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This project uses A/B testing to analyze whether payment method affects total fare. By comparing credit-card and cash trips through Python-based hypothesis testing and descriptive statistics, we identify if one payment type leads to higher fares, helping taxi drivers optimize earnings.

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Statistics Project On Hypothesis Testing | Data Analysis Project |

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The Primary Objective of this Project is to A/B test to examine the relationship between the total fare and the method of payment. Utilizing Python for hypothesis testing and descriptive statistics, the aim is to derive insights that can assist taxi drivers in maximizing earnings.

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.

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This project applies statistical hypothesis testing to analyze whether payment method (Card vs Cash) has a significant impact on taxi trip fare amounts. Using real-world trip-level data, the analysis combines exploratory data analysis (EDA), data cleaning, and a Welch’s two-sample t-test to derive actionable business insights.

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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.

🚕 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.

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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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.

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.

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.