FIT5196-S2-2018 assessment 2
This is an individual assessment and worth 35% of your total mark for
FIT5196.
Due date: 11:55 pm, Wednesday, 3 October 2018
Data Cleansing (%70)
For this assessment, you are required to write Python (Python 2/3) code to analyze your dataset,
find and fix the problems in the data. The input and output of this task are shown below:
Table 1. The input and output of the task
Input Output Jupyter notebook
Exploring and understanding the data is one of the most important parts in the data wrangling
process. You are required to perform both graphical and non-graphical EDA methods to
understand the data first and then find the data problems. However, as a starting point, here is all
we know about the dataset in hand:
The dataset is about delivering packages using drones in Victoria, Australia. The description of
each data column is shown in Table 2.
Table 2. Description of the columns
COLUMN DESCRIPTION
Id A unique id for the delivery
Drone type A categorical attribute for the type of the drone. We know that
each type of drone has three phases of flight (namely takeOff,
onRoute, and Landing). The drone may have different speeds at
different phases. takeOff and Landing phases only take five
minutes.
Post type A categorical attribute for the type of delivery (0:normal,
1:express)
Package weight The weight of the package
Origin region A categorical attribute representing the region for the origin of the
delivery
Destination region A categorical attribute representing the region for the destination
of the delivery
Origin latitude Latitude of the origin
Origin longitude Longitude of the origin
Destination latitude Latitude of the destination
Destination longitude Longitude of the destination
Distance Distance of the journey
Departure date Date of the departure
Departure time Time of the departure. We know that the delivery company has a
specific rule to define morning (6:00:00 – 11:59:59), afternoon
(12:00:00 – 20:59:59), and night (21:00 – 5:59:59)
Travel time Travel time (i.e., duration) of the journey
Delivery time The time of the delivery
Delivery price Delivery fare. We know that the fare has a linear relation with
some of the attributes of the dataset.
Note 1: the output csv file must have the exact same columns as the input.
Note 2: the radius of the earth is 6378 km.
Note 3: as EDA is part of this assessment, no further information will be given publicly regarding
the data. However, you can brainstorm with the teaching team during tutorials and consultation
sessions.
Note 4: there is at least one error in the dataset from each category of the data anomalies (i.e.,
syntactic, semantic, and coverage).
Documentation (%30)
The cleaning task must be explained in a well-formatted report (with appropriate sections and
subsections). Please remember that the report must explain the complete EDA to examine the
data, your methodology to find the data anomalies and the suggested approach to fix those
anomalies.