Prediksi Harga Rumah Menggunakan Metode Regressi Lasso

Kemas Alfin Yazi Bara Mentari, Anggara Falih Hartono, Rhaeyvan Phasca Sanggam Sinaga, Anggraini Puspita Sari

Abstract


A house is one of the basic human needs. This research aims to predict house prices using the Lasso regression method. With the increasing property market and house prices continuing to soar, accurate price predictions are important for investors, developers, and home buyers. This study uses a dataset of houses in the Greater Jakarta area, with features such as land area, building area, number of bedrooms, bathrooms, and garages. The methodology used includes data pre-processing, feature standardization, division of training and test data, and application of the Lasso regression model with hyperparameter optimization using GridSearchCV. Prediction results are visualized through scatter plots comparing actual values with predictions, as well as residual plots to assess model performance. This research provides insights into the factors that influence house prices and provides predictive tools that can assist in decision-making in the property market. This approach is expected to improve understanding of house price dynamics and facilitate better investment strategies in the real estate sector.


Keywords


Prediction, House Price, Lasso Regression, Machine Learning, Property Market, Jabodetabek

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