This Web App is going to classify the iris flowers for us using Artificial Neural Networks (ANN). The application of machine learning methods has become present in everyday life. From recommendations of which movies to watch, to which products to buy and recognizing your friends on social media, machine learning algorithms that learn from input/output pairs are called supervised learning algorithms because a “teacher” provides supervision to the algorithms in the form of the desired outputs for each example that they learn from. As creating a dataset of inputs and outputs is often a manual process, supervised learning algorithms are well understood and their performance is easy to measure. As stated before, we will be covering the Iris Species classification problem — a typical test case for many statistical classification techniques in machine learning.

Web App - https://iris-flower-classification-app.herokuapp.com/

Dataset:

• 150 Sample
• 3 Labels: Species of Iris (Iris setosa, Iris virginica and Iris versicolor)
• 4 Features: Sepal length, Sepal width, Petal length, Petal Width in cm.

Visualizing dataset

Plotting correlation matrix between dependent and independent features

Plotting correlation between dependent and independent features

After training the ANN model

Confusion matrix:

Plotting loss vs val_loss:

Plotting accuracy vs val_accuracy:

Link to GitHub Repository