Select any dataset that contains at least 100 observations and at least 10 attributes. Perform detailed analyses of the selected data by using one data reduction method (Singular Value Decomposition or Principal Component Analysis) and one clustering method (k-Means or Hierarchical). Explain your choices and discuss your results.

NOTES:

The link and the description of the selected dataset should be provided, and the dataset should not have been used in the lectures or labs of the course.

Any preprocessing method (e.g. removal or filling of empty cells) performed on the original data needs to be fully described.

Your analyses shall include the descriptions of your Python codes and plots.

Data Set Link:

https://drive.google.com/file/d/1Sgr0vYwLHb9BA2Bgaohm9z48bI-OlN3j/view?usp=sharing

DataSet Layout:

Description Label

Observation number NUM

Year YEAR

Month MONTH

Amount of bill (in dollars), BILL includes 5% sales tax

Average temperature (in degrees Fahrenheit)TEMP

Heating Degree Days HDD

Cooling Degree Days CDD

Number of family members at homeSIZE

New meter? (indicator variable, 1 = yes)METER

New heat pump 1? (indicator variable, 1= new) PUMP1

New heat pump 2? (indicator variable, 1= new) PUMP2

Total charge (per kwh) for all ridersRIDER TOTAL

Calculated consumption (in kwh)CONSUMPTION