Predictive models

Machine Learning

Expertise

N

Goal definition

N

Data collection

Data quality and quantity determine the model’s quality
N

Data preprocessing

Prepare and clean data
N

Modelling

Choose and implement a model based on:

  • Data type
  • Goal
N

Training

Train the model with your data to imporve its ability to predict
N

Evaluation

Test the model and evaluate the model performance
N

Prediction

We define the right goal with your domain experts and make predictions or decision about it, using machine learning algorithms which learn from your data.

After having defined your objective with domain experts, we help you predicting outputs based on input data.  The models we choose and implement take into account your data type and goal.

Possible application

Consumer Complains prediction

Using deep learning, we can predict consumer complains generated by a product based on multi-sectoral data (such as raw material, production line and quality control data).

The model prediction allows to reject products, not detected by quality control standard tests, that will generate high consumers complains.

Its analysis highlights the critical factors to monitor, to ensure better quality in production.

Anticipate future results

Support in decision making

Provide informing conclusions

Our partners

Technology

U

Research & Education

Contact us

+41 21 353 91 00

admin@procsim.ch

ProcSim, EPFL Innovation Park, Building D

CH – 1015 Lausanne

ProcSim