Implement this using python: 
INCA is an iterative and developed version of the NCA, and it was proposed by Tuncer et al in 2020.23 INCA uses a loss function to select the best features, and it has an iterative structure. It is a parametric feature selection method. Users can define the initial value of the loop, the final value of the loop, and the loss function. Generally, a classifier has been utilized as a loss function. Loop range is defined to decrease the time complexity of the INCA. The initial value of the loop value is set to 100, and End value of the loop value is set to 1000, and third degree (Cubic) SVM with 10-fold cross-validation is utilized in the loss function. By using these parameters, the best features are selected from the generated 3000 features. The length of the selected best feature vector is found as 980.