ICT707 Data Science Practice

Assignment Task

This assignment consists of two deliverables, being:

• One code implementation (50%). The code file in Jupyter Notebook format and the relevant data set files should be contained within a folder named: Task 3-Your Name-Student_Number, the folder is then to be zipped and uploaded to blackboard.

• A report (50%). The report must be uploaded as a separate file.

Part I - PySpark source code (50%)

Important Note: For code reproduction, your code must be self-contained. That is, it should not require other libraries besides PySpark environment we have used in the workshops. The data files are packaged properly with your code file.

In this component, we need to utilise Python 3 and PySpark to complete the following data analysis tasks:

1. Exploratory data analysis
2. Recommendation engine
3. Classification
4. Clustering

You need to choose a dataset from Kaggle (https://www.kaggle.com/datasets) to complete these tasks. Remember to include the data set file in you source code submission.

Note: In your notebook, please use Heading 1 Markdown cell to separate each sub task.

Task I.1: Exploratory data analysis

This subtask requires you to explore your dataset by

• Telling its number of rows and columns,
• Doing the data cleaning (missing values or duplicated records) if necessary
• selecting 3 columns, and drawing 1 plot (e.g. bar chart, histogram, boxplot, etc.) for each to summarise it

Task I.2: Recommendation engine

This subtask requires you to implement a recommender system on Collaborative filtering with Alternative Least Squares Algorithm. You need to include
• Model training and predictions
• Model evaluation using MSE

Task I.3: Classification

This subtask requires you to implement a classification system with Logistic regression with LogisticRegressionWithLBFGS class. You need to include
• Logistic Regression model training
• Model evaluation

Task I.4: Clustering

This subtask requires you to implement a clustering system with K-means. You need to include
• Model training
• Model evaluation

Part II –Report

You are required to write a report to explain your design and implementation of the machine learning parts in your code, including the following topics:
• Introduction/summary/explanation to the ML algorithm/concepts
• The learning settings, such as how to prepare training/testing set, what are the key parameters and how you set them up
• Comments/evaluation for the models learnt