You may have noticed that the sorting of date is descending – from 12.2017 to 01.2000. So we generate three new columns with values from the last 3 months. Let’s assume that there are some dependencies between the amount of traffic accidents last month and in this month. It is clear, that our output will be a forecast of the amount of traffic accidents. We train our model, by pairing the input with an expected output. In this part we predict the amount of road accidents due to alcohol consume.įirst of all, we separate must our data into two parts, test and training. 13 Zoomed scatter chart alcohol caused traffic accidents from 2000 to 2002 Time series prediction model With a filter-operator we define that just alcohol accidents are relevant.įig. If we want to analyze just the cases with alcohol involvement, we should filter out the rows and attributes which are not necessary for our analysis. This information may be useful especially for the police or other legislative institutions.įirst of all, import the data with “Retrieve” operator. Then predict how many traffic accidents will happen in the future. But we can try to analyze the amount of traffic accidents caused by alcohol before and after.
Unfortunately, we have no data from 19 to analyze the effectiveness of the first decision. In this Part we will build a time-series forecasting model based on an neural network with RapidMiner.
In Part 1: Introduction to RapidMiner we became acquainted with the functions and capabilities of RapidMiner. Was this ruling correct and efficient? And when yes, how effective was it?
This was the first time alcohol consumption from a driver was covered by a law. On 14 June 1973, the German Bundestag reduced this limit to 0.8 per mil and from the limit was again lowered to 0.5 per mil. In Germany the federal court in 1953 established a drink-drive limit of 1.5 per mil.