It involves data cleaning, data transformation, and data reduction. Every textual data may not be ready
Data preprocessing is a data mining technique that involves transforming raw data into an understandable and useful format. Real-world data is often incomplete, inconsistent, and/or lacking in certain behaviors or trends, and is likely to contain many errors. Data preprocessing is a proven method of resolving such issues. It involves data cleaning, data transformation, and data reduction.

List of Pre-Processing Algorithms

• Case Convertor
• Custom Words Remover
• Frequent Words Remover
• Lemmatizer
• Punctuation Remover
• Spelling Corrector
• Stemmer
• Word Correlation 
• Word Embedding
• Word Frequency

Notes:

• The Reader (Dataset) should be connected to the algorithm.
• These algorithms can be used only on textual data.
• You can use one algorithm after the other for preparing your data.

These algorithms are used for text mining and preparing your data for a targeted approach.