Recently Published
Classification of Loan Status and Prediction on Credit Score
Group Member:
Kau Zhi Wei
Chang Qi Han
Cheah Yining
Chee Zi Yaw
Zhuang Zhen Yu
No.33 weekly report on Euronext and BRVM financial markets
Our weekly roundup, from 06th to 10th of January 2025.
Uji HIPOTESIS
Tugas Statistika Dasar 14,Materi Uji Hipotesis
Application of Machine Learning Models on Predicting Cardiovascular Disease
This is group assignment of WQD7004 Programming for Data Science, one of the major course in Master of Data Science in University of Malaysia.
Diagram Jalur
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GVC
WQD7004 Group Project G14
PROGRAMMING FOR DATA SCIENCE OCC3 WQD7004
Group project submission
Dataset link: https://www.kaggle.com/datasets/yusufdelikkaya/datascience-salaries-2024
Predicting House Prices Using Machine Learning Models: A Comparative Analysis of Regression and Ensemble Approaches
This study explores the application of machine learning models for predicting house prices, comparing the performance of traditional regression techniques (Linear, Ridge, and Lasso) with advanced ensemble methods (Random Forest and XGBoost). The findings reveal that ensemble models, particularly XGBoost, outperform traditional methods, achieving the lowest RMSE of 0.4069. Key predictors identified include **distance to MRT**, **house age**, and **latitude**, with proximity to transportation being the most significant factor influencing house prices. The study underscores the efficacy of ensemble techniques in capturing complex relationships, offering valuable insights for real estate forecasting and decision-making.
