Data Science
Student Projects
Explore our students’ machine learning
projects that tackle real-world problems
House Price Prediction
Fiona Meisner
This project predicted the sales price of each house in a neighborhood in Ames, Iowa. I used XGBoost as my model because it has a highly efficient implementation, and I found it has a better performance than others. The most challenging piece of this project would be the data preparation because I felt like I had to keep going back and making changes repeatedly. I was able to place in the top 15% of submissions for this competition on Kaggle, however I would like to keep practicing more techniques for a better score!
Technologies Used
Python
Pandas
Sklearn
XGBoost
NLP Disaster Tweets
Belle Shen
The goal of this project was to use natural language processing (NLP) to monitor Twitter Tweets for signals of a natural disaster. I used a process called vectorization to change the words within the tweets into a numerical format that a machine learning algorithm can understand. For the model, I selected Multinomial Naive Bayes to predict wether the tweet contained disaster information or not with 96% accuracy! The most difficult aspect of this project was preprocessing all of the text data to prep it for the model.