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Web app and library for regression analysis of provided data files.

Project description

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Web app and library for regression analysis of provided data files.

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Install with pip:

$ pip install regression

For web app use git to clone GitHub repository:

$ git clone git://
$ cd regression
$ pip install -r requirements.txt

Check requirements.txt for dependencies.


from regression import processing as lib

files = lib.find_files()
sample = {'feature_1': 'value', 'feature_2': 'value', 'feature_n': 'value'}

X, y, sample = lib.process_data(files['data.csv'], sample)  # Ingest and format data
lib.predict_ols(X, y, sample)  # Run OLS model
lib.predict_gbr(X, y, sample)  # Run Gradient Boosting for regression model

Run web app on Flask’s built-in server or deploy to a WSGI server:

$ python


Provide your data files either with upload form or by copying them into the app root directory or subdirectory in one of the supported file formats:

  • Delimited text files (CSV)
index,features,...,outcome  # First row is a header containing a list of field names
item 1,X11,X12,...,X1p,y1   # Value rows should follow this order
item 2,X21,X22,...,X2p,y2   # Features can be given as numerical or categorical values
item n,Xn1,Xn2,...,Xnp,yn
  • Excel files (XLS, XLSX)
index features outcome
item 1 X11 X12 X1p y1
item 2 X21 X22 X2p y2
item n Xn1 Xn2 Xnp yn


Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

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