Abstract: A method for predicting sales order comprises the following steps: Step 1: obtaining information of multiple inquiry cases, and building inquiry original dataset based on the information of inquiry cases; step 2: randomly and with replacement, drawing m training samples from the inquiry original dataset as a training set; step 3: randomly selecting N features from the original dataset, training the selected features through the training set, and building a decision tree; step 4: repeating the steps 2 and 3 to build a total of Y decision trees to form a random forest model; step 5: importing the data to be predicted into the random forest model, and each decision tree votes on the imported data, and the probability of winning the sales order is determined based on the voting results. The invention can predict the probability of winning sales orders during the customer inquiry stage.

Abstract: A method and system for product processing price prediction based on multiple regression model includes: gathering multiple product data, building product original dataset, the data includes product quantity, surface area, processing complexity, product X axis length, product Y axis length, product Z axis length, tool utilization rate, product tolerance level, product machinability, material unit price, material density and price; building a multiple linear regression model based on the product original dataset; the product original dataset is divided into a training subset and a testing subset, the multiple linear regression model is trained through the training subset, the accuracy of the multiple linear regression model is verified by the testing subset, and the multiple linear regression model is adjusted according to the testing result to determine the final multiple linear regression model.