Do you really need machine learning?
First of all, that depends on whether
- processes you employ are highly complex
- your needs cannot be fulfilled by other solutions (filters, automation, etc.)
- a high level of maintenance is required for your processes in your business
- time and resources are available to “teach” your future system
…you are a candidate to upgrade and optimise part of your processes with machine learning.
Step 1: Define your needs
Also, this is unarguably the basement of machine learning. Furthermore, you should carefully consider the goals you want to achieve, detailing every word or concept.
As a result, the way you split down your needs/goals will affect the way the data will have to be formatted and/or clustered.
So, make sure you have a large amount of data available at your disposal.
Step 2: Prepare your data
Finally, machine learning algorithms learn from data. As a result, this leads to it being fed with the right data for the problem you want to solve. So, even if you have good data, you need to make sure that it is in a useful scale. Also, making sure that format and any meaningful features are included.
- Formatting: you can convert your data to any database format or text format
- Cleaning: remove errors in your data sets, add missing content, etc.
- Sampling: if you have too much data, think about a representative sample to run tests first
- Scale: use the same scale for the data (it can be USD, EUR, Kg, Ltr, Units, etc.)
- Transform: Split or aggregate depending on your specific needs
- Improve model accuracy with data preprocessing.