Skip to main content

ML package

Project description

ML Package

A Package through which multiple things can be done in a go like fitting a model of your own choice (regression/classification) and get final results in terms of model accuracy/ score/ mse/ mae/ confusion matrix and direct submission file which we need to upload in case of competitions.

Two things can be achieved from this package:

  1. Results after fitting any Regression/classification algorithm of your own choice. Function to be used - train_model
  2. Final submission file to be submitted directly in competitions. Function to be used - return_csv

Requirements:

  1. sklearn
  2. numpy
  3. pandas
  4. Pre-processed data to be passed - data already cleaned, splitted into train and test
  5. In train_model function, parameters to be passed:
    • x_train, y_train, x_test, y_test
    • model object
    • regression=True (if addressing regression problem)
    • regression=False (if addressing classification problem (default is True))
  6. In return_csv function, parameters to be passed:
    • x_train, y_train, x_test, y_test
    • model object
    • sample submission file (dataframe)
    • target column name (string)
    • file location name (filepath where to be saved)

Installation & Usage

  1. Make sure that your pip version is up-to-date: pip install --upgrade pip. Check version with pip -V.
  2. Select the correct package:
    • There are two packages (two versions of the package )and you should SELECT ONLY ONE OF THEM which is the latest one.
    • Install using pip install mlpkg with latest version
  3. Import the package and use its functions: from ML_Utility import ML_package

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mlpkg-0.3.tar.gz (2.8 kB view hashes)

Uploaded Source

Built Distribution

mlpkg-0.3-py3-none-any.whl (3.9 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page