a. kNN (K-Nearest Neighbour)
- kNN method is: if most of the k similar samples in the feature space (that is, the closest samples in the feature space) belong to a certain category, the sample also belongs to this category.
- After normalizing tennis related variables by applying the function (x - min(x)) / (max(x) - min(x)) which limit all numeric data to a range between 0 and 1, we train our model through kNN () function.
- Libraries used: class, KODAMA, caret
b. Neural Network
- The motivation behind using Neural Networks on the AO Tennis dataset was to utilise the deep learning nature to build an accurate model to classify the point endings.
- To determine whether our neural network would classify the dataset well, we first divided the data into a training set (67%) and test set (33%). This allowed us to determine how many neurons to include in the hidden layer based on the accuracy of the model.
- Libraries used: neuralnet