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missing_values_02x02.jl
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missing_values_02x02.jl
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### A Pluto.jl notebook ###
# v0.19.26
using Markdown
using InteractiveUtils
# ╔═╡ 862f68ae-b4ce-11ed-2f35-b706fd2c4574
using DataFrames, CSV, MLJ, MLJDecisionTreeInterface
# ╔═╡ 9d6d84c8-2718-42ea-a15e-ca1e2e4410b9
X_full = CSV.read("data//home-data-for-ml-course//train.csv", DataFrame);
# ╔═╡ af332b2d-e5df-4f8c-9099-cea12df949e0
X_test_full = CSV.read("data//home-data-for-ml-course//test.csv", DataFrame);
# ╔═╡ ac68a399-9070-48ba-953f-6b9052a88bff
describe(X_full);
# ╔═╡ e0f32a33-d78a-480b-8cca-ea9c06b106e7
#subset!(X_full, :SalePrice => ByRow(!=("missing")))
# filter SalePrice by row in place
# ╔═╡ 8dedba60-7f89-47b9-bc74-7ae512ff9b6a
dropmissing!(X_full, Cols(:SalePrice));
# ╔═╡ 084afa6a-f2d1-49a1-8c0c-becc33a21c95
y = X_full.SalePrice;
# ╔═╡ 00f6bb2e-edbd-4f1a-aa56-1e621b7ec88a
md"""
## Fixing data type
### convert String type to *Int type and missing*
"""
# ╔═╡ c4b828ef-4ad1-4f71-8248-11eca5e8db89
X_full.LotFrontage = something.(tryparse.(Int, X_full.LotFrontage), missing);
# ╔═╡ 99bb9cac-6bd6-4538-8572-a591813ac5a9
X_full.MasVnrArea = something.(tryparse.(Int, X_full.MasVnrArea), missing);
# ╔═╡ d19a9871-6ddd-4efb-8b13-5c63c0582793
X_full.GarageYrBlt = something.(tryparse.(Int, X_full.GarageYrBlt), missing);
# ╔═╡ dbe4da80-9cd6-4485-8b20-808f16591fcb
select!(X_full, Not(:SalePrice)); # drop column SalePrice in Place
# ╔═╡ 457a12fd-65cc-4c3d-b681-c3d7f96f981a
size(X_full)
# ╔═╡ 666567a7-4332-4e1a-95cd-d148c79cadbc
X = select(X_full, Cols(x -> eltype(X_full[:, x]) <: Union{Number,Missing})); #
# ╔═╡ 33a1687e-0e33-4395-91b3-3f3e6b0f156a
X_full
# ╔═╡ aad9c4fd-c2b5-4fc6-a5ff-a5f14f30fbd9
size(X)
# ╔═╡ 2c46c5ca-be1c-4bd1-a330-e8cc3b3ecf5b
begin
X_test_full.LotFrontage = something.(tryparse.(Int, X_test_full.LotFrontage), missing);
X_test_full.MasVnrArea = something.(tryparse.(Int, X_test_full.MasVnrArea), missing);
X_test_full.GarageYrBlt = something.(tryparse.(Int, X_test_full.GarageYrBlt), missing);
X_test_full.BsmtFinSF1 = something.(tryparse.(Int, X_test_full.BsmtFinSF1), missing);
X_test_full.BsmtFinSF2 = something.(tryparse.(Int, X_test_full.BsmtFinSF2), missing);
X_test_full.BsmtUnfSF = something.(tryparse.(Int, X_test_full.BsmtUnfSF), missing);
X_test_full.TotalBsmtSF = something.(tryparse.(Int, X_test_full.TotalBsmtSF), missing);
X_test_full.GarageCars = something.(tryparse.(Int, X_test_full.GarageCars), missing);
X_test_full.GarageArea = something.(tryparse.(Int, X_test_full.GarageArea), missing);
X_test_full.BsmtFullBath = something.(tryparse.(Int, X_test_full.BsmtFullBath), missing);
X_test_full.BsmtHalfBath = something.(tryparse.(Int, X_test_full.BsmtHalfBath), missing);
end
# ╔═╡ 232b7e6a-0697-45bf-90ca-337839fa5728
X_test_full
# ╔═╡ fc7fe2c9-dae1-46e1-aaa9-cc72e8607c9d
size(X_test_full)
# ╔═╡ 56016aea-7244-4945-bdea-315d9c98548b
X_test = select(X_test_full, Cols(x -> eltype(X_test_full[:, x]) <: Union{Number, Missing}));
# ╔═╡ 0a83bb74-ba5e-4a17-b6ad-dd901011b54a
size(X_test)
# ╔═╡ dbfd72f1-3022-4a8a-9b69-a314d8fef6e9
(X_train, X_valid), (y_train, y_valid) = partition((X, y), 0.8, multi=true, rng=0);
