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 validates the efficacy of ensemble techniques in capturing complex relationships, offering valuable insights for real estate forecasting and decision-making.

Test

Giáo trình xác suất thống kê và ứng dụng - Lê Sĩ Đồng

Predicting Crop Yield output and Classifying crop type based on climate data

5-slide pitch to accompany and explain the Next-Word-Predictor Shiny App. This presentation is part of the final project for the Johns Hopkins Data Science Specialization.

WQD7004 Group Assignment - Mohammad Iqbal Afif bin Mohamad Shahnaz (U2004720) - Ahmad Marwan Bin Murshidi (17205900) - Mawaddah Binti Musthafa (17147301) - Mohamad Ziqry Bin Zulkifli (S2153309) - Ahmad Salim Odeh Alsane (23083537)