batabyal 0.2.0

Data cleaning and automated ML model selection package

batabyal

batabyal is a lightweight Python package that provides:

• cleaning_module for CSV data cleaning utilities
• trainer_kit for automatic best machine-learning model selection and training works on roc_auc score It is designed for rapid experimentation, prototyping, and small-to-medium ML workflows where you want sensible defaults without repetitive boilerplate.

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Installation

pip install batabyal

Importation

from batabyal import trainer_kit as tk
from batabyal import cleaning_module as cm

Usage

tk.train(x, y, "numeric", "multiclass", 3) 
#structure: train(x, y, x_type:XType, y_type:YType, n_splits:int, random_state:int|None=42)
#XType = Literal["numeric", "one_hot", "mixed"]
#YType = Literal["binary", "multiclass"]

cm.clean_csv('filename.csv', numericData, charData, True) 
#structure: clean_csv(file, numericData, charData, Fill, dummies=None)
#If `Fill==True`, it fills NaN in numeric columns with its mean. 
#`dummies` are the list of values to replace with NaN before cleaning.

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'trainer_kit' details

it uses:

• StratifiedKFold and GridSearchCV to find the best estimator
• roc_auc_ovr_weighted for scoring it is limited to:
• LogisticRegression,
• DecisionTreeClassifier,
• RandomForestClassifier,
• GaussianNB,
• BernoulliNB use it when:
• you have binary or multi-classed datasets with target labels (i.e. only for ClassifierMixin) don't use when:
• your dataset is single-classed it assumes:
• your dataset is perfectly cleaned
• one hot encoded (if applicable)
• data is scaled (if applicable)
• column order is same for train and test data it returns:
• the best trained model
• its best roc_auc score obtained from hyperparameter tunning
• the best fitted algorithm name which has been used to train

'cleaning_module' details

only for .csv file cleaning it returns the cleaned dataframe

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This package will help you to train supervised learning models quicker