NAME

RAMINENI,PARDHA SARADHI

CLASS ID

38

Programming elements:

Keras Basics

Source Code:

https://umkc.box.com/s/3cvfiwc81lhgygc67deyeqs8m858lld0

IN CLASS PROGRAMMING

1.Use the use case in the class:

a.Add more Dense layers to the existing code and check how the accuracy changes.

2.Change the data source to Breast Cancer dataset * available in the source folder and make required changes

3.Normalize the data before feeding the data to the model and check how the normalization change your accuracy (code given below).fromsklearn.preprocessing importStandardScalersc =StandardScaler()

ANSWER))

For task 1, we added few more hidden layers to the given source code and observed the accuracy

With addition of hidden layers the accuracy increased from 68% to 71.8%.

For task2 I implemented the model using the Breast Cancer dataset where we need to predict the diagnosis category either M or B.We performed the label encoding on the target value and trained the model.

**For task3 we implemented the Normalization of data before training the model using the StandardScaler.

The accuracy increased from 90.9% to 95.8%.**