Mentat-IT offers consulting and hands-on services in data science & machine learning
Data science & machine learning problems can often be broken down into a distinct number of tasks using the CRISP-DM Methodology.
1. Business Understanding
Translate your business problem into a data problem
- Understand project objectives and requirements from a business perspective;
- Converting this knowledge into a data problem definition;
- Preliminary plan designed to achieve the objectives.
2. Data Understanding
Provide insight into your data
- Data collection
- Identify data quality problems
- Discover first insights into data
3. Data Preparation
Prepare your data for machine learning
- Transformation of data into usable formats
- Feature selection – which properties are relevant to the problem?
- Cleaning of data
- Recursive process with modeling phase
Apply various modeling techniques, depending on the problem e.g. supervised learning, unsupervised learning, deep learning.
- Selection of a model suitable to the problem
- Parameter selection – tuning the algorithm
- Recursive process with data preparation phase
Test and evaluate the model to see if the business objectives have been achieved.
- Testing model performance on test data
- Evaluate whether outcomes fits the business objectives
- Recursive process with data preparation & modeling phase
Bring insights and results from the modeling phase into a production stage.
- Reporting and visualisation of results
- Implement the model into production ready code