• G. Arutjothi
• Dr. C. Senthamarai

Today’s real-world databases are highly susceptible to noisy, missing, and inconsistent data due to their typically huge size (often several gigabytes or more) and their likely origin from multiple, heterogeneous sources. Low-quality data will lead to low-quality mining results. There are a number of data preprocessing techniques. Data cleaning can be applied to remove noise and correct inconsistencies in the data. Data integration merges data from multiple sources into a coherent data store, such as a data warehouse. Data reduction can reduce the data size by aggregating, eliminating redundant features, or clustering, for instance. Data transformations, such as normalization, may be applied, where data are scaled to fall within a smaller range like 0.0 to 1.0. This can improve the accuracy and efficiency of mining algorithms involving distance measurements. These techniques are not mutually exclusive; they may work together. For example, data cleaning can involve transformations to correct wrong data, such as by transforming all entries for a date field to a common format.

CREDIT RISK PREDICTION ANALYSIS PREPROCESSING USING CREDIT RISK ASSESSMENT MODEL ALGORITHM

"CREDIT RISK PREDICTION ANALYSIS PREPROCESSING USING CREDIT RISK ASSESSMENT MODEL ALGORITHM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 10, page no.1018-1029, October-2019, Available :http://www.jetir.org/papers/JETIR1912139.pdf

"CREDIT RISK PREDICTION ANALYSIS PREPROCESSING USING CREDIT RISK ASSESSMENT MODEL ALGORITHM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 10, page no. pp1018-1029, October-2019, Available at : http://www.jetir.org/papers/JETIR1912139.pdf