NYC Taxi fare estimation analysis

🚖 NYC Taxi Fare Estimation
Google Advanced Data Analytics | Python, Pandas, Seaborn

Overview
Developed as part of the Google Advanced Data Analytics course in collaboration with Automatidata, this project focused on cleaning and analyzing NYC taxi trip data to support fare estimation for an upcoming predictive app.

Objective
Prepare and analyze real-world taxi trip data to generate insights that will later feed into a regression model for fare prediction.

Tools Used

Python – Pandas, Seaborn, Matplotlib, Scikit-learn, Statsmodels

Jupyter Notebook – For analysis, visualizations, and reporting

Key Contributions

Cleaned and joined large-scale NYC TLC datasets

Performed EDA to uncover patterns in fares, trip duration, and distances

Conducted hypothesis testing for fare-related assumptions

Created impactful visualizations for stakeholders

Delivered an executive summary with findings and recommendations

Outcome
Provided a structured dataset and deep insights to support regression-based fare predictions. Strengthened expertise in EDA, statistical analysis, and stakeholder-focused reporting.