Welcome to the Automatidata Project! You have just started as a data professional in a fictional data consulting firm, Automatidata. Their client, the New York City Taxi and Limousine Commission (New York City TLC), has hired the Automatidata team for its reputation in helping their clients develop data-based solutions. The team is still in the early stages of the project. Previously, you were asked to complete a project proposal by your supervisor, DeShawn Washington. You have received notice that your project proposal has been approved and that New York City TLC has given the Automatidata team access to their data. To get clear insights, New York TLC's data must be analyzed, key variables identified, and the dataset ensured it is ready for analysis.
You are the newest data professional in a fictional data consulting firm: Automatidata. The team is still early into the project, having only just completed an initial plan of action and some early Python coding work.
Luana Rodriquez, the senior data analyst at Automatidata, is pleased with the work you have already completed and requests your assistance with some EDA and data visualization work for the New York City Taxi and Limousine Commission project (New York City TLC) to get a general understanding of what taxi ridership looks like. The management team is asking for a Python notebook showing data structuring and cleaning, as well as any matplotlib/seaborn visualizations plotted to help understand the data. At the very least, include a box plot of the ride durations and some time series plots, like a breakdown by quarter or month.
Additionally, the management team has recently asked all EDA to include Tableau visualizations. For this taxi data, create a Tableau dashboard showing a New York City map of taxi/limo trips by month. Make sure it is easy to understand to someone who isn’t data savvy, and remember that the assistant director at the New York City TLC is a person with visual impairments.
You are a data professional in a data consulting firm, called Automatidata. The current project for their newest client, the New York City Taxi & Limousine Commission (New York City TLC) is reaching its midpoint, having completed a project proposal, Python coding work, and exploratory data analysis.
You receive a new email from Uli King, Automatidata’s project manager. Uli tells your team about a new request from the New York City TLC: to analyze the relationship between fare amount and payment type. A follow-up email from Luana includes your specific assignment: to conduct an A/B test.
The data consulting firm Automatidata has recently hired you as the newest member of their data analytics team. Their newest client, the NYC Taxi and Limousine Commission (New York City TLC), wants the Automatidata team to build a multiple linear regression model to predict taxi fares using existing data that was collected over the course of a year. The team is getting closer to completing the project, having completed an initial plan of action, initial Python coding work, EDA, and A/B testing.
The Automatidata team has reviewed the results of the A/B testing. Now it’s time to work on predicting the taxi fare amounts. You’ve impressed your Automatidata colleagues with your hard work and attention to detail. The data team believes that you are ready to build the regression model and update the client New York City TLC about your progress.
You are a data professional in a data analytics firm called Automatidata. Their client, the New York City Taxi & Limousine Commission (New York City TLC), was impressed with the work you have done and has requested that you build a machine learning model to predict if a customer will not leave a tip. They want to use the model in an app that will alert taxi drivers to customers who are unlikely to tip, since drivers depend on tips. Conclusion:
The importance plot tells that ‘mean_duration’, ‘predicted_fare’ and ‘mean_distance’ are the three most important features.
The Random Forest model is not a great model, though it could still be useful depending on the situation/objective. For instance, it could be very useful if the objective to to help in giving idea whether or not a customer will leave a relatively good tip. It may be worth it to test with a group of drivers.
KEY INSIGHT:
The model reveals that for every mile travelled the fare increases by $2. Or for every 3.57 miles travelled, the fare increases by $7.13.
The model provides a generally strong and reliable fare prediction that can be used in downstream modeling efforts.
These findings can be used to create an app that allows NYC TLC riders to see the estimated fare before their ride begins. Model Metrics: Model metrics:
R-squared 0.868: This means that 86.8% of the variance is described by the model.
MAE 2.13
MSE: 14.34
RMSE 3.8