Package: lucifer-ml
LuciferML a Semi-Automated Machine Learning Library by d4rk-lucif3rAboutThe 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 withPreprocessing Data:EncodingSplittingScalingDimensionality ReductionResamplingTrying many different machine learning models with hyperparameter tuning,Installationpip install lucifer-mlAvailable Preprocessing TechniquesSkewness CorrectionTakes Pandas Dataframe as input. Transforms each column in dataset except the columns given as an optional parameter.
Returns Transformed Data.Example: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)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 PreprocessinghereAvailable Modelling TechniquesClassificationAvailable Models for Classification- 'lr'  : 'Logistic Regression',
 - 'sgd' : 'Stochastic Gradient Descent',
 - 'perc': 'Perceptron',
 - 'pass': 'Passive Aggressive Classifier',
 - 'ridg': 'Ridge Classifier', 
 - 'svm' : 'Support Vector Machine',
 - 'knn' : 'K-Nearest Neighbours',
 - 'dt'  : 'Decision Trees',
 - 'nb'  : 'Naive Bayes',
 - 'rfc' : 'Random Forest Classifier',
 - 'gbc' : 'Gradient Boosting Classifier',
 - 'ada' : 'AdaBoost Classifier',
 - 'bag' : 'Bagging Classifier',
 - 'extc': 'Extra Trees Classifier',
 - 'lgbm': 'LightGBM Classifier',
 - 'cat' : 'CatBoost Classifier',
 - 'xgb' : 'XGBoost Classifier',
 - 'ann' : 'Multilayer Perceptron Classifier',
 - 'all' : 'Applies all above classifiers'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 AboutClassificationRegressionAvailable 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',
 - 'ada' : 'AdaBoost Regressor',
 - 'bag' : 'Bagging Regressor',
 - 'extr': 'Extra Trees Regressor',
 - 'lgbm': 'LightGBM Regressor',
 - 'xgb' : 'XGBoost Regressor',
 - 'cat' : 'Catboost Regressor',
 - 'ann' : 'Multilayer Perceptron Regressor',
 - 'all' : 'Applies all above regressors'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 RegressionhereHyperparameter TuningLuciferML is powered byOptunafor Hyperparam tuning. Just add "tune = True" in either Regressor or Classifier it will start tuning the model/s with Optuna.PersistenceLuciferML's model can be saved as a pickle file. It will save both the model and the scaler to the pickle file.- Saving

    Ex: 
        regressor.save([<path-to-model.pkl>, <path-to-scaler.pkl>])A new LuciferML Object can be loaded as well by specifying path of model and scaler- Loading

    Ex: 
        regressor = Regression(path = [<path-to-model.pkl>, <path-to-scaler.pkl>])These are applicable for both Classification and Regression.ExamplesPlease refer to more exampleshereTo-Do's