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

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

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.