Before starting this #66daysofdata journey, I built a web scraper to extract award flight prices from NYC to Madrid. It got me interested in thinking about what next steps I could do with flight pricing data and led to me thinking about ways to explore predictive modeling. I found a great video online by Krish …
[DAY 3/4] Predicting flight prices with Random forest Read More »
On Day 1 of this #66DaysOfData, I used OneHot Encoding on locations on my housing prices data set. This pivots out each location into a separate column and uses a 1 or 0 to designate if it was True or False. One of the things that came up in the tutorial was the issue of …
I’ve been feeling a bit stagnant in my data skills lately, so I decided to take on the #66DaysofData challenge to improve. And to start off, I picked a pretty cool project: predicting housing prices in Bangalore using a Kaggle dataset. I found this awesome tutorial by CodeBasics that walks you through the whole process, step-by-step. I …
[Day 1] #66DaysofData: Bangalore Housing Price Prediction Read More »
WHAT MADE ME DECIDE TO WEBSCRAPE AWARD FLIGHTS As frequent travelers, my wife and I are always looking for ways to save money on flights. One strategy we’ve stumbled upon is to take advantage of credit card points and fly using award flights. However, we quickly realized,like so many others, that finding award flight availability …
Unlocking Award Flight Secrets with Selenium Web Scraping Read More »