Machine Learning/Traditional Method of Predicting

ml

We use our expertise to identify the best and most suitable

machine learning algorithms in finding those hidden patterns in your raw data.

What is Machine Learning?

It is used as a general term for computational data analysis: using data to makes inferences and predictions.
Interpreted broadly, it includes computational statistics, data analytics, data mining and a good portion of
data science. It is also the process of discovering patterns in large data sets. There are 6 essential steps to
building and delivering a machine learning model.

The Process

Step 1: Business Understanding: it is required to understand business objectives clearly and find out what
are the business’s needs. This includes assessing the current situation by finding the resources, assumptions,
constraints and other  important factors which should be considered.
Step 2: Data Understanding: get familiar with the data. Load, explore, visualise and report.
Step 3: Data Preparation: clean data for modelling purposes.
Step 4 Modelling: Use appropriate modelling techniques for your data. It should also include train/test 
scenario.
Step 5 Evaluation: the model results must be evaluated in the context of business objectives.
Step 6 Deployment: The knowledge or information, which is gained through machine learning process, needs to be
presented in such a way that stakeholders can use it when they want it.