This task is to produce a visualisation from initial data analytics. This is a key stage in any machine learning or data
Posted: Mon May 09, 2022 7:02 am
This task is to produce a visualisation from initial data
analytics. This is a key stage in any
machine learning or data science project which should inform your
future investigations,
and can be a vital tool when communicating findings with
stakeholders.
You should submit a single file (not zipped) named myVisualiser.py
containing a
function with signature:
def cluster_and_visualise(datafilename, K, featurenames):
This function has three parameters (respectively a string, an
int and a list) and must:
Read in a dataset from a file called ‘datafilename’. The data
will be commaseparated, so if you are using numpy.genfromtxt() you
will need to set the
parameter ‘delimiter’ appropriately
Run the kmeans algorithm on the data to get a cluster labels for
each data point.
Make a 2d visualisation of the results with:
o Suitable titles and labels for axes to make the plot
self-explanatory
o Different colour markers for each cluster
o A suitable title including your name, and preferably the number
of clusters
o Suitable size plot – for example setting the “figsize” parameter
to (12,12)
Save the visualisation to a file called myVisualisation.jpg
Return the fig, ax (the figure and axis variables) to the calling
function
analytics. This is a key stage in any
machine learning or data science project which should inform your
future investigations,
and can be a vital tool when communicating findings with
stakeholders.
You should submit a single file (not zipped) named myVisualiser.py
containing a
function with signature:
def cluster_and_visualise(datafilename, K, featurenames):
This function has three parameters (respectively a string, an
int and a list) and must:
Read in a dataset from a file called ‘datafilename’. The data
will be commaseparated, so if you are using numpy.genfromtxt() you
will need to set the
parameter ‘delimiter’ appropriately
Run the kmeans algorithm on the data to get a cluster labels for
each data point.
Make a 2d visualisation of the results with:
o Suitable titles and labels for axes to make the plot
self-explanatory
o Different colour markers for each cluster
o A suitable title including your name, and preferably the number
of clusters
o Suitable size plot – for example setting the “figsize” parameter
to (12,12)
Save the visualisation to a file called myVisualisation.jpg
Return the fig, ax (the figure and axis variables) to the calling
function