The folder data/university/ contains university data.
It will be necessary to cluster these universities into private and non-private.
It will be necessary to build a clustering model using clustering algorithm from the Spark ML library.
The folder data/university/ contains university data (train.csv).
The Private column was cut from the data set.
This column is located in the file is_private.csv.
The data contains the following fields:
|-------------|-------------------------------------------------------------------------|
| Variable | Definition |
|-------------|-------------------------------------------------------------------------|
| Name | University name |
| Private | A factor with levels No and Yes indicating private or public university |
| Apps | Number of applications received |
| Accept | Number of applications accepted |
| Enroll | Number of new students enrolled |
| Top10perc | Pct. new students from top 10% of H.S. class |
| Top25perc | Pct. new students from top 25% of H.S. class |
| F.Undergrad | Number of fulltime undergraduates |
| P.Undergrad | Number of parttime undergraduates |
| Outstate | Out-of-state tuition |
| Room.Board | Room and board costs |
| Books | Estimated book costs |
| Personal | Estimated personal spending |
| PhD | Pct. of faculty with Ph.D.’s |
| Terminal | Pct. of faculty with terminal degree |
| S.F.Ratio | Student/faculty ratio |
| perc.alumni | Pct. alumni who donate |
| Expend | Instructional expenditure per student |
| Grad.Rate | Graduation rate |
|-------------|-------------------------------------------------------------------------|
A more detailed description of the data can be found on Kaggle.
The task is to train a machine learning clustering model that will split university into 2 clusters (private and not private).
Conditions:
- Model should be trained only using Spark ML clustering algorithm.
- You need to train the model on a training data set
train.csv. - Calculate metrics based on the file
is_private.csv.