##### Beginning of file

# This file was generated by PredictMD version 0.26.0
# For help, please visit https://predictmd.net

import PredictMD

### Begin project-specific settings

PredictMD.require_julia_version("v1.1.0")

PredictMD.require_predictmd_version("0.26.0")

# PredictMD.require_predictmd_version("0.26.0", "0.27.0-")

PROJECT_OUTPUT_DIRECTORY = PredictMD.project_directory(
    homedir(),
    "Desktop",
    "boston_housing_example",
    )



### End project-specific settings

### Begin model output code

import PredictMDFull

import LinearAlgebra
import Random
import Statistics
try Pkg.add("GLM") catch end
try Pkg.add("Distributions") catch end
try Pkg.add("StatsModels") catch end
import GLM
import Distributions
import StatsModels

import Pkg
try Pkg.add("StatsBase") catch end
import StatsBase

Random.seed!(999)

trainingandtuning_features_df_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "trainingandtuning_features_df.csv",
    )
trainingandtuning_labels_df_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "trainingandtuning_labels_df.csv",
    )
testing_features_df_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "testing_features_df.csv",
    )
testing_labels_df_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "testing_labels_df.csv",
    )
training_features_df_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "training_features_df.csv",
    )
training_labels_df_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "training_labels_df.csv",
    )
tuning_features_df_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "tuning_features_df.csv",
    )
tuning_labels_df_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "tuning_labels_df.csv",
    )
trainingandtuning_features_df = CSV.read(
    trainingandtuning_features_df_filename,
    DataFrames.DataFrame;
    rows_for_type_detect = 100,
    )
trainingandtuning_labels_df = CSV.read(
    trainingandtuning_labels_df_filename,
    DataFrames.DataFrame;
    rows_for_type_detect = 100,
    )
testing_features_df = CSV.read(
    testing_features_df_filename,
    DataFrames.DataFrame;
    rows_for_type_detect = 100,
    )
testing_labels_df = CSV.read(
    testing_labels_df_filename,
    DataFrames.DataFrame;
    rows_for_type_detect = 100,
    )
training_features_df = CSV.read(
    training_features_df_filename,
    DataFrames.DataFrame;
    rows_for_type_detect = 100,
    )
training_labels_df = CSV.read(
    training_labels_df_filename,
    DataFrames.DataFrame;
    rows_for_type_detect = 100,
    )
tuning_features_df = CSV.read(
    tuning_features_df_filename,
    DataFrames.DataFrame;
    rows_for_type_detect = 100,
    )
tuning_labels_df = CSV.read(
    tuning_labels_df_filename,
    DataFrames.DataFrame;
    rows_for_type_detect = 100,
    )

linear_regression_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "linear_regression.jld2",
    )
random_forest_regression_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "random_forest_regression.jld2",
    )
knet_mlp_regression_filename = joinpath(
    PROJECT_OUTPUT_DIRECTORY,
    "knet_mlp_regression.jld2",
    )

linear_regression =
    PredictMD.load_model(linear_regression_filename)
random_forest_regression =
    PredictMD.load_model(random_forest_regression_filename)
knet_mlp_regression =
    PredictMD.load_model(knet_mlp_regression_filename)
PredictMD.parse_functions!(knet_mlp_regression)

PredictMD.predict(linear_regression,training_features_df,)
PredictMD.predict(random_forest_regression,training_features_df,)
PredictMD.predict(knet_mlp_regression,training_features_df,)

PredictMD.predict(linear_regression,testing_features_df,)
PredictMD.predict(random_forest_regression,testing_features_df,)
PredictMD.predict(knet_mlp_regression,testing_features_df,)

### End model output code



##### End of file

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