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).

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Automation specialist

**What it does**

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.

**How we built it**

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**:

*   Where is your data?
*   Select your features
*   Select your target
*   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.

1.  Connect one or multiple UiPath Orchestrators
2.  Create a Customer which stands for a Business Unit owning UiPath Robots
3.  Add an Automated Watched Process identifying the UiPath Processes, Robots & Queues involved
4.  Define an Alert Trigger to generate alerts by configuring rules.

UiPath Watcher is a web application designed to monitor UiPath Robots. It allows to create rules and trigger alerts on-top of UiPath Orchestrator and ElasticSearch APIs.