# 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.
********************************************************
## Installation
First, install `veda_lib` using pip:
```bash
pip install veda_lib
```
**************************************
## 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.
*******************************************************
## 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.
```bash
from veda_lib import Veda
```
```bash
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
```bash
from veda_lib import Preprocessor
```
```bash
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.
```bash
from veda_lib import OutlierHandler
```
```bash
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.
```bash
from veda_lib import FeatureSelector
```
```bash
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.
```bash
from veda_lib import DimensionReducer
```
```bash
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.
```bash
from veda_lib import BalanceData
```
```bash
balancer = BalanceData.AdaptiveBalancer(classification=True)
X, y, strategy, model = balancer.fit_transform(X, y)
```
****************************************************************
## 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.
*************************************************************
## License
`veda_lib` is licensed under the Apache License Version 2.0. See the [LICENSE](https://github.com/vishallmaurya/VEDA?tab=Apache-2.0-1-ov-file) file for more details.
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