lucifer-ml 0.0.42

Automated ML by d4rk-lucif3r

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

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

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

More To be Added Soon