Taxi price analysis

Introduction

I’ve always been fascinated by how data can reveal hidden patterns in everyday life. Like many urban dwellers, I often take taxis to save time—working on my laptop in the back seat instead of zoning out on public transport. But I couldn’t help noticing how wildly the prices fluctuated throughout the day!

For my daily commuting needs, I regularly use Yandex.Taxi (internationally known as Yango), one of the largest ride-hailing services in Eastern Europe. While the app is convenient and the service reliable, I noticed significant price fluctuations depending on the time of day, weather conditions, and other factors that weren’t immediately obvious.

New Yandex office walkability analysis

Turning Commute Frustration into Data-Driven Housing Decisions

When I heard about our company’s plans to relocate to a brand new headquarters, my analytical mind immediately kicked into high gear. As someone who values both sleep and work-life balance, I wasn’t thrilled about potentially extending my daily commute. Could data help me find the sweet spot between affordable housing and a reasonable walk to work?

Rather than browsing endless rental listings or asking around for opinions, I decided to approach this challenge like any good analyst would—by gathering concrete data and visualizing it. This side project would not only help me make a more informed personal decision but also sharpen my geospatial analysis skills.