Machine Learning & Predictive Analytics

A lot of companies have data and don't know where to get started. Other's don't have data and want to collect. With my history as a Data Scientist, I can help you leverage your data to generate machine learning algorithms that improve your business.

As a former data scientist and researcher, I am familiar with machine learning models - and their pitfalls. Thanks to my experience, I know how to build good, robust models and the various options available for feature engineering and hyperparameter tuning. However, because of my experience as a founder, I always consider the business side when assessing the models to get the best insights for your company.

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Approach and Process

Problem Definition & Data Preparation

Define the challenges

We discuss your business goals and explore whether and how machine learning can help you overcome your challenges.

Data identification & preprocessing

The next step is to identify whether the required data is present in your company and how to preprocess it to make it usable for analysis and machine learning - only then can models deliver meaningful results.

Data Exploration & Feature Engineering

Data exploration

The data is now explored in order to gain an initial impression of the existing database. With simple statistical evaluations, the data already provides information about the possibilities for feature engineering.

Feature engineering

When necessary, additional data fields are now created or existing data is enriched. This is an important step in improving the performance of a machine learning model.

Model Development & Deployment

Building Models

Choose an appropriate machine learning model based on the nature of the problem (classification, regression, etc.) and the characteristics of the data. Consider factors such as interpretability, complexity, and scalability. In most cases, there is no one perfect model. That's why I build several models and evaluate the results. Ofte, these models can be further improved through hyperparameter tuning.

Evaluation

The model is then evaluated and analyzed to answer the initial question. The insights must then be evaluated and discussed.

Model deployment

The model can now be deployed in a production environment and thus continuously provide insights on real-world data. To do this, we set up a suitable environment and discuss the integration into your business.

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Contact me now to
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