Common Python Data Analysis Method Based on Deep Learning

Abstract

In recent years, with the continuous development and progress of machine learning technology, especially the successful application of deep learning algorithm in the field of image and big data processing, how to efficiently develop and apply machine learning technology has become a hot issue. This paper focuses on deep learning-based common Python data analysis methods. In this paper, a traffic flow prediction and analysis model based on convolutional neural network is established according to the traffic flow characteristics of the first ring expressway in this city. According to the characteristics of urban expressway traffic flow and the spatial correlation of traffic flow, the traffic flow data in the spatial and temporal dimension are combined in the form of two-dimensional matrix as the network input, and the CAFRE framework based on deep learning is used to design the expressway traffic flow prediction model based on convolutional neural network. The model takes into account the historical traffic flow of the predicted sections and the traffic flow of the upstream and downstream sections, and restricts the model structure by capturing the road range with high spatial correlation.