In this project, I look to find the cheapest and best time for a ski trip in Colorado.

As a budget traveller, I like to keep the price of travel low. And usually I just optimize my travel dates based on airticket prices alone. I always wondered if I could save extra money if I also optimized on other variable costs like accommodation.

So here I collect data of flight prices, Airbnb listing prices, weather patterns for snow conditions and other data to plan the best budget ski-trip. Here is the combined figure showing breakdown of various variable costs for a 5-day ski trip vacation in Colorado.

1. The cheapest time to travel is 2nd February 2019 from Madison to Colorado with a total variable cost of $513.
2. On an average, I save about $221.83 dollars on variable cots with this optimization. This effort was worth it.
3. Compared to optimizing only based on flight prices, I save an extra $90.75 using my approach.

1. Flight price data collect, clean and explore
2. Airbnb data clean and explore
3. Weather data clean and explore
4. Secondary costs (Vacation day and peak time travel cost)
5. Combined cost analysis

While working on this project, I got interested in the Airbnb listing data. I was wondering if I could predict the prices of Airbnb listing using the listing feature. So I do sub-project where I explore various

My best model is based on xgboost regression and it achieved a median absolute percentage error of 20.85% or median absolute error of $20.12.

Airbnb listing price prediction

I used the following version of libraries in Python 3 for my model

• Pandas 0.23.4
• Numpy 1.15.1
• Scipy 1.1.0
• scikit-learn 0.19.2
• xgboost 0.81
• Seaborn 0.9.0
• matplotlib 2.2.3
• BeautifulSoup
• urllib
• time
• os
• warnings