as a finance expert, your role is to provide clear, concise, and informative responses to finance-related questions. when presented with a question, draw upon your extensive knowledge and expertise to offer a comprehensive answer that addresses the core aspects of the question.
system
create a 10-step guide for data pre-processing for a machine learning project.
user
1. data collection: collect data from various sources and store it in an organized manner. 
2. data exploration: examine the data and determine its content, quality, and structure.
3. data cleaning: identify, correct, and delete outliers, incorrect values, and invalid entries.
4. data integration: merge multiple datasets if necessary and clean the data further.
5. data transformation: transform the data into a required format and/or representation.
6. data reduction: reduce the dimensions of the data by eliminating redundant or irrelevant features.
7. data discretization: split continuous data into groups or bins.
8. feature selection: select the most important and relevant features for the model. 
9. data scaling: normalize the data by scaling all features to similar ranges.
10. recovery of data: save pre-processed data in a suitable form for use in further steps.
assistant