SQL case study developed to analyze historical taxi-trip data and identify demand patterns, company performance, and the impact of weather conditions on trip duration.
This project explores historical taxi-trip data stored in a relational database using SQL queries and statistical analysis.
The objective was to identify demand patterns, compare taxi companies, and evaluate whether weather conditions significantly affect trip duration.
- Statistical evidence that weather conditions significantly impact trip duration.
- Identification of demand concentration among taxi companies.
- Insights into travel patterns across different time periods.
Taxi companies need to understand customer demand and external factors affecting travel times in order to optimize operations and improve service quality.
This project analyzes operational data to answer business questions and validate hypotheses using SQL and statistical methods.
The project uses relational tables containing historical information about:
- Taxi companies
- Neighborhoods
- Taxi trips
- Weather conditions
The analysis combines SQL queries with statistical testing performed in Python.
- SQL
- PostgreSQL
- Python
- pandas
- Statistical Analysis
- Database Exploration
- SQL Queries
- Data Aggregation
- Exploratory Analysis
- Statistical Hypothesis Testing
- Business Interpretation
The analysis included:
- Company ranking by number of trips
- Neighborhood demand analysis
- Weather-based trip comparison
- Aggregated business metrics
- Statistical hypothesis testing
The project revealed that:
- Weather conditions have a statistically significant effect on trip duration.
- Certain taxi companies dominate the market during peak periods.
- Demand varies depending on destination and external conditions.
These findings provide valuable information for operational planning and service optimization.
The insights obtained from this analysis can help taxi companies:
- Improve fleet allocation
- Anticipate operational delays
- Better understand customer demand
- Support data-driven business decisions
taxi-sql-analysis
│
├── Analisis_de_viajes_en_taxi.ipynb
├── taxi.png
└── README.md
Paola Romero
Data Analyst | Business Intelligence | Data Science
📁 Portfolio
https://paolaromerop.github.io/paola-portafolio/
💼 LinkedIn
https://www.linkedin.com/in/paolaromeroperez/
Developed as part of the TripleTen Data Science Bootcamp, applying SQL, PostgreSQL, statistical analysis, and business intelligence techniques to solve real-world analytical problems.