Metadata-Version: 2.1
Name: SmplML
Version: 1.0.2
Summary: SmplML is a user-friendly Python library for streamlined machine learning classification. It offers intuitive modules for data preprocessing, feature engineering, model training, and evaluation. Ideal for beginners and experts alike, SmplML simplifies classification tasks, enabling you to gain valuable insights from your data with ease.
Home-page: https://github.com/JhunBrian/SmplML
Author: Jhun Brian Andam
Author-email: brianandam123@gmail.com
License: MIT
Keywords: Machine Learning,Classification
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: Microsoft :: Windows :: Windows 10
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas
Requires-Dist: numpy
Requires-Dist: scikit-learn

# SmplML / SimpleML: Simplified Machine Learning for Classification

SmplML is a user-friendly Python library for streamlined machine learning classification. It offers intuitive modules for data preprocessing, model training, and evaluation. Ideal for beginners and experts alike, EasyML simplifies classification tasks, enabling you to gain valuable insights from your data with ease.

## Features

- Data preprocessing: Easily handle encoding categorical variables.
- Model training: Train various classification models with just a few lines of code.
- Model evaluation: Evaluate model performance using common metrics and visualizations.

## Installation

- You can install SmpML using pip:
```shell
pip install SimpleML
```

## Usage

```python
import seaborn as sns
import pandas as pd
from smpl_ml.classification_helpers import TrainClassifier
from sklearn.neighbors import KNeighborsClassifier

# Load the dataset
df = sns.load_dataset('penguins')

# Set the target and features
target = 'species'
features = df.iloc[:, df.columns != target].columns

# Create the classifier trainer
trainer = TrainClassifier(df.dropna(), target=target, features=features, model=KNeighborsClassifier())

# Fit the model
trainer.fit()

# Evaluate the model
model = trainer.evaluate()
```

`model` when `verbose` is set to `True` will return a DataFrame of classification metrics.


|    | Recall | Specificity | Precision | F1-Score | Accuracy |
|----|--------|-------------|-----------|----------|----------|
| Adelie | 0.91   | 0.70        | 0.67      | 0.77     | 0.76     |
| Chinstrap | 0.38   | 0.92        | 0.62      | 0.47     | 0.76     |
| Gentoo | 0.86   | 1.00        | 1.00      | 0.92     | 0.76     |

Change Log
==================

1.0.2 (06/12/2023)
------------------
Updated README.md
