ds11mltoolkit 1.9
Helper functions for all stages of the machine learning model building process
License: MIT
Keywords: machine learning,data visualization,data processing,sklearn,pandas

Welcome to ds11mltoolkit, we are delighted to see you here!

Thank you for your interest, and we hope this library can help you in your daily life as a **Data Scientist**

![Logotipo](./assets/logo.jpg)

[![Powered by NumFOCUS](https://img.shields.io/badge/powered%20by-TheBridge-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://www.thebridge.tech/) ![Powered by NumFOCUS](https://img.shields.io/badge/Contributors-13-orange.svg?style=flat&colorA=E1523D&colorB=007D8A) ![PyPI](https://img.shields.io/pypi/v/ds11mltoolkit.svg)

## Table of contents
- What is ds11mltoolkit?
- How to install ds11mltoolkit
- Dependencies
- Functions and methods
 - Data Analysis
 - Data visualization and exploration
 - Data processing
 - Machine Learning
- Github framework
- Contributors

## What is ds11mltoolKit?

It is a Python package that will help you in your first steps as a Data Scientist. *"Faster, cleaner, easier"* From simple databasis to complex neural networks, this library will accelerate your work processes in all stages of the machine learning cycle.

## How to install ds11mltoolkit?

Install as you would normally install a Pypi library.

```
pip install ds11mltoolkit
```

We suggest to import ds11mltoolkit as mlt, to make it easier to deploy by the users

```
import ds11mltoolkit as mlt
```

# Dependencies

ds11mltoolkit requires these libraries to work properly:

- beautifulsoup4==4.11.1
- imblearn==0.0
- keras==2.11.0
- matplotlib==0.1.6
- nltk==3.8.1
- opencv-python-headless==4.7.0.68
- pandas==1.3.5
- Pillow==9.3.0
- plotly==5.11.0
- requests==2.28.1
- scikit-image==1.0.2
- scikit-learn==0.19.3
- scipy==1.7.3
- seaborn==0.12.1
- selenium==4.7.2
- tensorflow==2.11.0
- wordcloud==1.7.0

## Functions and methods

In the current version, ds11mltoolkit will provide users around 40 functions, divided in 4 groups:

## Data Analysis

* read_url
* read_csv_zip
* chi_squared_test

## Data visualisation and exploration

* heatmap
* sunburst
* correl_map_max
* plot_map
* plot_ngram
* wordcloudviz
* plot_cumulative_variance_ratio
* plot_roc_cruve
* plot_multiclass_prediction_image

## Data processing

* list_categorical_columns
* last_columns
* uniq_value
* load_imgs
* class ImageDataGen(ImageDataGenerator) 3-in-1 functions
* clean_text
* processing_model_classification
* replace_convert_numeric
* log_transform_numeric
* add_previous
* _exponential_smooth
* Nan treatment
* convert_to_numeric
* auto_dtype_converter
* winner_loser
* lstm_model

## Machine Learning

* export_model
* import_model
* worst_params
* load_model_zip
* quickregression
* polynomial_features_non_binary
* balance_binary_target
* image_scrap
* create_multiclass_prediction_df
* show_scoring
* predict_model_classification
* Unsupervised KMeans
* UnsupervisedPCA


## Quick example


```

df = pd.DataFrame(data= {'Cities': ['Madrid', 'Barcelona'], 
                            'Teams': ['Team 1', 'Team 2'],
                            'Players': ['Vinicius', 'Pedri'],
                            'Goals': [10, 9]})


def list_categorical_columns(df):
    '''
    Function that returns a list with the names of the categorical columns of a dataframe.

    Parameters
    ----------
    df : dataframe
    
    Return
    ----------
        features: list of names

    '''
    features = []

    for c in df.columns:
        t = str(df[c].dtype)
        if "object"  in t:
            features.append(c)
    return features

list_categorical_columns(df)

output: ['Cities', 'Teams', 'Players']


```

## Github framework

![Logotipo](https://github.com/TheBridgeMachineLearningPythonLibrary/MachineLearningToolKit/blob/dev/assets/diagrama.png?raw=true)

## Contributors

- [Miguel de Frutos](https://github.com/Migueldfr)
- [Pedro Vergara](https://github.com/pericotronic)
- [Bogdan Radacina](https://github.com/BogdanBoyan92)
- [Sean Stevenson](https://github.com/seenstevo)
- [José Nevado](https://github.com/JNevado81)
- [Celia Cabello](https://github.com/celiacnavarro)
- [Jared Rivas](https://github.com/JaredR33)
- [Nicolás Eyzaguirre](https://github.com/NicolasEyzaguirre)
- [Enrique Moya](https://github.com/3Moya)
- [Javi López](https://github.com/javlopsan)
- [Kyung Min Ohn](https://github.com/exAdun)
- [Leandro Salvado](https://github.com/Lean788)
- [Ramón Fernández](https://github.com/RamonFCerezo)

# License

ds11mltoolkit uses an “Interface-Protection Clause” on top of the MIT license. This library is free for personal use. Therefore, it can be used for both commercial and non-commercial purpose. 

[See license](https://github.com/TheBridgeMachineLearningPythonLibrary/MachineLearningToolKit/blob/dev/LICENSE.txt)
---

Please don't hesitate to contact us if you have any questions or comments. Thank you for using our library!