Pentaho+ Data Processing Methodology
There is a critical need for data processing while using data for analytical purposes. Agility and competitiveness are maintained with the help of an effective data processing strategy.
Data Cleansing for Machine Learning
Databases may generally contain incorrect, incomplete, duplicate or improperly-formatted data. It is essential to employ data cleaning to remove these inconsistencies and prepare data for further analysis.
Extracting Meaningful Insights from Data using Exploratory Data Analysis
Raw data is normally ambiguous and difficult to interpret. Cleaning it is essential in order to understand the relationships between the variables present in the data.
Improving Data Quality using Data Cleansing and Normalization – Part Two
In this blog, I will be explaining about how to further clean the technical data into a ‘Consistent Data’ and the methodologies adopted.
