Jingmiao Fei
My research focuses on using data-driven methods to solve real-world problems， particularly in the field of city palnning. During my studies, I’ve worked on several projects involving data analysis and modeling, especially with spatiotemporal data. For example, I’ve done researches on housing price prediction, bike rebalancing optimization, and urban crime risk analysis.
I’m proficient in Python, R and JavaScript, and I have hands-on experience with machine learning and deep leaning techniques, such as using supervised learning for land use classification. I'm skilled at data cleaning, modeling, and visualization (making maps and web application).
I enjoy solving real-world problems through data and turning insights into actions, and I aim to build sustainable, livable cities and natural environment that enhance well-being.
I’m proficient in Python, R and JavaScript, and I have hands-on experience with machine learning and deep leaning techniques, such as using supervised learning for land use classification. I'm skilled at data cleaning, modeling, and visualization (making maps and web application).
I enjoy solving real-world problems through data and turning insights into actions, and I aim to build sustainable, livable cities and natural environment that enhance well-being.
