notebook_02x01.jl

### A Pluto.jl notebook ###
# v0.19.22

using Markdown
using InteractiveUtils

# ╔═╡ 59a02c80-b348-11ed-3b60-e91278db6bbc
using DataFrames, CSV, MLJ, MLJDecisionTreeInterface

# ╔═╡ 690b29b0-65ba-4059-9b44-dc3f0d51c6e4
X_full = CSV.read("data//home-data-for-ml-course//train.csv", DataFrame);

# ╔═╡ a43d5769-edb3-4a0b-ac12-42e6f799329c
X_test_full = CSV.read("data//home-data-for-ml-course//test.csv", DataFrame);

# ╔═╡ 76f316e3-0834-4524-afe3-1c5eab71b979
y = X_full.SalePrice;

# ╔═╡ 1bc4d347-fc7d-4ef1-a4e8-9968203f02c6
features = [:LotArea, :YearBuilt, Symbol("1stFlrSF"), Symbol("2ndFlrSF"), :FullBath, :BedroomAbvGr, :TotRmsAbvGrd];

# ╔═╡ 2260a3ce-3cc4-4654-9bba-513bbf48c9b9
X = copy(X_full[:,features]);

# ╔═╡ bb8a2fad-4591-4c8b-88a8-d30144f83274
X_test = copy(X_test_full[:,features]);

# ╔═╡ 8f2fd439-e093-4ba7-9d65-eba33b47e546
(X_train, X_valid), (y_train, y_valid) = partition((X, y), 0.8, multi=true)

# ╔═╡ d1dbf656-7ab4-4663-82bd-75569b886dc8
Forest = @load RandomForestRegressor pkg=DecisionTree verbosity = 0

# ╔═╡ 75b721f5-fc13-4890-adc4-e106ece39349
model_1 = Forest(n_trees=50, rng=0);

# ╔═╡ d51ef33f-e119-4b06-997b-5167536bbb60
model_2 = Forest(n_trees=100, rng=0);

# ╔═╡ 2235dbb7-80e8-41a0-b1a1-1ef48d7ae101
model_3 = Forest(n_trees=100, feature_importance= :split, rng=0);

# ╔═╡ 1dc6319e-fa49-45ec-9907-24f473a9976a
model_4 = Forest(n_trees=200, min_samples_split=20, rng=0);

# ╔═╡ 72ad5e92-8125-42ae-b094-9ab1da2ccbab
model_5 = Forest(n_trees=100, max_depth=7, rng=0); 

# ╔═╡ 00e953d0-5fdb-4b12-86f8-bd4763026072
models = [model_1, model_2, model_3, model_4, model_5];

# ╔═╡ 780a80e4-5a7b-464c-9ef3-4d0d051af341
function score_model(model; X_t = X_train, X_v = X_valid, y_t = y_train, y_v = y_valid)
	mach = machine(model, X_t, y_t, scitype_check_level=0)
	fit!(mach, verbosity=0)
	preds = predict(mach, X_v)
	return mean_absolute_error(preds, y_v)
end

# ╔═╡ 85777a92-b290-4439-aa32-586bab979f5c
for i in 1:length(models)
	mae = score_model(models[i])
	println("Model $(i) MAE: $(mae)")
end

# ╔═╡ 4056e3f0-1d15-4ef0-ba0d-5fe5623188e7
best_model = model_3;

# ╔═╡ e44c2c32-35c1-42a1-9256-9078d59dd221
my_model = machine(best_model, X, y, scitype_check_level=0);

# ╔═╡ e68d4534-5564-426a-bd6c-87c55e7eeca1
fit!(my_model)

# ╔═╡ 07caef1c-f476-40ad-a0f7-8ddc97745962
preds_test = predict(my_model, X_test)

# ╔═╡ f9d6564b-ff4e-47ce-af89-2260b0f119bb
output = DataFrame("Id" => X_test_full.Id,
                       "SalePrice" => preds_test)

# ╔═╡ d8d8cc96-716d-4467-8642-f3fdb7dfbd99
CSV.write("data//home-data-for-ml-course//submissions_02x01.csv", output)

# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
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[compat]
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"""

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"""

# ╔═╡ Cell order:
# ╠═59a02c80-b348-11ed-3b60-e91278db6bbc
# ╠═690b29b0-65ba-4059-9b44-dc3f0d51c6e4
# ╠═a43d5769-edb3-4a0b-ac12-42e6f799329c
# ╠═76f316e3-0834-4524-afe3-1c5eab71b979
# ╠═1bc4d347-fc7d-4ef1-a4e8-9968203f02c6
# ╠═2260a3ce-3cc4-4654-9bba-513bbf48c9b9
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