Background:

Since 1971, TLC has been regulating and overseeing the licensing of New York City's taxi cabs, for-hire vehicles, commuter vans, and paratransit vehicles.

Project goal:

In this fictional scenario, the New York City Taxi and Limousine Commission (TLC) has approached the data consulting firm Automatidata to develop an app that enables TLC riders to estimate the taxi fares in advance of their ride.

Scenario:

The New York City TLC data is ready for exploratory data analysis (EDA) in Python. You will need to clean, join, validate, and create a visualization for the taxi commission data. The findings will be shared with internal stakeholders from different departments within Automatidata.

Course 3 tasks:

• Load data, explore, and extract the New York City TLC data with Python
• Use custom functions to organize the information within the New York City TLC dataset
• Build a dataframe for the New York City TLC project
• Create an executive summary for Automatidata for a general audience of internal professionals

Project goal:

In this fictional scenario, the New York City Taxi and Limousine Commission (TLC) has approached the data consulting firm Automatidata to develop an app that enables TLC riders to estimate the taxi fares in advance of their ride.

Scenario:

Exploratory data analysis is complete for the project. The New York City TLC would like the data team at Automatidata to analyze the relationship between fare amounts and payment type. The team agrees that the next step is to perform a hypothesis test using the data.

Course 4 tasks:

*Compute descriptive statistics *Conduct a hypothesis test using the New York City TLC dataset *Create an executive summary for the Automatidata data team before sharing the results with the client

Project goal:

In this fictional scenario, the New York City Taxi and Limousine Commission (TLC) has approached the data consulting firm Automatidata to develop an app that enables TLC riders to estimate the taxi fares in advance of their ride.

Scenario:

The relationship between fare amounts and payment type has been analyzed. The operations manager with New York City TLC is seeking more insight through regression modeling. The team’s next milestone is to run a regression model for taxi fares based on variables in the dataset.

Course 5 tasks:

• Compute descriptive statistics

• Create a regression model from the New York City TLC dataset

• Create an executive summary for the Automatidata data team before sharing the results with the client

Project goal:

In this fictional scenario, the New York City Taxi and Limousine Commission (TLC) has approached the data consulting firm Automatidata to develop an app that enables TLC riders to estimate the taxi fares in advance of their ride.

Background:

Since 1971, TLC has been regulating and overseeing the licensing of New York City's taxi cabs, for-hire vehicles, commuter vans, and paratransit vehicles.

Scenario:

New York City TLC stakeholders have been impressed with the data analytical work completed by the Automatidata team in this project. As a result, they have reached out once again for assistance in creating a machine learning model that can help predict whether or not a rider will be a generous tipper.

Course 6 tasks:

• To complete this task, you’ll need to:

• Build a random forest model from the New York City TLC dataset

• Create an executive summary for the Automatidata data team before sharing the results with the client

Note: The story, all names, characters, and incidents portrayed in this project are fictitious. No identification with actual persons (living or deceased) is intended or should be inferred. And, the data shared in this project has been created for pedagogical purposes.

https://www.nyc.gov/site/tlc/about/accessibility.page