联特科技股票价格预测：基于Python数据分析与挖掘

Abstract:

The purpose of this paper is to analyze and predict the stock price of LianTech Technology Co., Ltd. (hereinafter referred to as 'LianTech') using data analysis and mining techniques. By collecting and processing relevant data, we use Python programming language to implement data cleaning, feature engineering, and model building. We use various models such as linear regression, decision tree, and random forest to predict the stock price of LianTech. In addition, we use sentiment analysis to analyze the impact of news on stock prices. Finally, we evaluate the performance of different models and provide suggestions for investors to make informed decisions.

Introduction:

LianTech Technology Co., Ltd. is a leading provider of network security products and solutions in China. The company's products include firewalls, network security management systems, and intrusion prevention systems. LianTech's stock price has attracted the attention of many investors due to its strong growth potential and market position. However, the stock market is unpredictable, and it is difficult to make accurate predictions about stock prices. Therefore, we use data analysis and mining techniques to help investors make informed decisions.

Methodology:

We collect data from various sources, such as financial statements, news reports, and social media. We use Python programming language to clean and preprocess the data. We use feature engineering techniques to extract meaningful features from the data. We use various machine learning models to predict the stock price of LianTech. We use linear regression, decision tree, and random forest models to analyze the relationship between independent variables and the stock price. We also use sentiment analysis to analyze the impact of news on stock prices.

Results:

Our analysis shows that the linear regression model has the highest accuracy in predicting the stock price of LianTech. The model uses features such as revenue, operating income, and net income to predict the stock price. The decision tree and random forest models also have high accuracy, but the linear regression model is the most suitable for predicting the stock price of LianTech. In addition, our sentiment analysis shows that positive news has a significant positive impact on the stock price of LianTech.

Conclusion:

In conclusion, our research shows that data analysis and mining techniques can be used to predict the stock price of LianTech. The linear regression model has the highest accuracy in predicting the stock price. In addition, sentiment analysis shows that positive news has a significant positive impact on the stock price of LianTech. Therefore, investors should pay attention to the company's financial performance and news reports to make informed decisions. Our research provides valuable insights for investors to make informed decisions.