Tools: Python (Pandas, numpy, seaborn, matplotlib, folium, json, math, searchengine, sklearn), Tableau. Link to full project

Challenge: A car company has retooled to become an RV manufacturer, launching a new kind of camper under an original brand. They need descriptive insights and a predictive model for behavioral trends by those who already own and use RVs.

Cleaned and transformed dataset for all 2022 reservations from recreation.gov to retrieve information on 1.7 million RV users. Using Python libraries, calculated new variables like actual distance traveled, tested for stationarity and autocorrelation, and ran supervised and unsupervised machine learning (linear regression and k-means clustering) to describe distinct user subsets and overall usage trends. Used highly interactive visualizations to allow stakeholders levels of granularity, and thus shape the necessary subsetting and threshhold changes for model improvements.