veda_lib

A Python library designed to streamline the transition from raw data to machine learning models.
veda_lib automates and simplifies data preprocessing, cleaning, and balancing, addressing the time-consuming and complex aspects of these tasks to provide clean, ready-to-use data for your models.

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Installation

First, install veda_lib using pip:

pip install veda_lib

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How to use?

After installing veda_lib, import it into your project and start utilizing its modules to prepare your data. Below is a summary of the key functionalities provided by each module:

1. Preprocessor Module

• Functions:
  • Removing null values
  • Handling duplicates
  • Imputing missing values with appropriate methods
• Usage: Ideal for initial data cleaning and preprocessing steps.

2. OutlierHandler Module

• Functions:
  • Handling outliers by either removing or capping them
  • Customizable based on the nature of your data
• Usage: Useful for managing data skewness and ensuring robust model performance.

3. FeatureSelector Module

• Functions:
  • Selecting important features from the dataset
  • Tailored selection based on the nature of the data
• Usage: Helps in reducing dimensionality and focusing on the most impactful features.

4. DimensionReducer Module

• Functions:
  • Reducing data dimensionality using appropriate techniques
• Usage: Crucial for addressing the curse of dimensionality and improving model efficiency.

5. BalanceData Module

• Functions:
  • Balancing class distribution in imbalanced datasets
  • Methods chosen based on data characteristics
• Usage: Essential for improving model fairness and performance on imbalanced datasets.

6. Veda Module

• Functions:
  • Integrates all the above functionalities into a single pipeline
• Usage: Pass your raw data through this module to perform comprehensive EDA and get fully preprocessed, cleaned, and balanced data ready for model training.

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Importing

• Here is an example of importing Veda from veda_lib.Veda, here set classification to True if the problem is classification otherwise set to False.

from veda_lib import Veda

eda = Veda.Veda(classification=True)
eda.fit_transform(X, Y)

• Here is an example of importing DataPreprocessor from veda_lib.Preprocessor, using default values of parameters

from veda_lib import Preprocessor

preprocessor = Preprocessor.DataPreprocessor()
X, y = preprocessor.fit_transform(X, Y)

• Here is an example of importing OutlierPreprocessor from veda_lib.OutlierHandler, using default values of parameters.

from veda_lib import OutlierHandler

outlier_preprocessor = OutlierHandler.OutlierPreprocessor()
X, y = outlier_preprocessor.fit_transform(X, Y)

• Here is an example of importing FeatureSelection from veda_lib.FeatureSelector, using default values of parameters.

from veda_lib import FeatureSelector

selector = FeatureSelector.FeatureSelection()
X, y = selector.fit_transform(X, y)

• Here is an example of importing DimensionReducer from veda_lib.DimensionReducer, using default values of parameters.

from veda_lib import DimensionReducer

reducer = DimensionReducer.DimensionReducer()
X, y = reducer.fit_transform(X, y)

• Here is an example of importing AdaptiveBalancer from veda_lib.BalanceData, using default values of parameters.

from veda_lib import BalanceData

balancer = BalanceData.AdaptiveBalancer(classification=True)
X, y, strategy, model = balancer.fit_transform(X, y)

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Contributing

I welcome contributions to veda_lib! If you have a bug report, feature suggestion, or want to contribute code, please open an issue or pull request on GitHub.

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License

veda_lib is licensed under the Apache License Version 2.0. See the LICENSE file for more details.