classification](https://github.com/d4rk-lucif3r/LuciferML/blob/master/luciferml/supervised/README/Classification.md)
        
        2) Regression
        
               Available Models for Regression
        
                - 'lin' : 'Linear Regression',
                - 'sgd' : 'Stochastic Gradient Descent Regressor',
                - 'elas': 'Elastic Net Regressot',
                - 'krr' : 'Kernel Ridge Regressor',
                - 'br'  : 'Bayesian Ridge Regressor',
                - 'svr' : 'Support Vector Regressor',
                - 'knr' : 'K-Nearest Regressor',
                - 'dt'  : 'Decision Trees',
                - 'rfr' : 'Random Forest Regressor',
                - 'gbr' : 'Gradient Boost Regressor',
                - 'lgbm': 'LightGB Regressor',
                - 'xgb' : 'XGBoost Regressor',
                - 'cat' : 'Catboost Regressor',
                - 'ann' : 'Artificial Neural Network',
        
            Example:
        
                from luciferml.supervised.regression import Regression
                dataset = pd.read_excel('examples\Folds5x2_pp.xlsx')
                X = dataset.iloc[:, :-1]
                y = dataset.iloc[:, -1]
                regressor = Regression(predictor = 'lin')
                regressor.fit(X, y)
                result = regressor.result()
        
            More about Regression [here](https://github.com/d4rk-lucif3r/LuciferML/blob/master/luciferml/supervised/README/Regression.md)
        
        ## More To be Added Soon
        
Keywords: luciferML,AutoML,Python
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
