Uber & Lyft Analysis Project

^{(Image Source: https://www.ridester.com/uber-vs-lyft/)}

Since the Jupyter Notebook file is not rendered properly on GitHub, please use its HTML version or view it with NBViewer for the best experience

I. Data set:

• Downloaded from Kaggle: https://www.kaggle.com/datasets/brllrb/uber-and-lyft-dataset-boston-ma
• Contains information of Lyft & Uber rideshare trips in Boston, along with various weather conditions at the time

II. Main goals:

• Perform data cleaning, EDA, and conduct hypothesis tests to explore the main differences in characteristics between Lyft & Uber services
• Visualize insights using interactive maps and charts
• Construct regression models to examine the major factors influencing the prices of Lyft & Uber
• Construct classification models to examine the major factors determining whether prices will surge or not

III. Files shared:

• .ipynb: presents a comprehensive report that fulfills all main goals (using Python)
• .html: the printed output of the .ipynb script
• .rmd: presents a smaller report that focuses solely on the predictive modelling portion (using R)
• .pdf: the report knitted by the R Markdown (.rmd) script
• .rar: contains the data set used for this project