Instead of using K$-$MEANS as stated below: PLEASE SEE IF USING T$-$SNE WILL REDUCE THE NUMBER OF DIMENSIONS. ARE THE RESULTE BETTER, WORSE, OR THE SAME?

$*$please use numpy$**$

he program should print out the habitat number for each alligator in the dataset. Then it should print out the K$-$means centers. Then, we are assuming that a new turtle is arriving. Ask the user to input the length, height, width, color and weight for this new alligator Then have the K$-$mean model predict the correct habitat $(1,2$ or $3)$ for the new alligator.

Here is sample code. This is only a sample of how the program will run. The number may not be correct. Do no use these numbers for testing.

Load alligator data

Create K$-$means mode

Habitat number for each alligator

$[0022200111]$

Centers

$[[4.2.2\mathrm{.}51\mathrm{.}256.]$

$[7.5.3\mathrm{.}666666672.7\mathrm{.}66666667]$

$[8.1\mathrm{.}666666672\mathrm{.}666666672\mathrm{.}666666678.]]$

Enter information for new alligator

Enter length $2$

Enter height $3$

Enter width $4$

Enter color code $(1-3)1$

Enter weight $5$

Put this alligator in habitat $[0]$