LARA FARES

Intro

I love learning how things work. When I was a small child, I would point at everything and demand to know what it was and what made it function. My inquisitive nature has been a defining aspect of my life ever since.

I studied Mathematics in college, but found myself fascinated by data because of its ability to inform, generate insights, and even lead to new discoveries. In 2021, I completed a data science immersive course through General Assembly as a way to build my data science skill set beyond what I had previously learned through on-the-job experience and self-teaching. The statistical methods and modeling techniques I learned are highly applicable to the projects I will work on throughout my career. Equally valuable, though, is my approach to solving problems, which begins with the same question I asked as a child: "How does it work?"

House Price Prediction

For this project, I created a regression model with the Ames Housing Dataset to see how well can we predict the price of a house, given it's characteristics and other important properties. I cleaned and analyzed 79 features which described (almost) every aspect of residential homes in Ames, Iowa.

Our model achieved an R squared of 97.6% on training data and 94.3% on unseen data. This means two things: 94.3% of the variability in the data is explained by our model and our model is overfit. In the future I would try improve the model by:

• Revisiting the data cleaning and imputation process
• Filtering the features to only the ones that are highly correlated with Sale Price
• Feature engineering new features and checking their correlation with the Sale Price

Github Repo

• GitHub

Wildfire Smoke Detection

Wildfires pose a threat to nature and human health especially in the age of global warming. These fires are getting bigger and more destructive than ever. Their early detection is key to effectively fighting it, because once a wildfire reaches a certain size it can be difficult to control.

For this project, I built a wildfire smoke object detector using wildfire smoke images captured by HPWREN cameras. After training the model with thousands of annotated images, the model was able to correctly detect smoke in 80% of the images. In the future, I would try to improve the models performance by collecting more images from different altitudes and angles.

Github Repo

• GitHub

Resume

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Contact

My LinkedIn

• LinkedIn

Elements

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