Skip to main content

Automatic Machine Learning with many powerful tools.

Project description

GML - Ghalat Machine Learning!

Tired of training multiple models and then picking the best among them? No worries now! GML is here for you!

GML is an automatic machine learning library in python built on top of Scikit-Learn,Keras,XGBoost,LightGBM and Catboost. with this library, you can train your data on multiple machine learning algorithms and a neural network! not only training but scaling the data for normal distribution and after scaling and training, testing the data on validation data (don't worry you don't need to provide validation data. we will extract it from your data). after testing models on validation data, they will be ranked accordingly and you will see which one performs better than other. the first ranked model will be returned (untrained, so you can train it yourself and check results). You already got some models? no problem! pass them to us to make them compete with our models and let see who wins ;-)

In future updates many other things will also be automated like hyper parameter tunning, multiple neural networks, other machine learning algorithms and many more cool things!

See GML in Action!!



Function description:


These parameters are common in both GMLRegressor and GMLClassifier
* X 
  Data column excluding the target column. it can either be a pandas dataframe or a numpy array. but please make sure your data doesn't contains missing data or non-numeric data. (clean it before passing)
* y 
  The targeted column

Below parameters are optional.

* metric
  metric on which you want to test your model. by default, it is mean-squared-error for regression and accuracy score for classification
* test_Size 
  size to split your test data, by default = 0.3 (70% training 30% testing)
* folds (only in GMLClassifier)
  Data will also be validated using KFolds. pass number of folds. by default folds = 5
* shuffle
  Shuffle the data when spliting for validation. by default = True
* scaler
  for Scaler pass: 
    'SS' for StandardScalar
    'MM' for MinMaxScalar
    'log' for Log scalar
     None for not scaling
  by default: StandardScalar
* models
  You got your own models to make them compete with our models? pass them in a list here. default = None
* neural_net
  Want to train on Neural Networks? Pass 'Yes', default = 'No'
* epochs
  for neural networks, by default = 10 
* verbose
  for neural networks, by default = True

Parameter when creating object of GML

models = Ghalat_Machine_Learning(n_estimators=300)
  • by default n_estimators are 300, you can change it to whatever you want.

As its first version of GML, feel free to give suggestions,ask questions,report bugs etc in issues portion of this repository!
you can directly contact me at: <font color="blue"> m.ahmed.memonn@gmail.com</font>

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

GML-0.0.4.tar.gz (5.8 kB view hashes)

Uploaded source

Built Distribution

GML-0.0.4-py3-none-any.whl (8.7 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page