DATA & ANALYTICS / PROJECT 06
NYC Taxi Fare & Tip Analysis.
Understand the trip. Predict the fare.
Python · Jupyter · Random Forest · XGBoost
View repository ↗THE PROJECT
Real context.
Practical engineering.
An in-progress analytics and machine learning project exploring fare pricing and tipping behavior in 2017 NYC yellow taxi data as part of the Automatidata program.
Adapted from the project’s GitHub README. Explore the repository for implementation details, setup instructions, and source code.
Read the full documentation ↗THE APPROACH
01
Clean data, engineer features, and explore trip patterns.
02
Use hypothesis testing and multiple linear regression for fare analysis.
03
Explore Random Forest and XGBoost to classify generous tipping.
WORKFLOW AT A GLANCE
Trip data → Exploration → Features → Prediction