Data preprocessing transforms raw data into a standardized format for analysis, enhancing the accuracy and efficiency of machine learning projects. Key components include managing missing data, encoding, and scaling features, along with validation processes to ensure data completeness and accuracy. Companies typically employ data analysts to handle preprocessing before engaging machine learning development services.

1. PRE-PROCESSING
All About Data

2. What is Data
Preprocessing
The pre-processing stage converts raw data
from its natural state to a standard format
suitable for analysis.
It is an important part of machine learning
development services, as data pre-processing
enables increased accuracy and efficiency in the
final product.

3. Types of Data
• Numerical Data
• Categorical Data
• Text Data
Time Series Data

4. Import Datasets
Import Libraries
Manage Missing Data
Get the Dataset
Steps of Data Pre-processing
Encoding Data
Dataset into Test Set
Scaling the Features

5. Characteristics
of Data Preparation
Data validation is the process by which
businesses examine and judge whether the
raw data for a project is complete and
accurate in order to achieve the best results.
1
Data imputation is the process of manually
inputting missing numbers and correcting
data errors discovered during the validation
process or through coding, such as
business process automation.
2

6. Pre-Processing is a Must in Machine
Learning Development Services
Machine learning development services must include data. Companies generally
hire data analysts to pre-process the data before going to a machine learning
development company to create the final product. Get in touch with MoogleLabs
today, and start your journey of utilizing the latest technology to improve your
operations today.

7. www.mooglelabs.com