FIT5149留学生讲解、R语言辅导、讲解R编程设计、辅导Critical Temperature
调试C/C++编程|讲解Processing
FIT5149 S2 2019 Assessment 1
Predicting the Critical Temperature
of a Superconductor
Aug-2019
Marks 15% of all marks for the unit
Due Date 17:00 Friday 13 Spetember 2019
Extension
An extension could be granted for circumstances. A
special consideration application form must be
submitted. Please refer to the university webpage on
special consideration.
Lateness
For all assessment items handed in after the official
due date, and without an agreed extension, a 10%
penalty applies to the student’s mark for each day
after the due date (including weekends, and public
holidays) for up to 5 days. Assessment items handed in
after 5 days will not be considered.
Authorship
This assignment is an individual assignment and
the final submission must be identifiable your own
work. Breaches of this requirement will result in an
assignment not being accepted for assessment and
many result in disciplinary action.
Submission
You are required to submit two files, one is either a
Jupyter notebook or a R Markdown file, another is the
PDF file generated by them. The two files must be
submitted via Moodle. Students are required to
accepted the terms and conditions in the Moodle
submission page. A draft submission won’t be marked.
Programming
language R in Jupyter Notebook or R Markdown
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Introduction
Superconductivity is a phenomenon of exactly zero electrical resistance and
expulsion of magnetic flux fields occurring in certain materials, called superconductors,
when cooled below a characteristic critical temperature. Superconductors
are widely used in many industry fields, e.g. the Magnetic Resonance
Imaging (MRI) in health care, electricity transportation in energy industry and
magnetic separation, etc.
Predicting the critical temperature (Tc) of a superconductor is still an open
problem in the scientific community. In the past, simple empirical rules based on
experiments have guided researchers in synthesizing superconducting materials
for many years. Nowadays, features (or predictors) based on the superconductor’s
elemental properties can be generated and used to predict Tc.
In this task, we are going to analyze superconductor data from the Superconducting
Material Database maintained by Japan’s National Institute for Materials
Science (NIMS). The aim is to build statistical models that can predict
Tc based on the material’s chemical properties.
Specifically, you are going to analyse a superconductor data set, which is
based on real world material science data. The problem you are going to solve
is: Can you
• predict the critical temperature Tc given some chemical properties of a
material?
• explain your prediction and the associated findings? For example, describe
the key properties associated with the response variable.
Data set
The data set was originally from from the Superconducting Material Database
maintained by Japan’s National Institute for Materials Science(NIMS) and prepossessed
by Kam []. It contains 21,263 material records, each of which have 82
columns: 81 columns corresponding to the features extracted and the last 1 column
of the observed Tc values. Among those 81 columns, the first column is the
number of elements in the material, the rest 80 columns are features extracted
from 8 properties (each property has 10 features). Detailed data preparation
process can be found in [].
The data set files are stored in UCI’s website below (click the hyper-line to
download the data)
superconduct.zip : After you unzip the file, there are two data sets: train.csv
can be used to train and validate prediction models and build a description
(21,263 material records). Each record consists of 82 columns, containing
number of elements (column 1), features extracted from 8 properties
(columns 2-81) and the critical temperature (column 82). unique m.csv
tells you the chemical formula of each corresponding material.
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In order to finish the analyse task, you should split the provided train.csv into
your own training and testing sets before building the models.
Task description
In this assessment, you will focus on the following two tasks.
Prediction task
For the prediction task, the underlying problem is to estimate the critical temperature
given a new conductor’s properties. There are eight properties that can
be used: Atomic Mass, First Ionization Energy, Atomic Radius, Density, Electron
Affinity, Fusion Heat, Thermal Conductivity, Valence. For each property,
ten features are extracted: Mean, Weighted mean, Geometric mean, Weighted
geometric mean, Entropy, Weighted entropy, Range, Weighted range. Standard
deviation, Weighted standard deviation. The provided data sets are well organised,
you do not need to wrangle the data. But make sure you understand the
intuition of these attributes.
To measure the performance of your model(s), you firstly split the original
data into training and testing set, fit the model using the training set, do the
predictions on the test set and compute the Mean Squared Error (MSE).
In this task, you are required to develop models that can accurately predict
a superconductor’s critical temperature. To finish the task, you should
1. develop and compare 2 to 3 models;
2. describe and justify the choice of your models;
3. analyze and interpret your results
Please note that testing set cannot be used to train your models.
Description task
The purpose of the description task is identify the key properties for a superconductor.
In other words, which property contributes the most to your model’s
performance? Descriptions can be based on variable correlation analysis, regression
equations, linguistic descriptions, or any other form. The descriptions
and the accompanying interpretation must be comprehensible, useful. To finish
this task, you should use proper data analysis techniques (e.g., EDA, statistics)
to
1. identify a subset of attributes that have a significant impact on the prediction
of the critical temperature;
2. and give statistical reasons of your finding.
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Files to be submitted
There are two files required to be submitted, which are
• The R implementation of the two tasks in one file.
– The file must be either a Jupyter notebook or an R Markdown
file. Besides the R code, all the discussions must also be included in
the file.
– The name of the file must be in one of the following formats:
∗ XXXXXXXX FIT5149 Ass1.ipynb
∗ XXXXXXXX FIT5149 Ass1.Rmd
You should replace “XXXXXXXX” with your student ID.
• A PDF file generated by the Jupyter notebook or R Markdown. The name
of the PDF file must be in the following format
– XXXXXXXX FIT5149 Ass1.pdf
Please refer to the Assessment 1’s Moodle page for how to submit the two
files. Please note that If you do not follow the instruction to name your files, a
penalty will be applied.
Additional learning resources
This assessment is based on the paper A Data Driven Statistical Model for
Predicting the Critical Temperature of a Superconductor at https://arxiv.
org/pdf/1803.10260.pdf
• Raw data is available at http://supercon.nims.go.jp/supercon/material_
menu
Warning: Monash University takes academic misconduct very seriously. You
can learn from the above materials and understand the principle of how the
analysis was done. However, you must finish this assessment with your own
work.
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