Analyze the income in different groups of people.

Read the data from the txt file "people.txt$",$ perform some data preprocessing steps $($e$.$g$.$ data normalization, string encoding, etc.$),$ and save the preprocessed data as a new txt file $"$my$\_$data.txt$".$

Read the data from the txt file $"$my$\_$data.txt$",$ and delete the last column $($i$.$e$.$ "Income"$).$ Then, please apply K$-$means algorithm to cluster the data, and find out the approriate value of $K.$

Read the data from the txt file $"$my$\_$data.txt$",$ use the last column $($i$.$e$.$ "Income"$)$ as the class label $($or the ground truth value$),$ and randomly partition the data into training set $(70\%)$ and testing set $(30\%).$ Then, please build the support vection machine $($SVM$)$ model using training set, and applied this established model to predict the class label of each data point in testing set.