Question

The goal of this question is to give you experience in implementation of ANN and analyze the problem of overfitting and underfitting based on the different structures of ANN.

Data sets:
The dataset that you use in this project is "pima-indians-diabetes". Please find the data set on
https : // gist. github. com / chaityacshah / 899a95deaf8b1930003ae93944fd17d7
This dataset describes the medical records for Pima Indians and whether or not each patient will have an onset of diabetes within year.
Fields description follow:
preg = Number of times pregnant
plas = Plasma glucose concentration a 2 hours in an oral glucose tolerance test
pres = Diastolic blood pressure (mm Hg)
skin = Triceps skin fold thickness (mm)
test = 2-Hour serum insulin (mu U/ml)
mass = Body mass index (weight in kg/(height in m)^2)
pedi = Diabetes pedigree function
age = Age (years)
class = Class variable (1:tested positive for diabetes, 0: tested negative for diabetes)
Please write python code for the following question using dataset link attached:
Question: Implementing ANN with 1- input layer with 8 neurons, 1 hidden layer with 5 neurons and 1-output layer.

Answer 1

The question involves creating an ANN for the Pima Indians Diabetes dataset with specific architecture requirements using Python. The provided template code outlines the basic structure of the ANN with Keras, including the input, hidden, and output layers.

Step-by-step explanation:

The student is asking for assistance in implementing an Artificial Neural Network (ANN) using the Python programming language, specifically for a binary classification problem found in the Pima Indians Diabetes dataset. This implementation should consist of an ANN with 1 input layer containing 8 neurons, 1 hidden layer with 5 neurons, and 1 output layer. The focus is on understanding and addressing potential issues of overfitting and underfitting in the model design. Here is a simple example of how this could be implemented using Keras:

from keras.models import Sequential
from keras.layers import Dense

# Load and prepare your data
# X, y = preprocess(pima-indians-diabetes dataset)

# Define the model
model = Sequential()
model.add(Dense(8, input_dim=8, activation='relu')) # Input layer with 8 neurons
model.add(Dense(5, activation='relu')) # Hidden layer with 5 neurons
model.add(Dense(1, activation='sigmoid')) # Output layer

# Compile the model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

# Fit the model on the data
model.fit(X, y, epochs=150, batch_size=10)

# Evaluate the model
_, accuracy = model.evaluate(X, y)
print(f'Accuracy: {accuracy * 100}%')

Note that the above code is a template, and the actual implementation requires loading the data, preprocessing it, fitting the model on the training data, and then evaluating its performance. It's essential to split the dataset into training and test sets to evaluate the model for overfitting or underfitting.