Initial Dataset

DATASET	DESCRIPTION	DATA USED
Average Monthly Household Electricity Consumption by Postal Code (Public Housing) & Dwelling Type, 1H & 2H 2015 Link (1H): https://www.ema.gov.sg/cmsmedia/Publications_and_Statistics/Statistics/23RSU.xls Link (2H): https://www.ema.gov.sg/cmsmedia/Publications_and_Statistics/Statistics/25RSU.xls	Average monthly household electricity consumption (kwh) By month By postal code By public housing type Total Average household electricity consumption (kwh) By postal code By public housing type	9379 rows of raw data X 12 sheets = 112,548 rows of raw data
Average Monthly Household Electricity Consumption by Postal Code (Private Apartments), 2015 Link: https://www.ema.gov.sg/cmsmedia/Publications_and_Statistics/Statistics/2RSU.xls	Average monthly household electricity consumption (kwh) By month By postal code Total Average household electricity consumption (kwh) By postal code	9911 rows of raw data
Basic Demographics Characteristics (2015) Link: http://www.singstat.gov.sg/docs/default-source/default-document-library/publications/publications_and_papers/GHS/ghs2015/excel/t7-9.xls	Resident Population by Planning Area/Subzone By age group By sex By ethnicity By type of dwelling	T7 Age group 378 rows of raw data T8 Ethnicity 378 rows of raw data

Data Cleaning

METHOD	DESCRIPTION
Data cleaning: Household electricity consumption data Stack data to consolidate data table in to 2 columns (Postal Code, Housing Type) Remove rows with missing data
Data cleaning: Household electricity consumption data Concatenate all 12 months data into one consolidated data table By the end of this phase of data cleaning, we have a total of 177,053 rows
Data cleaning: Household electricity consumption data Merging Private Housing Data with Public Housing Data Final consolidated data consist of 241,766 rows
Geocoding: Postal codes with missing latitudes and longitude via https://developers.google.com/maps/documentation/geocoding/intro Public housing data: 223 missing data Private housing data: 338 missing data
Data cleaning: Age, Gender, Ethnicity Delete rows that are empty & blank so at to merge the tables into one data sheet
Data cleaning: Age, Gender, Ethnicity Consolidate data through Stacking