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This study proposes a prediction model which functions by estimating the bandwidth of the Internet over the time period used for data transmission, that is the RTT (Round Trip Time) and PLR (Packet Loss Rate), which are the most important factors to consider for transmission rate control. The prediction model improves the number of valid transmitted packets by predicting the one-step-ahead transmission rate control factors. A method of prediction modeling was developed using a neural network, which makes it possible to model a nonlinear system and the LMBP algorithm was used to training the neural networks. RTT and PLR data was collected by the TFRC transmission method, which is a kind of adaptive transmission control based on UDP, and used as the training data for the neural network prediction model. Through the training of the neural network, the prediction model can predict the RTT and PLR after one step. It can also be seen that the error in the predicted values is small. This result shows that the congestion situation of the Internet can be predicted by the proposed prediction model. In addition, it shows that it is possible to implement a mechanism, which allows for a substantial amount of data to be transmitted, while actively coping with a congestion situation.