SIT742 - Modern Data Science - Report Writing - IT Assignment Help

Assignment Task

Data Analytic — Web Log Data
Here is the hypothetical background:
Hotel TULIP (a hypothetical organisation) is a five star hotel that locates in Australia. It is a very special hotel with an equally special purpose: Not only does it embody all the creative energy and spirit of TULIP-Lab, it’s a “learning environment” on which the tourism and hospitality students are trained for future hoteliers.
In the past two decades, the Web server of Hotel TULIP has logged all the web traffic to the hotel website, and stored large amount of data related to the use of various web pages. The hotel’s CIO, Dr Bear Guts (not Bill Gates!), believes that those log files are great resources to help their Information Technology Division improve their potential customers’ online experience, and help their Market Promotion Division to identify potential customers and their behaviour patterns.
Hence, Hotel TULIP would like you Group-SIT742 (a hypothetical data analytics group with up to 3 data analysers) to analyse web log files and discover user accessing patterns of different web pages.
The Web server is using Microsoft Internet Information Service (IIS), and the Web log format can be found

Task Description
This task requires you to develop a data analysis report for the provided Hotel TULIP Web logs. Without exploration or further analysis, ‘raw’ Web log data hardly reveals any insightful information.
In this part, you are required to complete the Python code snippets to generate suitable numeric and visual description in the Hotel TULIP Web log dataset based on the detailed requirements in SIT742Task2.ipynb, and develop the report SIT742Task2-Report.pdf to summarise the data analytic results. The detailed requirements can also be found in the notebook SIT742Task2.ipynb, here we summarise them as follows:

1. Data ETL
1.1 Load Data
Load data from files. In order to reduce the processing time, we will remove missing values, and select 30% of total data for the following tasks.
Code

• Remove missing values. For the columns, if the column is with 15% NAs, you need to remove that column. Then, for the rows, if there are any NAs in that row, you need to remove that row (requests)
• Randomly select 30% of the total data into a new dataframe weblog_df.

Report

• Please show the number of requests in weblog_df.

1.2. Feature Selection
Code Select ’cs_method’, ’cs_ip’, ’cs_uri_stem’, ’cs(User_Agent)’ as features and ’sc_status’ as the class label into a new dataframe ml_df for following Machine Learning Tasks.

2. Unsupervised learning

You are required to complete this part using sklearn.
Code • Perform unsupervised learning on le_df with K Means.
Report • Visualization of ‘KMeans’ performance using the elbow plot , with a varying K from 2 to 10.
• What is the best K for this dataset?

3 Supervised learning

You are required to complete this part using PySpark packages for model building and K-Fold Cross Validation.
3.1 Data Preparation
Prepare the data for supervised learning by completing the code.

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