3551 Trousdale Rkwy, University Park, Los Angeles, CA
You are required to prepare a set of presentation slides which must include (1) the full name
and student number of each student in the group, the contribution (in percent) of each group
member, (2) a description of the task, (3) description of your approach and how the
methodology was implemented; (4) the strengths and weaknesses of the approach or
implementation; (5) your results and an analysis of the results; (6) a brief summary and a
conclusion. The summary should state new and interesting things that you learned and
discovered while working on this project. The conclusion should summarize your main findings
and statements about possible future work (e.g., how you plan to improve your models and
approach in future).
Below is the recommended structure of your slides:
• Introduction (define the problem and the goal)
• Methods (approach, and discussion of strengths and weaknesses)
• Implementation (methods, key-issues, how these were addressed and sample codes)
• Results (include illustrative Figures and Tables and explanations)
• Discussion and Conclusions
Definition of the task:
You are to implement an end-to-end data mining project to analyse the provided dataset. The
objective is to implement a workflow to predict the targe variable of the data (i.e., classification
or regression). This workflow must include two stages, as illustrated in Figure 1.
Figure 1. The workflow stages
Stage1: Data exploration
This stage includes the use of Spark’s DataFrame and RDD APIs in Python to explore the data.
Understand the dataset by querying a few important statistic measures of the data. Visualise the
data and explain your findings. It is important that you demonstrate an in-depth understanding
on the data that you are analysing. (Note. You cannot use Pandas and Scikit-Learn in this stage.)
ML process in
ML process in
Stage2: Predictive analysis
This stage includes three machine learning (ML) processes. Each process must include at least
three kinds of ML models (such as decision tree, random forest, naïve Bayesian, feedforward
network, etc.). The models must be evaluated with common metrics (such as accuracy,
precision, recall and ROC).
Specific requirements of Process One:
• All ML models and evaluation methods in this process must be implemented from
scratch in Python. You can use any Python modules except the ML libraries.
Specific requirements of Process Two:
• This process is built with the ML library of Spark (i.e., pyspark.ml and pyspark.mllib)
Specific requirements of Process Three:
• This process is built with TensorFlow and Keras.
• Python Source Codes
In your slides, you must explain the detailed pipeline design and evaluation outcomes, as well
as any other interesting findings or lessons learned. Any claim that you make in the slides must
be supported by the implementation in your submitted Python source codes.