# ╔═╡ d6fcc16d-be9c-4ea7-9e10-4df65d3111c9
md"""
## Preliminary Investigation
"""
# ╔═╡ 42bc55e8-d002-43db-ab26-c1ac33b9aaa6
begin
println("(nrows, ncols)")
@show size(X_train);
end
# ╔═╡ 1f84e261-b99e-4657-ba2b-f5ba2e882e77
describe(X_train, :nmissing)
# ╔═╡ 99f2160d-b4e8-49d7-a32d-5ccbbcbe5f80
# Function for comparing different approaches
function score_dataset(X_train, X_valid, y_train, y_valid)
Forest = @load RandomForestRegressor pkg=DecisionTree verbosity=0
model = Forest(n_trees=100, rng=0)
mach = machine(model, X_train, y_train, scitype_check_level=0)
fit!(mach, verbosity=0)
preds = predict(mach, X_valid)
return mean_absolute_error(preds, y_valid)
end
# ╔═╡ 51412838-9222-4d96-b785-5da995a0d29e
cols_with_missing = names(X_train, any.(ismissing, eachcol(X_train))) # pick columns that contain missing values
# ╔═╡ 808ce094-a94f-4a46-9ae7-644fef7402c5
reduced_X_train = select(X_train, Not(cols_with_missing));
# ╔═╡ 52e494e6-29ff-4b15-94e2-7e1ab8f1cd8c
reduced_X_valid = select(X_valid, Not(cols_with_missing));
# ╔═╡ 5b7e3df8-498c-4fad-b68d-315863dd62fe
begin
println("MAE (Drop columns with missing values):")
println(score_dataset(reduced_X_train, reduced_X_valid, y_train, y_valid))
end
# ╔═╡ ef94c803-cfea-4ad1-89f7-4600111b8cb6
my_imputer = FillImputer()
# ╔═╡ 0da72a72-ee11-4532-ae93-0b298d91eeed
mach = machine(my_imputer, X_train) |> fit!
# ╔═╡ 14fe3ec4-3a06-42d7-9d10-a41f2707f7c9
imputed_X_train = MLJ.transform(mach, X_train);
# ╔═╡ a3a748fe-2e01-4538-8d00-3ad911bcc0c8
imputed_X_valid = MLJ.transform(mach, X_valid);
# ╔═╡ bb97b4d4-ce9e-4b0b-9911-2e3ef85691f4
begin
println("MAE (Imputation):")
println(score_dataset(imputed_X_train, imputed_X_valid, y_train, y_valid))
end
# ╔═╡ 841d97f9-16ce-45d1-8b75-e5feeea3e0ce
md"""
## Part A
"""
# ╔═╡ b51fd4e7-2db7-4555-8a85-c7aab81fdd00
begin
final_imputer = FillImputer()
impute_mach = machine(final_imputer, X_train) |> fit!
final_X_train = MLJ.transform(impute_mach, X_train)
final_X_valid = MLJ.transform(impute_mach, X_valid)
end;
# ╔═╡ c5db0058-3f08-4962-b9c5-ee5c2d7b35f0
begin
Forest = @load RandomForestRegressor pkg=DecisionTree verbosity=0
forest = Forest(n_trees=100, rng=0)
final_mach = machine(forest, final_X_train, y_train, scitype_check_level=0) |> fit!
# Get validation predictions and MAE
preds_valid = predict(final_mach, final_X_valid)
println("MAE (Your approach):")
println(mean_absolute_error(preds_valid, y_valid))
end
# ╔═╡ f76b9828-168d-4761-b21d-9f7b6091ddac
md"""
## Part B
"""
# ╔═╡ aa9ed3ed-a150-46bf-ab07-d58e7af36bab
final_X_test = MLJ.transform(impute_mach, X_test)
# ╔═╡ 7436201b-dfd6-47a3-8358-7ced2eb83f1d
# Fill in the line below: get test predictions
preds_test = predict(final_mach, final_X_test)
# ╔═╡ 55ca2be3-2669-42fa-b33d-024a711c92b1
begin
output = DataFrame("Id" => X_test.Id,
"SalePrice" => preds_test)
CSV.write("data//home-data-for-ml-course//submissions_02x02.csv", output)
end
# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
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MLJDecisionTreeInterface = "c6f25543-311c-4c74-83dc-3ea6d1015661"
[compat]
CSV = "~0.10.11"
DataFrames = "~1.5.0"
MLJ = "~0.19.2"
MLJDecisionTreeInterface = "~0.4.0"
"""
# ╔═╡ 00000000-0000-0000-0000-000000000002
PLUTO_MANIFEST_TOML_CONTENTS = """
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