In each of the following exercises, the goal is to develop a classification or prediction model for the given situation and data. Follow these steps: 1. Examine the data descriptions and explore the data. 2. Clean the data as needed. 3. Transform the data as needed (e.g., create dummy variables for categorical variables). 4. Partition the data. 5. Run the specified algorithm. 6. Interpret the results and choose the best model (e.g., choose the best k in the k-nearest neighbor method). 1. K-NEAREST NEIGHBORS-CLASSIFICATION The dataset Low Birth Weight.xlsx* consists of data on 189 women, 59 of whom had low-birth-weight babies. The data provided include basic demographic information (e.g., age of mother) aswell as detailed information on the mother's behavior prior to the birth (e.g., smoking status). The goal is to create a k-nearest neighbor model to classify births as low risk