v8

Author: seenstevo

README.md

@@ -1,20 +1,185 @@
-# MachineLearningToolKit
-Helper functions for all stages of the machine learning cycle.
+Welcome to ds11mltoolkit, we are delighted to see you here!
 
-# List of functions and methods
+Thank you for your interest, and we hope this library can help you in your daily life as a **Data Scientist**
 
-## Data collection, loading and pre-processing
+![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
 
-## Machine Learning Models
+![Logotipo](https://github.com/TheBridgeMachineLearningPythonLibrary/MachineLearningToolKit/blob/dev/assets/diagrama.png?raw=true)
 
+## Contributors
 
-## Model Productizing
+- [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. 
 
-#### Contributors
+[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!

dist/ds11mltoolkit-1.7.tar.gz

@@ -7,15 +7,15 @@
 setup(
   name = 'ds11mltoolkit',
   packages = ['ds11mltoolkit'],
-  version = '1.7',
+  version = '1.8',
   license = 'MIT',
   description = 'Helper functions for all stages of the machine learning model building process',
   long_description = long_description,
   long_description_content_type='text/markdown',
   author = 'TheBridgeMachineLearningPythonLibrary',
   author_email = 'seenstevol@protonmail.com',
   url = 'https://github.com/TheBridgeMachineLearningPythonLibrary/MachineLearningToolKit',
-  download_url = 'https://github.com/TheBridgeMachineLearningPythonLibrary/MachineLearningToolKit/archive/refs/tags/v_1_7.tar.gz',
+  download_url = 'https://github.com/TheBridgeMachineLearningPythonLibrary/MachineLearningToolKit/archive/refs/tags/v_1_8.tar.gz',
   keywords = ['machine learning', 'data visualization', 'data processing', 'sklearn', 'pandas'],
   install_requires=['pandas',
                     'scipy',