Prepare the Data for Machine Learning Algorithms

The most important part of a machine learning project is preparing the data. Data preparation is the process of cleaning and transforming raw data before processing and analysis. It is an important step before processing and often involves reformatting data, making corrections to data, and combining data sets to enrich data.

Data preparation is often a lengthy process for data professionals. Data professionals spend most of the time preparing the data by cleaning and transforming raw data before processing and analysis. It is essential as a prerequisite to put data in context to turn it into insights and eliminate bias resulting from poor data quality.

Benefits of Data Preparation

1. Fix errors quickly
2. Produce top-quality data
3. Make better business decisions

Knowledge Discovery in Databases

Data Preparation Steps

1. Gathering data
2. Discover and Assess data
3. Cleanse and validate data
4. Handling Categorical Data
5. Normalizing Data
6. Feature Construction

Understanding the Data Preparation Steps in detail

1. Gathering data