simplefit 0.1.4

Package that will clean the data, do basic EDA and provide an insight to basic models, LR and ridge

simplefit

A python package that cleans the data, does basic EDA and returns scores for basic classification and regression models

Overview

This package helps data scientists to clean the data, perform basic EDA, visualize graphical interpretations and analyse performance of the baseline model and basic Classification or Regression models, namely Logistic Regression, Ridge on their data.

Functions

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Function Name	Input	Output	Description
cleaner	dataframe	list of 3 dataframes	Loads and cleans the dataset, removes NA rows, strip extra white spaces, etc and returns clean dataframe
plot_distributions	dataframe, bins, dist_cols, class_label	Altair histogram plot object	creates numerical distribution plots on either all the numeric columns or the ones provided to it
plot_corr	dataframe, corr	Altair correlation plot object	creates correlation plot for all the columns in the dataframe
plot_splom	dataframe, pair_cols	Altair SPLOM plot object	creates SPLOM plot for all the numeric columns in the dataframe or the ones passed by the user
regressor	train_df, target_col, numeric_feats, categorical_feats, text_col, cv	dataframe	Preprocesses the data, fits baseline model(Dummy Regressor) and Ridge with default setup and returns model scores in the form of a dataframe
classifier	train_df , target_col , numeric_feats , categorical_feats , text_col , cv	dataframe	Preprocesses the data, fits baseline model(Dummy Classifier) and Logistic Regression with default setup and returns model scores in the form of a dataframe

Our Package in the Python Ecosystem

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There exists a subset of our package as standalone packages, namely auto-eda, eda-report, quick-eda, s11-classifier. But these packages only do the EDA or just the classification using XGBoostClassifier. But with our package, we aim to do all the basic steps of a ML pipeline and save the data scientist's time and effort by cleaning, preprocessing, returning grpahical visualisations from EDA and providing an insight about the basic model performances, after which the user can decide which other models to use.

Installation

$ pip install git+https://github.com/UBC-MDS/simplefit

Usage

Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

Contributors

This python package was developed by the following Master of Data Science program candidates at the University of the British Columbia:

• Mohammadreza Mirzazadeh @rezam747
• Zihan Zhou @zzhzoe
• Navya Dahiya @nd265
• Sanchit Singh @Sanchit120496

License

simplefit was created by Reza Zoe Navya Sanchit. It is licensed under the terms of the MIT license.

Credits

simplefit was created with cookiecutter and the py-pkgs-cookiecutter template.