$(3)$ Advanced Analytics using PySpark $[50$ marks$]$ In this section, you will conduct advanced analytics using PySpark. $3\mathrm{.}1.$ Analyze and Interpret Big Data $(15$ marks$)$ We need to learn and understand the data through at least $4$ analytical methods $($descriptive statistics, correlation, hypothesis testing, density estimation, etc.$).$ You need to present your work numerically and graphically. Apply tooltip text, legend, title, X$-$Y labels etc. accordingly to help end$-$users for getting insights. $3\mathrm{.}2.$ Design and Build a Classifier $(35$ marks$)$ a$)$ Design and build a binary classifier over the dataset. Explain your algorithm and its configuration. Explain your findings into both numerical and graphical representations. Evaluate the performance of the model and verify the accuracy and the effectiveness of your model. $[15$ marks$]$ b$)$ Apply a multi$-$class classifier to classify data into ten classes $($categories$)$: one normal and nine attacks $($e$.$g$.,$ Fuzzers, Analysis, Backdoors, DoS, Exploits, Generic, Reconnaissance, Shellcode and Worms$).$ Briefly explain your model with supportive statements on its parameters, accuracy and effectiveness. $[20$ marks$]$