➕ Add link for MLJBalancing

README.md

@@ -76,7 +76,7 @@ Xover, yover = transform(mach, X, y)
 All implemented oversampling methods are considered static transforms and hence, no `fit` is required. 
 
 #### Pipelining Models
-If `MLJBalancing` is also used, an arbitrary number of resampling methods from `Imbalance.jl` can be wrapped with a classification model from `MLJ` to function as a unified model where resampling automatically takes place on given data before training the model (and is bypassed during prediction).
+If [MLJBalancing](https://github.com/JuliaAI/MLJBalancing.jl) is also used, an arbitrary number of resampling methods from `Imbalance.jl` can be wrapped with a classification model from `MLJ` to function as a unified model where resampling automatically takes place on given data before training the model (and is bypassed during prediction).
 
 ```julia
 using MLJBalancing
@@ -147,4 +147,4 @@ One obvious possible remedy is to weight the smaller sums so that a learning alg
 To our knowledge, there are no existing maintained Julia packages that implement resampling algorithms for multi-class classification problems or that handle both nominal and continuous features. This has served as a primary motivation for the creation of this package.
 
 ## 👥 Credits
-This package was created by [Essam Wisam](https://github.com/JuliaAI) as a Google Summer of Code project, under the mentorship of [Anthony Blaom](https://ablaom.github.io). Special thanks also go to [Rik Huijzer](https://github.com/rikhuijzer) for his friendliness and the binary `SMOTE` implementation in `Resample.jl`.
+This package was created by [Essam Wisam](https://github.com/JuliaAI) as a Google Summer of Code project, under the mentorship of [Anthony Blaom](https://ablaom.github.io). Special thanks also go to [Rik Huijzer](https://github.com/rikhuijzer) for his friendliness and the binary `SMOTE` implementation in `Resample.jl`.