I came across an interesting dataset recently. The dataset has 308 items. Each item has seven numbers that correspond to a boat hull shape. The first six numbers are predictor variables and the seventh number is a measure of resistance. The idea is to predict resistance from the first six values.

The data looks like:

-2.3 0.568 4.78 3.99 3.17 0.125 0.11
-2.3 0.568 4.78 3.99 3.17 0.150 0.27
. . .
-5.0 0.600 4.78 4.24 3.15 0.125 0.06
. . .

The predictor variables are:

1. Longitudinal position of the center of buoyancy.

2. Prismatic coefficient.

3. Length-displacement ratio.

4. Beam-draught ratio.

5. Length-beam ratio.

6. Froude number.

Just for kicks, on my lunch break, I decided to take a crack at creating a deep neural regression model. I used the CNTK library. My first step was to normalize all the data. I used min-max normalization.

I was surprised at how fast I got a prediction model up and running — about 15 minutes. But in retrospect, I had all the CNTK code done from previous experiments, so all I really had to do was spend some time adjusting the neural network hyperparameters. In the image below, I used two hidden layers of 5 nodes each, tanh activation, a fixed learning rate of 0.005, and the Adam optimization algorithm.

In regression problems, where the goal is to predict a numeric value (resistance in this case), you have to define what it means for a predicted value to be correct. In my little demo, the program counts a prediction correct if it is within 20% of the true value. With that criterion, my model got 74.66% accuracy (230 correct, 78 wrong).

Good fun!

*The USS Sequoia yacht was used by U.S. presidents Herbert Hoover, Franklin Roosevelt, Dwight Eisenhower, John F. Kennedy, Richard Nixon, and Gerald Ford.*

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