This study addresses the current societal demand for environmental monitoring by designing an environmental monitoring system based on the STM32 platform. This system assesses and monitors environmental conditions in real-time by tracking parameters such as CO, PM2.5, temperature, humidity, and light intensity. It holds significant value in preventing air pollution and improving indoor air quality. The system employs four types of sensors: the DHT11 digital temperature and humidity sensor, the BH1750FV light sensor, the GP2Y1010AUOF optical dust sensor, and the MQ-7 CO sensor to collect environmental data, which is then processed by the STM32F103C8T6 controller. This system is characterized by its real-time capabilities, high precision, and low power consumption, making it highly practical and valuable for widespread application. The paper provides a detailed discussion of sensor selection, measurement algorithms, and system design and implementation, offering valuable insights for research and applications in related fields.

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This paper aims to use multiple linear regression model and random forest models to analyze and study the factors affecting the housing price in Boston. The multiple linear regression model describes the relationship between multiple independent variables and one dependent variable through linear equations, and the random forest improves the accuracy and robustness by constructing multiple decision trees and combining their prediction results. To deal with complex nonlinear relationships and high dimensional data. Housing price is an important index to reflect the level and condition of economic and social development of a region, so it is of theoretical value and practical significance to explore its influencing factors and ways and degrees. Multiple factors are selected to analyze the weight and importance of each influencing factor, so as to help the government and decision makers to formulate more accurate policies, promote the stable development of the market, and provide scientific decision-making support for real estate developers, investors and ordinary buyers. In this study, the random forest model based on decision tree was used to clean, select and reduce the acquired housing price data, and to find out the main factors affecting housing price from the perspective of information gain, so as to obtain a relatively complete mathematical model and provide a reference scheme for future research by scholars.

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Gold is one of the most prevalent currencies in the world and its price has a very strong influence in the global financial markets. Gold has safe-haven properties, which can have a significant impact on its demand and price, especially in times of social unrest or financial crisis. Now, the demand for gold by investors has increased dramatically. Therefore, being able to accurately predict the direction of the gold price can help investors to effectively develop investment strategies and risk management measures. The overall objective of this study is to forecast the price of gold futures for the next six months. In this study, the Kaggle website was searched to find the price of gold from 2020 to 2024 and finally the CLOSE price was chosen as the final predicted price. This paper uses the ARIMA model for gold price forecasting. By comparing the RSME size of each model, ARIMA (1, 1, 2) is finally chosen. From the prediction results the price of gold remains stable in the first half of the year and then increases significantly. From the results of the residual test, there is no autocorrelation, and then it is white noise.

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The second-hand car market is a hot topic. Buying a second-hand car has advantages in price and many other aspects. Therefore, it is important to establish a good price prediction model. This paper will explore the factors that affect the price of second-hand cars. After analyzing and learning many kinds of literature, this paper establishes a multiple linear regression model and a random forest model and makes a comparative analysis of the model effect. The sum of the square error and R-square value of the random forest are better than the multiple linear regression model. Among the factors affecting the price of second-hand cars, the year of production has the greatest impact on the price, which shows that the age of the year is an important factor in determining the price of second-hand cars. The next most important factor is the number of kilometers traveled, followed by fuel type and transmission type-finally, engine displacement, number of transfers and number of seats. The random forest model established in this paper has better application value to price prediction.

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