This project aims to leverage the New York City Taxi and Limousine Commission (TLC) dataset to predict taxi fares and provide insights into the operations and business of the TLC. The solution includes an automated machine learning (AutoML) model for fare prediction, an infographic for data visualization, and SQL queries for data analysis.
An AutoML model that predicts taxi fares based on various factors such as pickup and dropoff locations, trip distance, time of day, etc. This model helps the TLC in fare regulation and prevents overcharging by taxi drivers.
A visually appealing infographic that represents the key insights and trends from the dataset. This infographic helps the TLC in understanding the patterns and trends in the data, which could inform their decision-making process.
SQL queries are used to answer various business questions on the dataset. These queries help the TLC in getting specific insights from the data, such as the most popular pickup and dropoff locations, average fare per trip, etc.