Readme.txt.txt

Topic:unsupervised learning- Hierarchy Clustering

The objective of this project is to use a different algorithm of clustering data.
In class we learned the K means algorithm, which is clustering data that is closer to one another based on the mean of the cluster.
In our algorithm we will be using Hierarchy Clustering to get the cluster of students based on their GPA and SAT.
This can be helpful to help determine students for college admission type senarios.

Algorithm: Our task was to use a dataset that has students GPA and SAT to determine their ranking. This can be used
in multiple situations such as admission, finding the best students, and many more. The algorithm is to clusters the data based on their 
similarities and it would keep on making new clusters until it has gotten all the data as a whole. Then we can divide the the hierarchy of the cluster in multiple 
different cluster based on our criteria on what we need.

Data: We are using the GPA and SAT csv file to extract the data onto our code

Input: CSV file
Output: A graph with a hierachry cluster of the data that was inputed from the CSV file

Project instructions:

1) In this compressed zip file you will need to extract the files

2) Once extracted you will need to open up jupyter notebook.
-This can be downloaded using this link -> https://jupyter.org/install

3)Install the following libraries that are needed to run the file.
-pandas
-numpy
-matplotlib
-sklearn
-scipy.cluster

4) once jupyter notebook is open, find the file that ends with .ipynb, and open the file in juypter notebook

5) run each file using shift+enter