diff --git a/README.md b/README.md
index dcde1154e..2486da9d2 100644
--- a/README.md
+++ b/README.md
@@ -45,3 +45,4 @@ We welcome contributions of new models. They should be in a folder with a projec
   * [BitString Parity Challenge](other/bitstring-parity)
   * [MLP on housing data](other/housing/housing.jl) (low level API)
   * [FizzBuzz](other/fizzbuzz/fizzbuzz.jl)
+  * [Logistic Regression Iris](other/iris/iris.jl)
\ No newline at end of file
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diff --git a/other/iris/Project.toml b/other/iris/Project.toml
new file mode 100644
index 000000000..477b202d8
--- /dev/null
+++ b/other/iris/Project.toml
@@ -0,0 +1,3 @@
+[deps]
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diff --git a/other/iris/README.md b/other/iris/README.md
new file mode 100644
index 000000000..c9e5cc96d
--- /dev/null
+++ b/other/iris/README.md
@@ -0,0 +1,39 @@
+
+
+# Use Flux to do Logistic Regression on the Iris dataset
+
+This is a very simple model, with a single layer that outputs to softmax.
+
+Logistic regression can basically be thought of as a [single layer neural network](https://sebastianraschka.com/faq/docs/logisticregr-neuralnet.html).
+
+## Data Source
+
+The data source is Fisher's classic dataset, retrieved from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/iris).
+
+## Usage
+
+`cd` into `model-zoo/other/iris`, start the Julia REPL and instantiate the environment:
+
+```julia
+
+using Pkg; Pkg.activate("."); Pkg.instantiate()
+
+```
+
+Then train and evaluate the model:
+
+```julia
+julia> include("iris.jl")
+
+Accuracy: 0.92
+
+Confusion Matrix:
+
+3×3 Array{Int64,2}:
+ 16   0   0
+  0  15   2
+  0   2  15
+
+julia>
+
+```
diff --git a/other/iris/iris.jl b/other/iris/iris.jl
new file mode 100644
index 000000000..0d0da16ef
--- /dev/null
+++ b/other/iris/iris.jl
@@ -0,0 +1,67 @@
+
+
+using Flux
+using Flux: crossentropy, normalise, onecold, onehotbatch
+using Statistics: mean
+
+
+labels = Flux.Data.Iris.labels()
+features = Flux.Data.Iris.features()
+
+
+# Subract mean, divide by std dev for normed mean of 0 and std dev of 1.
+normed_features = normalise(features, dims=2)
+
+
+klasses = sort(unique(labels))
+onehot_labels = onehotbatch(labels, klasses)
+
+
+# Split into training and test sets, 2/3 for training, 1/3 for test.
+train_indices = [1:3:150 ; 2:3:150]
+
+X_train = normed_features[:, train_indices]
+y_train = onehot_labels[:, train_indices]
+
+X_test = normed_features[:, 3:3:150]
+y_test = onehot_labels[:, 3:3:150]
+
+
+# Declare model taking 4 features as inputs and outputting 3 probabiltiies, 
+# one for each species of iris.
+model = Chain(
+    Dense(4, 3),
+    softmax
+)
+
+loss(x, y) = crossentropy(model(x), y)
+
+# Gradient descent optimiser with learning rate 0.5.
+optimiser = Descent(0.5)
+
+
+# Start Training.
+for epoch in 1:100
+    Flux.train!(loss, params(model), [(X_train, y_train)], optimiser)
+end
+
+
+# Evaluate trained model against test set.
+accuracy(x, y) = mean(onecold(model(x)) .== onecold(y))
+
+accuracy_score = accuracy(X_test, y_test)
+
+println("\nAccuracy: $accuracy_score")
+
+# Sanity check.
+@assert accuracy_score > 0.8
+
+
+function confusion_matrix(X, y)
+    ŷ = onehotbatch(onecold(model(X)), 1:3)
+    y * ŷ'
+end
+
+println("\nConfusion Matrix:\n")
+display(confusion_matrix(X_test, y_test))
+