This project aims at analyzing two different versions of Udacity website and determining which version of the website will bring more traffic or generate more revenue. The project included choosing a metric, building intuition, defining hypothesis and comparing two samples for hypothesis testing.

Hypothesis testing using data analytics for yellow trip car ride provider service to increase their revenue

A/B hypothesis testing can be leveraged by businesses to improve customer experience of their products or services and also improve customer retention. This project focuses on uses A/B hypothesis testing in business decision making.

Hypothesis prioritization using ICE/RICE frameworks and statistical A/B test analysis.

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.

R Shiny dashboard analyzing Microsoft's financial performance (FY2016–FY2023) using SEC EDGAR XBRL data. Hypothesis-driven analysis testing strategic propositions on revenue growth, profitability, cash generation, and capital efficiency.

A project focused on unraveling the factors impacting the demand for shared electric cycles in the Indian market. In response to Yulu's recent revenue setbacks, this project employs Python and hypothesis testing to provide actionable insights for strategic expansion and revenue recovery.

Conducted a comprehensive data analysis project on movie genres using Python and pandas. The analysis focused on determining the most common genres, and those with higher budgets, revenues, and popularity. Demonstrated strong skills in data cleaning, preparation, and hypothesis testing.

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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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.

Statistics Project On Hypothesis Testing | Data Analysis Project |

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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.

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.

Hypothesis testing and statistical analysis to gain insights about effects on movies’ popularity and box office revenue

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A/B hypothesis testing for

Statistical Data analysis

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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.

A Statistical Analysis of Payment Behavior and Fare Pricing