Automated ML by d4rk-lucif3r
Project description
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
- Preprocessing Data:
- Encoding
- Splitting
- Scaling
- Dimensionality Reduction
- Resampling
- Trying many different machine learning models with hyperparameter tuning,
Installation
pip install lucifer-ml
Available Preprocessing Techniques
-
Skewness Correction
Takes 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 Preprocessing here
-
Available Modelling Techniques
-
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': 'Artificical Neural Network',
Example:
from luciferml.supervised import classification as cls dataset = pd.read_csv('Social_Network_Ads.csv') X = dataset.iloc[:, :-1] y = dataset.iloc[:, -1] cls.Classification(predictor = 'lr').predict(X, y)
More About Classification
-
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' : 'Artificical Neural Network',
Example:
from luciferml.supervised import regression as reg dataset = pd.read_excel('examples\Folds5x2_pp.xlsx') X = dataset.iloc[:, :-1] y = dataset.iloc[:, -1] reg.Regression(predictor = 'lin').predict(X, y)
More about Regression here
More To be Added Soon
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