Metadata-Version: 2.3
Name: shash
Version: 0.1.1
Summary: Custom data science utilities for model evaluation and data preparation
License: MIT
Author: Shashikanth
Author-email: shashikanth1110@gmail.com
Requires-Python: >=3.11
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Requires-Dist: hyperopt (==0.2.7)
Requires-Dist: joblib (==1.4.2)
Requires-Dist: jupyterlab (==4.2.3)
Requires-Dist: lightgbm (==4.4.0)
Requires-Dist: lime (==0.2.0.1)
Requires-Dist: pandas (==2.2.2)
Requires-Dist: scikit-learn (>=1.5.0,<2.0.0)
Requires-Dist: seaborn (>=0.13.2,<0.14.0)
Requires-Dist: wandb (==0.17.4)
Requires-Dist: xgboost (==2.1.0)
Description-Content-Type: text/markdown

# Shash Package

A custom Python package for data analysis, preparation, and model evaluation.

## Features

- **Data preparation & EDA**
  - `datacheck`: Checks for missing/null values and duplicate rows in a DataFrame.
  - `dataeda`: Provides basic exploration of a DataFrame, including `head`, `shape`, `info`, and descriptive statistics (`describe`).

- **Model evaluation**
  - `evaluate_model`: Computes key evaluation metrics for classification models, including accuracy, precision, recall, F1-score, confusion matrix, Classification report and ROC AUC score.


## Installation

Install from PyPI:

```bash
pip install shash


## Usage

```python
from shash.dataprep import datacheck, dataeda
from shash.evaluation import evaluate_model

# Example usage
datacheck(df)        # Check for missing values and duplicates
dataeda(df)          # Explore the dataset: head, shape, info, describe
evaluate_model(y_val, y_pred_val, y_proba_val, dataset_name="Validation Set")  # Evaluate model performance: accuracy, precision, recall, F1-score, confusion matrix

