Train SimulatorTrain Simulator is a cool online railway locomotive driving game that simulates the work of train driver. Working as a train operator in 2017 is not as easy as it seems. You can experience it yourself with this incredible and free 3D train driving simulator.
In Train Simulator you will learn when to accelerate, when to brake and when to use your horn to take the train from one station to another without causing any accident. Earn money to buy new trains, but be careful, you may also have to pay some penalty money for driving through a street without honking your horn, for example. So you better pay attention to the signs and keep going on like a real professional. Enjoy playing Train Simulator, a free online game on Silvergames.com!Controls: Mouse.
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Predicting Horse Racing ResultsA few years ago I decided to take on a personal project to predict horse racing results. When I started, I didn’t really know exactly what the end goal of the project was suppose to be.
My primary objective was to have a journey to help become a better programmer and maybe learn a bit about machine learning as well. The result was.Over the years I’ve worked on several different machine learning models to predict the outcome of a horse race.
I've tried a variety of different flavours of classifiers, clustering engine and regression algorithms. Here I’ll be talking about one approach that I’ve taken. Hope you enjoy what I did and learn a few things along the way.Most of you probably know horse racing in the traditional. That is, jockey sitting on the horse that’s running along the track. Popular races like the Kentucky Derby, Preakness Stakes, and Belmont Stakes are thoroughbred races. I’ve grown up watching a different type of horse racing,.
These are a different type of horse that pull a sulky (two wheel cart) where the driver sits.In general both types of racing are very similar but there are a few key differences:. In thoroughbred racing, the horses start from a standstill whereas in harness racing the horses start from a running start. North American harness races all have the same distance, 1 mile, whereas thoroughbred races have many different lengths. (Note, harness race tracks are not all the same size, but the race is always 1 mile). Harness Racing horses don’t need as much time off between races as thoroughbred horses. Harness racing have two different running style.
You have pacers and trotters. If you are in a trotting race the driver always needs to keep his horse in a trot. If the horse breaks strides the driver must slow down until the horse is in last place before continuing to race.The algorithmThe model that we'll be creating will be using is a algorithm to train and predict results. Regression algorithm are nice for horse racing predictions. I used historical race data to create a set of features (which are listed below).
Features are a list of attributes (like which post the horse starts, the winning percentage of the horse, how good the driver is, etc.) that define the characteristics of a horse for a particular race. Using these features, you teach the algorithm the types of attributes a winning horse needs to have.When it comes to predictions, the algorithm can then estimate the position a horse will come in based on the same type of feature set. The goalThe goal the algorithm will try to answer is: How many times can I predict the winner of a horse race? A secondary goal is: How much money will I win or lose if I were to wager using the predictions made by the algorithm? The dataI’ve been gathering harness racing entries and results from across North America over the past 5 years. The data has come from public sources on the internet.For this test, we will be training our model on data from December 1st 2016 to February 28th 2017. Harness racing runs throughout the year and the weather affects the outcome of the race.
Rain, snow, wind and temperature all affect how the race will go. I have found that training the model on a shorter time frame will yield to better results. Training with data close to the date you want to predict will, in general, have similar weather patterns.The validation data that we will use to test the algorithm will be from March 1st to March 31st 2017. FeaturesOur model will be trained on 20 different features that I came up with.
Both the training and validation sets can be found in the github repo. #Row NameDescriptionARow KeyUnique Identifier for the row.