• To familiarize with the implementation of a machine learning algorithm from scratch, without the usage of any machine learning API.
• To apply KNN algorithm to classify the age of abalone using the Abalone dataset.
• To familiarize with evaluating the performance of a machine learning algorithm

There are 4,177 data observations in the dataset with 8 input attributes and 1 output variable. The input attributes are as follows:

1. Sex [Male (M), Female (F), or Infant (I)]
2. Length
3. Diameter
4. Height
5. Whole weight
6. Shucked weight
7. Viscera weight
8. Shell weight
9. Rings (output)

1. Use the function loadData() to load data from file. The command X = loadData(‘abalone.data’) returns an array of size . In this function, the values of the first attribute have been converted into floats:

• M: 0.333
• F: 0.666
• I: 1.000

2. Normalize the dataset. You are to normalize the 8 input attributes by writing a function, dataNorm(). The normalization equation is given as: (data-min)/(maxmin)

3. Split the dataset into training and testing set by: (i) Using the train-and-test split method. (ii) Using the -fold cross-validation method. Note that the k -value here is different from the K-value in the KNN algorithm. Set the k value to 5, 10 and 15 respectively.

4. Implement the KNN algorithm by writing a function, KNN() . You can use the Euclidean distance as the similarity measure for any two samples.

5. Use the classification_report() function provided by the scikit-learn library to construct aclassification report for the 5-fold cross validation with K = 15