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random_forest_competition_01x04.jl
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### A Pluto.jl notebook ###
# v0.19.22
using Markdown
using InteractiveUtils
# ╔═╡ 9ddae550-b2fa-11ed-1cff-8bfff3026733
using DataFrames, CSV, MLJ, MLJDecisionTreeInterface
# ╔═╡ ef052fb0-69e3-45e6-aff0-e125ba7bf56c
begin
iowa_file_path = "data//home-data-for-ml-course//train.csv"
home_data = CSV.read(iowa_file_path, DataFrame)
y = home_data.SalePrice
feature_names = [:LotArea, :YearBuilt, Symbol("1stFlrSF"), Symbol("2ndFlrSF"), :FullBath, :BedroomAbvGr, :TotRmsAbvGrd]
X = home_data[:, feature_names]
end
# ╔═╡ e4993614-bae2-483d-abb8-66b448f64478
Forest = @load RandomForestRegressor pkg=DecisionTree verbosity=0
# ╔═╡ c17d8989-0719-4d29-bae4-9dbb34066bea
forest = Forest()
# ╔═╡ b7f9ac6f-7bbc-4d75-bffa-86aa0d6d122d
mach = machine(forest, X, y, scitype_check_level=0)
# ╔═╡ 4909ed18-577a-490b-aaa7-b2f9f73bef5e
fit!(mach)
# ╔═╡ d38382c6-eb80-4f88-a203-311d305981e8
test_data_path = "data//home-data-for-ml-course//test.csv"
# ╔═╡ f2301433-8465-499a-a9af-445c358c572c
test_data = CSV.read(test_data_path, DataFrame)
# ╔═╡ e9da55b8-2a45-46f3-8d80-f8382cfad87b
test_X = test_data[:,feature_names];
# ╔═╡ 3624bfdc-7183-4527-a7d5-cb0fe010a34b
test_preds = predict(mach, test_X)
# ╔═╡ eb78ab1c-ed0c-4395-8245-2702560f1520
output = DataFrame(:ID => test_data.Id,:SalePrice => test_preds)
# ╔═╡ e04a4aeb-4771-4b6d-840b-8e711f46a524
CSV.write("data//home-data-for-ml-course//submissions.csv", output)
# ╔═╡ b3d31de8-c3b7-4474-9876-56575ed3f898
ŷ = predict(mach, X)
# ╔═╡ 31fbad07-0f7b-4317-9c9a-9f5ecbc825b7
mean_absolute_error(ŷ, y)
# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
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MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7"
MLJDecisionTreeInterface = "c6f25543-311c-4c74-83dc-3ea6d1015661"
[compat]
CSV = "~0.10.9"
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MLJ = "~0.19.1"
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"""
# ╔═╡ 00000000-0000-0000-0000-000000000002
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"""
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