Price Prediction and Analysis Based on the LightGBM Regression Model

Price forecasting is widely used in many fields.lt is of great significance for economic development to find out the model and method of predicting price accurately.Traditional methods of predicting prices include market comparison, income restoration, and cost estimation, but these methods require a significant amount of human and material resources and usually fail to predict housing prices accurately due to the influence of numerous factors. Therefore, this paper aims to use the LightGBM regression model and LGBMRegressor algorithm for the prediction and analysis of housing prices. We collected a large amount of data on second-hand housing prices and preprocessed the data to eliminate entries with missing values. Subsequently, we performed a general descriptive statistical analysis of the data and established feature data and label data based on different characteristics such as region, age of the house, nearby geographical conditions, cultural and traffic factors. Finally, we divided the dataset and made model predictions. The experimental results show that the predicted values of the regression model are basically consistent with the actual fluctuations in housing prices, allowing for relatively accurate predictions.