Data-driven analysis on NYC taxi trips to explore how payment methods impact fare amounts, using descriptive analysis, hypothesis testing, and regression modeling to optimize driver revenue.

This project performs an A/B test to explore the relationship between total fare and payment method. Using Python’s hypothesis testing and descriptive statistics, it examines whether fares differ significantly between credit card and cash payments, providing insights to help taxi drivers increase revenue.

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Analyzed 6M+ taxi trip records to evaluate revenue differences between payment methods. Conducted A/B testing to compare credit card vs cash payment fares.

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A Statistical Analysis of Payment Behavior and Fare Pricing

This project analyzes taxi trip data to determine whether payment type (Cash vs Credit Card) has an impact on fare amount. The goal is to help taxi companies and drivers increase revenue by understanding customer payment behavior.

Hypothesis testing project analyzing the effect of payment methods on taxi revenue using Python and statistical inference.

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🚕 Statistical analysis of NYC taxi trip data to test whether payment type (card vs cash) impacts fare amounts, using Python for hypothesis testing and visualization.

To maximize taxi revenue and driver satisfaction, we are analyzing the link between payment methods and fare amounts. This data-driven research aims to determine if how a passenger pays significantly impacts pricing, providing actionable insights for the fast-paced booking sector.

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This project analyzes NYC Taxi Trip data to explore how different payment methods (card vs. cash) impact fare amounts. The goal is to provide data-driven insights that can help maximize revenue for taxi drivers by identifying and encouraging the use of payment methods associated with higher fares.

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Maximising revenue for taxi drivers. Data analysis, visualisation, statistical analysis, hypothesis testing

Applied hypothesis testing on NYC taxi data to assess payment method impact on fares and driver revenue.

A statistical data analysis with hypothesis testing to understand wether payment methods impact the revenue of NYC Taxi drivers

Data-driven analysis of NYC Taxi trips to uncover how payment type influences fare revenue, using hypothesis testing and regression to guide driver revenue optimization strategies.

This project examines the impact of payment methods on taxi fare revenue using NYC Yellow Taxi data. Through EDA and A/B hypothesis testing, it finds that credit card payments generate significantly higher fares, offering data-driven insights to improve driver revenue.

This project analyzes NYC taxi trip data to examine how payment methods impact fare amounts and driver revenue. Using data cleaning, exploratory analysis, hypothesis testing, and regression techniques, it identifies patterns showing higher fares for card payments and provides insights to optimize revenue strategies for taxi drivers.

Performed hypothesis testing on taxi-trip data to analyze how different payment types affect driver revenue. Identified statistically significant patterns and recommended the optimal payment method mix to maximize earnings.

Statistical analysis project using hypothesis testing to explore the relationship between payment method and fare amount in NYC Taxi data. The goal is to maximize revenue for drivers using data-driven insights.

A data analysis project on NYC Yellow Taxi data focusing on maximizing revenue through EDA, data visualization, hypothesis testing, and storytelling with data. Covers data cleaning, feature engineering, and actionable business insights.

tatistical hypothesis testing and revenue analysis of NYC Yellow Taxi trips (January 2020) to evaluate patterns, trends, and potential growth opportunities for the cab provider. Includes exploratory data analysis, statistical tests, and insights derived from trip-level data.

This comprehensive project analyzes Taxi trip data to uncover patterns in taxi usage, identify revenue drivers, and build predictive models for fare amounts and customer tipping behavior. We conducted end-to-end analysis including exploratory data analysis, statistical hypothesis testing, regression modeling, and machine learning classification.

This project analyzes NYC taxi trip data to explore how payment methods (card vs cash) influence fare amounts and driver revenue. Using statistical analysis and hypothesis testing, it identifies key trends in customer behavior and revenue patterns. The insights help recommend strategies to maximize driver earnings through optimized payment preferen

This project is my first foray into statistical analysis, focusing on the relationship between payment types and fare amounts in the NYC yellow taxi dataset. The goal is to provide data-driven insights to help maximize revenue streams for taxi drivers. The project employs descriptive analysis, hypothesis testing to investigate these relationships.

This project's main goal is to run an A/B test to examine the relationship between the total fare and the method of payment. We utilise Python's hypothesis testing and descriptive statistics to extract valuable insights that can help taxi drivers increase their revenue.

This project's main goal is to run an A/B test to examine the relationship between the total fare and the method of payment. We utilise Python's hypothesis testing and descriptive statistics to extract valuable insights that can help taxi drivers increase their revenue.