Neural Network and Data Preprocessing Concepts

The video tutorial covers the analysis of a fish market dataset, which is small, clean, and numerical. The instructor demonstrates loading the data, checking for missing values, and scaling it. Two models, logistic regression and a neural network, are set up and trained using sklearn and TensorFlow. The video concludes with evaluating the models' performance, showing the neural network achieving higher accuracy than logistic regression.

What is the primary characteristic of the fish market dataset discussed in the video?

• It is primarily text-based.

• It is a large and complex dataset.

• It contains a lot of missing values.

• It is small, simple, and clean.

How many rows are present in the fish market dataset?

• 250

• 160

• 500

• 1000

What is the first step in preparing the dataset for analysis?

• Checking for missing values

• Splitting the data into training and test sets

• Scaling the data

• Encoding the labels

Which library is used for scaling the features in the dataset?

• Pandas

• NumPy

• TensorFlow

• scikit-learn

What is the purpose of label encoding in the preprocessing step?

• To convert numerical data to text

• To handle missing values

• To convert categorical labels into numerical format

• To split the data into features and labels

What accuracy did the logistic regression model achieve on the test set?

• 81%

• 75%

• 85%

• 90%

Which activation function is used in the hidden layer of the neural network?

• ReLU

• Softmax

• Tanh

• Sigmoid

How many nodes are in the output layer of the neural network?

• 7

• 16

• 6

• 10

What was the final accuracy of the neural network model?

• 90%

• 81%

• 85%

• 75%

Which model performed better in terms of accuracy?

• Neural Network

• Neither performed well

• Logistic Regression

• Both performed equally