Dataset/Python HW

Implement and train a classification model for the Titantic dataset (for dataset you
may find it here: https://www.kaggle.com/c/titanic). Please ignore the test set (i.e.,
test.csv) and consider the given train set (i.e., train.csv) as the dataset (which you will
use in this HW).

What you need to do:
• Data cleansing
• Split the dataset (i.e., train.csv) to a training set (80% samples) and a testing set
(20% samples)
• Train your model (see details below)
• Report the overall classification accuracies on the training and testing sets
• Report the precision, recall, and F-measure scores on the testing set

1. Required Model (100 pts) Implement and train a logistic regression as your classification model.
• You have to use Sklearn deep learning library.
• You may want to refer this tutorial: https://bit.ly/37anOxi.

2. Bonus Model (100 pts) Implement and train a neural network as your classification
model.
• You have to use PyTorch deep learning library.
• Two hidden layers: the first hidden layer must contain 5 units using ReLU
activation function; the second layer must contain 3 units using tanh activation
function.

What need to include in your submission:
• Your Python code
• Screenshots of your Python code
• Screenshots of your outputs
• Your conclusion (written in PDF file) of the evaluation performance on the testing dataset

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