A neural network built with the help of Keras to perform classification on Iris dataset.
Iris dataset is one of the classical datasets to perform classification. The problem is to classify the Iris flower into its 3 categories: Setosa, Versicolor, Virginica
The parameters given in the dataset are: Sepal Length, Sepal Width, Petal Length, Petal Width
Train Data- 120 rows
Test Data- 30 rows
No. of Layers-5
Types of Layers- Dense Layers, Dropout Layers
Activation Functions used- ReLU, tanh, softmax
Loss Function- Categorical CrossEntropy
No. of Epochs-10
The predictions performed on the test dataset have an average accuracy of 96%.