Applying Machine learning models to Diabetes dataset using MLJ.jl packages

**Hello, **
Please I have this diabetes dataset which i have cleaned using Altery designer, and I would love to build ML models(Random Forest tree, Logistic regression, KNN, Support Vector Machine and Naive Baye algorithms on it.). I am new to Julia and the MLJ package. Please how can I go about to build my models(and evaluate its accuracy and performance)
-Showing me how to do it with just one or two chosen algorithms will go a long way to help please.

using Pkg
Pkg.add("CSV")
Pkg.add("DataFrames")
using DataFrames
using CSV
diabetes= CSV.read("diabetes.csv", DataFrame)
using Pkg
using Pkg; Pkg.activate("my_MLJ_env", shared=true)
Pkg.add("MLJ")
using MLJ
models()
Pkg.add("MLJDecisionTreeInterface")
diabetes_x= Matrix(diabetes[:,[1,2,3,4,5,6,7,8]]);
diabetes_y=diabetes[:, 9];
tree_model = @load RandomForestClassifier pkg=DecisionTree

Please how do I apply that to my dataset? I would have loved to include the dataset

Did you look at the MLJ tutorials here? Data Science Tutorials in Julia

They are pretty detailed and include a bunch of end to end examples that go from data cleaning via model selection and training to validation

Thank you very much for your reply. I have read through the documentation(including what you sent). But i still have some issues occurring within my code:
For example:

using Pkg
Pkg.add("MLJLinearModels")
@load LogisticClassifier pkg=MLJLinearModels
X2 = select(diabetes_x)
clf = machine(LogisticClassifier(), X2, diabetes_y)

When I run the above code i get the error:

UndefVarError: LogisticClassifier not defined