Initial Airfoil

Author: ashryaagr

_layout/head.html

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+      <li class="pure-menu-item "><a href="/end-to-end/airfoil" class="pure-menu-link"><span style="padding-right:0.5rem;">•</span> Airfoil </a></li>
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_literate/EX-airfoil.jl

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+# ## Getting started
+#
+# ### Loading and  preparing the data
+
+using MLJ, PrettyPrinting, DataFrames, Statistics, CSV
+using PyPlot, HTTP
+
+MLJ.color_off() # hide
+
+req = HTTP.get("https://raw.githubusercontent.com/rupakc/UCI-Data-Analysis/master/Airfoil%20Dataset/airfoil_self_noise.dat");
+
+df = CSV.read(req.body, header=[
+                  "Frequency","Attack_Angle","Chord+Length","Free_Velocity","Suction_Side","Scaled_Sound"
+              ]
+       );
+df[1:5, :] |> pretty
+
+# We load the RandomForestRegressor from ScikitLearn
+rfr = @load RandomForestRegressor pkg=ScikitLearn
+
+#-unpack into the data and labels
+y, X = unpack(df, ==(:Scaled_Sound), col -> true);
+
+# Partition into train and test set
+train, test = partition(eachindex(y), 0.7);
+
+rfr = RandomForestRegressor();
+
+rfr_m = machine(rfr, X, y);
+
+# train on the rows corresponding to train
+fit!(rfr_m, rows=train);
+
+# predict values on the rows corresponding to test
+pred = MLJ.predict(rfr_m, rows=test);
+pred[1:5]

end-to-end/airfoil.md

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+@def hascode = true
+@def showall = true
+
+# Airfoil
+
+\tutorial{EX-airfoil}