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Anita is an artificial intelligence equity investment robot incubated by Sinochem, a subsidiary of Sinochem, which provides you with professional venture capital market consulting services through predictive analysis and data visualization. "She" can help you understand the industry trend and the development of the project, provide you with structured network wide investment and financing data, and assist you in making project due diligence and investment decisions. Through the systematic process of data collection, data storage, data analysis, manual verification and structural processing, the venture capital data of hundreds of indicators, thousands of data sources and nearly 200000 projects are processed, and the structured whole network investment and financing data is provided in the form of data visualization to assist the investors in making project adjustment and investment decisions. Through the structured analysis of the information of the target project, and through the quantitative investment model to track, analyze and score the attributes of the information, predict the development trend and investment of the project. The professional team established by the investor extracts project attributes based on mature investment experience and real project data, and sets evaluation indicators and standards for each attribute; the machine learning research team solves data positioning and extraction through the practical application of natural language processing technology, and then completes data diagnosis through machine learning combined with manual re inspection. Inertia model, financial model, sensitive model and other multiple models are combined to help investors realize quantitative investment. Anita's team is mainly composed of investment research team and machine learning team. They have worked together for nearly two years and gradually established a quantitative investment model in the field of venture capital. The professional team set up by the investors conducts in-depth research on the industry investment and combs and summarizes the typical cases, which lays the foundation for high-quality data; while the machine learning team adopts in-depth neural network, knowledge map, semantic analysis and other artificial intelligence technologies, so that it can actively obtain and update data, and extract useful information from it to add to the existing models and knowledge In the library of knowledge.

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