# Add projections
## 7 Results and Discussion of Data Classification  
The dataset contains mechanical tensile properties YS, UTS and EL. We have introduced a dummy categorical variable called ductility based on the value of EL. Ductility holds value as one if EL value is greater than 15 and holds value as zero if EL value is less than 15. We chose Tensile YS, UTS as independent input variables and ductility as the output variable. The dataset is now divided into test and train samples. Out of 161 data rows, 80 percent of the dataset, i.e., 128 rows form the train data set and 20 percent of dataset i.e., 33 rows form the test dataset. The independent input variables in the train dataset are normalized using Standard Scaler function before fitting to the model. The 10-fold cross validation is applied on the 128 rows and observed the accuracies of 70.25%, 75% and 71.92% for Logistic Regression, SVM, Random Forest, respectively. Figure 6 shows the spot-check algorithm comparison of all the three models. The predictions are made by fitting the transformed input values, output values of the train dataset (128 data rows) and tested on an independent dataset of 33 rows using the Logistic regression and SVM models, with observed accuracies of 72.72% and 78.78%,  
Figure 6: Spot Check Algorithm comparison for Logistic Regression, SVM and Random Forest models. respectively. Standardization did not improve the model performance and hence we used the original independent values with Random Forest, as the model is not sensitive to the magnitude of any two input variables. We have tested the model on an independent dataset of 33 rows and observed an accuracy of 87.87%. We noticed that Random Forest outperformed, with an increase of 15% and 9% against Logistic Regression and SVM models. Table 3 shows the details of parameters that we have used for all three models. For all three models, the random state parameter is set to zero to calculate the reproducible output across multiple function calls. For Logistic Regression, the parameter for solver is set to liblinear based on the smaller size of the dataset, l2 penalty is chosen to apply Ridge regression regularization method. For SVM, as the data is non-linearly separable, we have evaluated the model performance using two different kernels as listed in Table 3. Firstly, for SVM-Model-1 the kernel is set to the radial basis function (RBF), the regularization parameter C is set to 1 (the regularization strength is inversely proportional to C value) and the constructor option probability is set to True, to enable the class membership probability estimates (from the methods predict_proba and predict_log_proba). This probability variable is used to calculate the Receiver Operating Characteristic (ROC)--Area under curve (AUC) score to determine the performance of the classification model. Secondly, the kernel is changed to polynomial and the degree of polynomial is set to a numerical value 8 and the rest of the model parameters remain the same. When evaluated, both the models SVM-Model-1 and SVM-Model-2 equally performed, resulting in an accuracy of 78.78%. For Random Forest, we have set the model parameters n_estimators equal to 200 and criterion as \"entropy\". The parameter \"n_estimators\" is to specify the number of the trees in the forest and criterion: \"entropy\" for the information gain. Here we can also use Gini as criterion, but we have observed that model performed better with entropy for criterion. Below is the comparison of performance metrics of Logistic regression, SVM and Random Forest models. From Table 4, we can notice that Logistic regression has low performance when compared to the other models and Random Forest model has outperformed with a value of 0.88 for all the metrics- precision, recall, F1-Score, and accuracy.