Helper functions for all stages of the machine learning model building process
Project description
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
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
Contributors
- Miguel de Frutos
- Pedro Vergara
- Bogdan Radacina
- Sean Stevenson
- José Nevado
- Celia Cabello
- Jared Rivas
- Nicolás Eyzaguirre
- Enrique Moya
- Javi López
- Kyung Min Ohn
- Leandro Salvado
- Ramón Fernández
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
Please don't hesitate to contact us if you have any questions or comments. Thank you for using our library!
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