A cab rental start-up company has successfully run the pilot project and now wants to launch their cab service across the country. After collecting the historical data from the pilot project, the certain variables are obtained which leads to the fare amount. The project is based on the Forecasting/Prediction type of data science problem statement.

Uber taxi fares complete analysis using machine learning and statistical techs

This project explores the relationship between payment type (cash vs. card) and fare amount in New York City taxi services. Using statistical analysis and hypothesis testing, we aim to determine if the method of payment significantly impacts fare prices.

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

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Statistical analysis and A/B testing on NYC Taxi data to analyze the relationship between payment types and fare amounts using Python.

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Statistical analysis and A/B testing of New York City Taxi & Limousine Commission (TLC) data to examine how payment type (credit card vs. cash) impacts total taxi fare amounts and identify strategies to increase driver revenue.

NYC Green Taxi trip data analysis using Python, statistical hypothesis testing, and regression-based fare prediction models.

Statistical Analysis applying descriptive statistics and hypothesis testing to examine the relationship between payment type and fare amount in NYC taxi fare data.

Statistical data analysis of NYC Taxi Trip data to study the impact of payment methods on fare revenue.

Statistical analysis of NYC taxi data to compare fare amounts by payment type using T-tests and uncover revenue insights.

Statistical analysis of NYC taxi trip data to explore the impact of payment type on fare amount using Python, hypothesis testing, and regression modeling

Statistical analysis of NYC taxi trip data to explore the impact of payment type on fare amount using Python, hypothesis testing, and regression modeling.

This project performs comprehensive exploratory data analysis (EDA) and statistical analysis on NYC yellow taxi trip data. It uncovers patterns, trends, and insights about taxi usage, fares, distances, and passenger behavior in New York City.

A statistical analysis of NYC Yellow Taxi data using T-Tests to validate the correlation between payment methods (Card vs. Cash) and trip fare dynamics.

Data analysis project using NYC Taxi data to explore how payment methods (cash vs. credit card) impact fare revenue, with statistical testing and business insights.

🚕 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.

As the capstone project for my Coursera Advanced Google Data Analytics Specialization, I conducted a comprehensive statistical analysis to determine the relationship between taxi payment type and fare amount.

Analyzed taxi trip data using Python to understand passenger distribution, payment behavior (Cash vs Card), and fare patterns. Performed exploratory data analysis, data visualization, pivot table analysis, and statistical hypothesis testing (Welch’s T-test) to determine whether payment type significantly impacts fare amount.

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 Taxi trip data to explore factors affecting fare amounts and maximize revenue. It includes data cleaning, feature engineering, exploratory data analysis, and statistical testing using Python and Jupyter Notebook.

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.

Statistical analysis of NYC Yellow Taxi data using Python. Cleaned 6M+ records, performed EDA, outlier treatment, hypothesis testing, and linear regression to study how payment type, trip duration, and distance influence fare amount.

This project explores the relationship between fare amount and payment type using NYC taxi trip data. It uses statistical analysis to uncover customer preferences and behaviors based on payment methods and ride characteristics.

This project analyzes New York City taxi trip data to explore factors affecting fare amounts and maximize revenue. It includes data cleaning, feature engineering, exploratory data analysis, and statistical testing using Python and Jupyter Notebook.

Taxi Payment Type Analysis: Analyzed ~6.4M NYC taxi trips to examine whether payment method (Card vs Cash) relates to fare differences. Performed data cleaning, EDA, outlier removal (IQR), visualizations, and an independent two-sample t-test to evaluate statistically significant differences in average fares between payment types.

Performed exploratory data analysis on NYC Taxi trip records to uncover travel patterns, peak hours, fare distributions, and geographic trends. Applied data cleaning, visualization, and statistical techniques to derive actionable insights into urban mobility and demand.

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 explores how different payment methods (Cash vs Card) influence fare amounts in the taxi industry. Using real trip data and statistical analysis, it uncovers patterns in rider payment preferences and how they affect overall revenue.