# Financial Data Quality Evaluation Method Based on Multiple Linear Regression
## 3 Construction of Quality Evaluation Model Based on Multiple Liner Regression  
In this study, we present a machine learning-based data quality evaluation method. The method revolves around the creation of a data quality evaluation indicator system. The key steps of this approach are as follows:  
1. Select relevant customer information data from the customer information database, and preprocess the data based on the proposed label generation algorithm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Indicator Number** & **Indicator Name** & **Scoring Formula and Instructions** \\ \hline C13 & value integrity & \(X_{\text{C13}}=100\times(1-N_{\text{C13}}/W_{\text{C13}})\) \\  & value integrity & \(N_{\text{C13}}\): missing required fields in the data table \\  & & \(W_{\text{C13}}\): the total number of data items that should be required in the data table \\ \hline \(B2\) & accuracy & \(X_{B2}=\lambda_{\text{C21}}X_{\text{C21}}+\lambda_{\text{C22}}X_{\text{C22}}+ \lambda_{\text{C23}}X_{\text{C23}}\) \\ \hline C21 & record accuracy & \(X_{\text{C21}}=100\times(1-N_{\text{C21}}/W_{\text{C21}})\) \\  & record accuracy & \(N_{\text{C21}}\) : the number of inaccurate data items recorded in the data table \\  & & \(W_{\text{C21}}\) : the total number of assigned data items in the data table \\ \hline C22 & value accuracy & \(X_{\text{C22}}=100\times(1-N_{\text{C22}}/W_{\text{C22}})\) \\  & & \(W_{\text{C22}}\): the total number of data items that should be recorded in the data table \\ \hline C23 & conceptual accuracy & \(N_{\text{C23}}\) : the number of conceptually inaccurate data items in the data table \\  & & \(W_{\text{C23}}\): the total number of data items that should be recorded in the data table \\ \hline \(B3\) & timeliness & \(X_{\text{C31}}=100\times(1-N_{\text{C31}}/W_{\text{C31}})\) \\ \hline C31 & data update timeliness & \(N_{\text{C31}}\): the number of data items that are not updated in time in the data table \\  & & \(W_{\text{C31}}\): the total number of data items that should be recorded in the data table \\ \hline \(B4\) & consistency & \(X_{\text{S4}}=X_{\text{C41}}\) \\ \hline C41 & logical consistency & \(N_{\text{C41}}=100\times(1-N_{\text{C41}}/W_{\text{C41}})\) \\  & & \(N_{\text{C41}}\) : the number of data items in the data table that do not satisfy the logic \\  & & \(W_{\text{C41}}\) : the total number of data items that should be recorded in the data table \\ \hline \(B5\) & uniqueness & \(X_{\text{S5}}=X_{\text{C51}}\) \\ \hline C51 & record uniqueness & \(N_{\text{C51}}\): the number of data items repeated in the data table \\  & & \(W_{\text{C51}}\): the total number of assigned data items in the data table \\ \hline B6 & compliance & \(X_{\text{S6}}=X_{\text{C61}}\) \\ \hline C61 & data format compliance & \(X_{\text{C61}}=100\times(1-N_{\text{C61}}/W_{\text{C61}})\) \\  & & \(N_{\text{C61}}\): number of data items with non-compliant format in the data table \\  & & \(W_{\text{C61}}\): the total number of assigned data items in the data table \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._Extract quality evaluation indicators and associated data, constructing a quality evaluation indicator system suitable for machine learning models.
* Train and test the machine learning model to complete its construction.
* Evaluate the results using performance indicators.