PHASE $3(30\%)$

Phase $2$ program, which implements k$-$means algorithm, produces two clusters $-$ one containing benign

cells $($predicted class $=2)$ and the other one that contains malign cells $($predicted class $=4).$ But there are

chances that a malign cell is clustered into a benign cluster and vice versa.

In phase $3$ you will analyze the quality of the clustering. To check how well your clustering worked, you

will calculate the error rate for your clusters. Assume that the column "Class" of the initial data set

contains correct clustering of the data points.

INSTRUCTIONS

There are two parts in phase $3$:

Write a code to calculate the individual and total error rates of the predicted clusters.

Prepare and submit final report

a$)$ Write code to calculate the individual and total error rates of the predicted clusters

Your phase $3$ program will calculate the error rates based on two arguments:

The predicted clusters, calculated by your phase $2$ program,

The correct clusters, specified by the column "Class" of the initial data set.

Let's have a look at the example of the cluster assignment with first $20$ data points, listed on page

Column "Class" represents the correct clusters and column "Predicted$\_$Class" represents the

clusters calculated by the $k-$means algorithm.Marked data points represent the errors of the k$-$means clustering:

Yellow data points are predicted as class $4($malign cells$),$ while the correct class is $2$

$($benign cells$).$

Gray data points are predicted as class $2($benign cells$),$ while the correct class is $4$

$($malign cells$).$

Let's define the following notation:

Use the following formulae to calculate and print error rates for each cluster:

$erro{r}_B=(erro{r}_{24}\frac{?}{?}pclas{s}_2)**100\%$

$erro{r}_M=($ erro$\frac{r_{42}}{p}clas{s}_4)**100\%$

$erro{r}_T=\frac{erro{r}_{a}ll}{clas{s}_{a}ll}$

Total error rate more than $50\%$ indicates that your program swapped the predicted clusters. Your

program has to detect this situation, swap the predicted clusters by replacing $2$ with $4,$ and $4$ with

$2$ in column "Predicted$\_$Class", and recalculate the error rates.

b$)$ Prepare final report that incorporates all the results and your conclusions for phases $1$ to $3.$

SAMPLE OUTPUT

This is the output in case the clusters are swapped and the program swapped the predicted class.Error data points, Predicted Class $4$:

Number of all data points: $699$

Number of error data points: $28$

Error rate for class $2$: $,3\mathrm{.}7\%$

Error rate for class $4$: $,4\mathrm{.}7\%$

Total error rate: $,4\mathrm{.}0\%$

SUBMISSION GUIDELINES

Prepare and submit a PDF with final report that includes:

$>$ Project statement

Short description of phase $1,2$ and $3$ programs $($algorithm$,$ description of input data,

structure of the programs and description of results$)$

$>$ Phase $1,2$ and $3$ results

$>$ Conclusion

Submit phase $1,2$ and $3$ programs together with any data files that may be needed to run your

programs.

Provide 'readme.txt$\text{'}$ file that provides information about how to execute your code.

I did phase $1$&$2$ just need #$3$