Welcome to Sientia Predictive Power Evaluator 👋

The fastest way to assess the predictive potential of a data problem in the industry

Pandora Box enables analysts and data scientists to discover the predictive potential of an industrial process in minutes.

Pandora does quick feasibility analysis of the problems you want to predictively model in the industry (quality prediction, virtual sensors, etc).

Sientia Predictive Power Evaluator (SPPE) can be used to analyze correlations between process variables and quality (linear and non-linear correlation), do hypothesis testing (check causality), calculate lag time, synchronize variables in time, and provide information on the predictive potential of the problem.

Why Sientia Predictive Power Analysis?

You know those times when you need to do a quick feasibility analysis (predictive potential) of a problem and you don't have time to do all that python coding or you don't have anyone available to help you?

If you've ever been in this situation, then SPPE is the perfect app to save you in those moments!

How Sientia Predictive Power works

Analyzing whether a quality variable of an industrial process can be predictively modeled is a complex task that requires evaluating a lot of information. SPPE makes this process easier, making all the necessary steps for it more automatic and faster.

Of course, we don't reinvent the wheel. However, we have made this process more democratic and accessible to analysts, engineers and decision makers in the industry, allowing a minimal analysis to be done on the data you want to explore/model.

Such a preliminary analysis, using SPPE, allows the user to have a minimum of necessary information regarding the predictive potential of the problem, facilitating the decision-making on whether or not to proceed with the data project.

SPPE also provides the user with a synchronized dataset ready to be explored more consistently and systematically.

Installation

1. Clone the project and navigate to the project folder

git clone https://github.com/Aignosi/sientia-predictivepower-evaluation.git
cd <project folder name>

2. Run your virtual environment (in this example we will use virtualenv)

python -m venv myenv

Linux

source myenv/bin/activate

Windows

. myenv/Scripts/activate

3. Install dependencies

pip install -r requirements.txt

4. Run the application in the command terminal

streamlit run sppe_app.py

Using SPPE

• A practical example

Access credentials

• User Name: default
• Password: 12345

Loading data into SPPE

Visualizing the data and its basic characteristics

Visualizing and Interpreting Correlations

Join our community of analysts who use SPPE

• Join us at Discord

Reporting bugs and contributing code

Open an issue

Support or contact

Send an e-mail to alexandre@aignosi.com

SPPE for enterprises or cloud provisioning

Want to share your findings, export datasets, and assess the predictive potential of a large-scale problem?

enter our lista de espera do Pandorabox Cloud

License

Our app is open source under the terms of use of the GPL 3.0 license