CSCI 4930/5930 Machine Learning Programming Assignment 1: Predicting Newborn’s Weight

Quote from the WebMD article [1]:

“A new baby's gender, name, time of birth, and birth weight are nice information for a birth

announcement, but birth weight is especially important for an obstetrician. A large size at delivery has

long been associated with an increased risk of injuries to a newborn and its mom. So the better a doctor

can predict birth weight, the easier the delivery may be.”

Ultrasound is a popular way of doing it. But, aha! In this assignment, You can amaze people by predicting

the birth weight way earlier than ultrasound using linear regression.

Dataset

baby-weights-dataset2.csv

It has 101400 rows (samples) with 37 columns (variables). Each sample represent a case

for a new-born. It contains 37 variables (just mentioned! Haha) about it. Very last

column of it is “BWEIGHT”, that true weight of the new-born (in lbs unit). Actually,

this needs to be considered as the target variable here.

data-description.txt

You will see that the name of the 37 variables are actually contracted form of some sort.

And, the source of the dataset did not offer me description of every single of them. But,

after studying about them, I could elaborate only few of them. Please pardon my

laziness. Okay, this file contains few descriptions for the variables. All the rest are

mostly talking about the Mother’s medical history and all. No big deal, I guess, for you

to work with these variables without knowing their meaning.

judge-without-label.csv

This is an interesting file. It contains new samples: additional 2001 rows with 36

columns (without the BWEIGHT target column). Once again, this should be part of the

training, as there are no ground truth target labels, right? Once the training is complete

with the dataset provided above, you must apply your prediction algorithm to predict

BWEIGHT of these 2001 samples, and submit the result as part of your assignment

submission.

Results

judge-submission-run-1.csv

judge-submission-run-2.csv

judge-submission-run-3.csv

Predicted BWEIGHT/result for each of the samples from the judge-without-label.csv file are saved

in the files above.

Tasks

Please read the LinearRegression.ipynb file using Jupyter Notebook to learn about 15

mandatory tasks, and 2 additional tasks for graduate students (CSCI-5930).