Q1. Case study on various methods of data cleaning >>>> data cleaning reduces errors and improves data quality. Correcng errors in data and eliminang bad records can be a me-consuming and tedious process, but it cannot be ignored. Data mining is a key technique for data cleaning. Data mining is a technique for discovering interesng informaon in data.Methods of Data Cleaning There are many data cleaning methods through which the data should be run. The methods are described below : Ignore the tuples: This method is not very feasible, as it only comes to use when the tuple has several aributes is has missing values. Fill the missing value: This approach is also not very effecve or feasible. Moreover, it can be a me- consuming method. In the approach, one has to fill in the missing value. This is usually done manually, but it can also be done by aribute mean or using the most probable value. Binning method: This approach is very simple to understand. The smoothing of sorted data is done using the values around it. The data is then divided into several segments of equal size. Aſter that, the different methods are executed to complete the task. Regression: The data is made smooth with the help of using the regression funcon. The regression can be linear or mulple. Linear regression has only one independent variable, and mulple regressions have more than one independent variable. Clustering: This method mainly operates on the group. Clustering groups the data in a cluster. Then, the outliers are detected with the help of clustering. Next, the similar values are then arranged into a "group" or a "cluster". Process of Data Cleaning