# Predicting Financial Performance in the IT Industry with Machine Learning: ROA and ROE Analysis
## 4 Results and Discussion  
In this study, a series of data preprocessing steps were performed on the data set. The missing data rate was calculated for each variable. If the missing data rate is above 5%, this may affect the data analysis and modeling processes. Missing values can be filled with mean, median or mode. For continuous data, the mean or median is usually used, and for categorical data, the mode is used. For this study, the mean method was used. Since outliers may have a negative impact on the modeling process, these values were removed from the data set. The data were converted to the range \([0,1]\) with the min-max normalization method. Correlation between features was analyzed for feature selection. The dataset was adapted to the modeling process, and the overall performance of the model was improved. The forward-elimination method was used for data selection in the study. The forward-elimination method starts from a situation where there are no features in the model andadds the best-performing feature to the model at each step. This process continues until a certain stopping criterion is reached. These criteria are usually no significant improvement in the performance of the model, a certain number of features, or some other specified target. Normalization, partitioning and analysis were performed, respectively. In the first stage, the data were normalized according to the specified method. Min-max normalization is a technique frequently used in data preprocessing. This method transforms data expressed in different ranges into a range between 0 and 1, making them comparable. The smallest (minimum) and largest (maximum) values are found for each feature in the data set. For each data point, the minimum value is subtracted from the value for that feature and divided by the difference between the maximum value and the minimum value. The min-max method normalizes the data linearly. While the minimum is the lowest value a data point can take, the maximum is the highest value a data point can take [36]. \[x^{i}=\frac{x_{i}-x_{min}}{x_{max}-x_{min}} \tag{6}\]  
In this equation, \(x^{i}\) denotes the normalized data; \(x_{i}\) denotes the input value; \(x_{min}\) denotes the smallest number in the input set; \(x_{max}\) denotes the largest number in the input set. In this study, R\({}^{2}\) and RMSE statistical metrics were used to analyze the success and error values of the model. R\({}^{2}\) is a measure of the model's explainability. R\({}^{2}\) is a statistical measure of how close the data are to the fitted regression line and works by measuring the amount of variance in the predictions explained by the dataset. It is also a measure of the difference between the observations in the data set and the predictions generated by the model [37]. R\({}^{2}\) takes a value between 0 and 1. R\({}^{2}\) value of 1 means that the model is perfect, while a value of 0 means that the model performs poorly on a data set that it has not encountered before [38]. The closer R\({}^{2}\) is to 1, the better trained the model is. \[R^{2}=1-\frac{\text{Unexplained Variation}}{\text{Total Variation}} \tag{7}\]  
RMSE measures the average magnitude of the error. RMSE is found by taking the root of the square of the difference between the predicted value and the true value for all values in the data set. RMSE is heavily influenced by outliers and is often used to reconstruct the error distribution [39]. \[RMSE=\sqrt{\frac{\sum_{j=1}^{n}e_{j}^{2}}{n}} \tag{8}\]  
where e represents the amount of error, and n represents the amount of data. It is crucial to use a variety of data sets and techniques to verify the applicability and robustness of the model. Training-test separation, cross-validation, external data sets, and comparison with standard data sets are some of these methods. In this study, the data set was divided into two as training and testing. This method allows you to evaluate the performance of the model against data that it has not seen before.