Natixis is a French corporate and investment bank created in November 2006 from the merger of the asset management and investment banking operations of Natexis Banque Populaire (Banque Populaire group) and IXIS (Groupe Caisse d'Epargne).

It takes data from an Excel file and trains a Machine Learning model according to the type of problem to solve (regression or classification) and the algorithm used (linear regression, polynomial regression, logistic regression, etc.). Eventually, the trained models can be used to make predictions on new data.

We first created configuration files using JSON language to specify all kind of parameters:

one as a resource in order to list all algorithms and parameters handled by the tool

one for the dataset, in which you can specify the path of the Excel file, the sheet to use, which columns are corresponding to the features, and which one is corresponding to the target, the algorithm to use and its parameters

Then, we made the Python script to preprocess the data (dataset creation, label encoding) and train the model using pandas, numpy and scikit-learn libraries.

Next, we created another Python script to use a saved model in order to make predictions on new data.

Finally, we designed a user-friendly web application allowing users to follow all the steps from selecting the data to evaluate the accuracy of the trained model. There are 6 different steps:

Select your algorithm and specify its parameters

Review all your configuration and start the training

Evaluate your model by its score, some plots, a confusion matrix and generate a template to make predictions with new data

Eventually, we created two UiPath robots:

a first one to interact with users through Custom Input activity displaying the different pages of the web application

a second one based allowing users to make predictions with their trained models

Non-datascientists users can now train machine learning models by simply asking their robot to do it for them. Take data from an Excel file and train a supervised Machine Learning model.

MIT License Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the 'Software'), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Automation specialist

Visual Basic

Great Contribution - This sounds like an autoML solution. Are you challenging Google, Microsoft etc... :-). Great job. This can help for many use cases. Keep going guys.


Natixis - HandyML

Natixis - HandyML

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