Question 2What is the purpose of data preprocessing in data mining?1 pointTo develop a formal method for storing dataTo identify the right kind of data needed for data miningTo transform variables from one type to anotherTo ensure the integrity of data, deal with missing data, and remove irrelevant attributes

The purpose of data preprocessing in data mining is to ensure the integrity of data, deal with missing data, and remove irrelevant attributes. This process is crucial because it prepares and cleans the data for further analysis and processing.

Here are the steps involved in data preprocessing:

1. Data Cleaning: This step helps to handle the missing and noisy data. Missing data is handled by either deleting the entire row or column, or replacing it with a probable value. Noisy data (random error or variance in a measured variable) is handled by binning, regression, or clustering methods.

2. Data Integration: This process involves combining data from different sources into a coherent data store. Data integration techniques are used to manage the consistency of data.

3. Data Transformation: In this step, data is transformed or consolidated into forms appropriate for mining.

4. Data Reduction: This process aims to present a reduced representation of the data in a data warehouse without compromising the integrity and quality. It is done by dimensionality reduction, numerosity reduction, or data compression methods.

5. Data Discretization: This step involves the reduction of a number of values of a continuous attribute by dividing the range of attribute intervals.

By performing these steps, data preprocessing makes the data mining process more efficient and the results more accurate.