Automatidata Project Proposal for Data Analysis

Dataset Description

Automatidata is a data consulting firm that helps enterprises maximize the potential of their data. They work with clients to transform raw data into powerful insights and solutions such as performance dashboards, customer‑facing tools, and strategic business recommendations. Their approach emphasizes aligning data analysis with client business needs to drive better decisions and achieve business objectives. Overall, Automatidata’s mission is to turn data into a strategic asset for the digital age, fostering growth and success.

Project Background

Automatidata has been commissioned to develop a regression model for the New York City Taxi and Limousine Commission (TLC) that predicts trip duration based on location and time. The goal is to help TLC gain better understanding and control of NYC taxi and limousine services. TLC’s data come from more than 200 k taxi and limousine license holders, amounting to roughly one million combined trips per day.

Project Activities

To achieve this goal, the project team must complete the following tasks:

1. Global Project Documentation: Collaborate with the senior project manager to prepare documentation covering project objectives and milestones.
2. TLC Dataset Inspection: Prior to analysis, the TLC dataset requires a general sanity check. The data team should conduct exploratory data analysis (EDA) to understand the dataset’s contents.
3. Regression Model Development: The core focus is to develop a regression model that provides insights for TLC. The data analysis director stresses the importance of ensuring the model conforms to the project scope before sharing insights with TLC.
4. Visualization Creation: TLC’s operations manager has requested the team develop visualizations for presentation to TLC executives.
5. Variable Relationship Establishment: The data analysis director recommends using A/B testing to determine relationships among variables in the TLC dataset.
6. Insight Presentation to TLC: Once the final model is built, the data team should identify key points to present to TLC.

Business Problem

TLC is responsible for regulating and licensing taxis and rental vehicles in New York City. To manage and standardize these services effectively, TLC needs to understand trip durations and identify improvement areas. The current manual process of collecting trip‑duration data is time‑consuming and costly. Therefore, TLC has partnered with Automatidata to develop a location‑ and time‑based trip‑duration prediction regression model.

Issues and Considerations

1. Target Audience: The project targets TLC senior management, including financial and administrative leaders as well as operations managers.
2. Project Objective: Develop a regression model that predicts trip duration based on location and time, enabling TLC to manage and regulate taxi and limousine services more effectively. The model offers an efficient, cost‑effective alternative to manual data collection, allowing TLC to pinpoint areas for improvement, optimize services, increase customer satisfaction, reduce wait times, and improve resource efficiency. Additionally, the model can aid in estimating trip costs and improving traffic flow.
3. Key Questions:
   • What is the state of the dataset?
   • How to avoid over‑reliance on heuristic approaches?
   • Which variables should be included in the regression model?
   • What transformations are needed to improve model performance?
   • Which type of regression model should be applied (e.g., linear regression, polynomial regression)?
   • How to evaluate and validate the model?
   • How to design the model for real‑time production predictions?
   • What additional insights or trends in the data could aid TLC’s decision‑making?
   • How to integrate the model into TLC’s existing workflows?
4. Resources Required:
   • Data: Large dataset of taxi and limousine trip records including location, time, and trip duration.
   • Jupyter Notebook
   • Time and Budget: Sufficient time and funding are essential for project success.
   • Stakeholder Input
5. Deliverables:
   • Data Exploration Report: Detailed documentation of data cleaning and preprocessing, insights into data distribution, identification of missing values, and exploration of variable correlations.
   • Model Selection Report: Overview of evaluated machine‑learning models, their pros and cons, and the rationale for selecting the most appropriate model.
   • Regression Model Report: Description of the regression model development process, including feature engineering and selection.
   • Prediction Results Report: Summary of final model predictions and performance metrics, with interpretation of results.
   • Insight Presentation: Slides summarizing key insights and findings, using visualisations and charts to explain model performance and other important observations.