Big Data Analytics & Statistical Analytics Methods - Statistics Assignment Help

Assignment Task

Task
Visualizing the outcomes of queries into the graphical and textual format and be able to interpret them.

Big Data & Analytics using

PySpark 60 (45) Analyzing the dataset through 3 statistical analytics methods including advanced descriptive statistics, correlation, hypothesis testing, density estimation, etc. (15) Designing one ML-based method, then evaluate and visualize the accuracy/performance. Applying a multi-class classifier is considered for full mark.Documentation 10 (10) Write down a well-organized report for a programming and analytics project (i.e., HTML report) Understanding Dataset: CSE-CIC-IDS20181This dataset was originally created by the University of New Brunswick for analyzing DDoS data. You can find the full dataset and its description here. The dataset itself was based on logs of the university's servers, which found various DoS attacks throughout the publicly available period to generate totally 80 attributes with 6.40GB size. We will use about 2.6GB of the data to process it with the restricted PCs to 4GB RAM. Download it from here. When writing machine learning or statistical analysis for this data, note that the Label column is arguably the most important portion of data, as it determines if the packets sent are malicious or not.

a) The features are described in the “IDS2018_Features.xlsx” file in Moodle page.

b) The labels are as follows:

c) In this coursework, we use more than 8.2-million records with the size of 2.6GB. As a big data specialist, firstly, we should read and understand the features, then apply modeling techniques. If you want to see a few records of this dataset, you can either use [1] Hadoop HDFS and Hive, [2] Spark SQL or [3] RDD for printing a few records for your understanding.

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