Doyun-lab

Subject : Prediction of Patients with Low Blood Pressure during Anesthesia Language : R Data : ‘수술 중 마취한 환자의 혈압과 정보’ 데이터 Model : Random Forest, SVM, Boosting 1. Data parsing & preprocessing # total 이라는 리스트에 모든 엑셀 파일을 불러와 저장 setwd("C:\\r_temp\\homework") total

Subject : Prediction of Power Energy Consumption in Apartment and Shopping Districts in Korea Language : R Data : ‘국내 특정 지역 아파트와 상가의 전력에너지 사용량’ 데이터 1. Data preprocessing 2. EDA 3. Modeling 사용 모델 : ARIMA, Smoothing, Neural Network (TLFN, RNN) setwd("C:\\r_temp\\result") test

Subject : Predicting real estate prices in Korea with various variables Language : R Data : ‘직방’ 데이터 train.csv : Apartment transaction data (1.6 million) school.csv : Elementary, middle and high school information (1,100) subways.csv : Subway information (400) 1. Data preprocessing colnames(train)