An Ensemble Machine Learning Model To Predictive Analysis of End to End Uber Data

Accurately predicting both daily and monthly transactions is crucial for businesses, providing invaluable insights to analyze fluctuations and formulate strategic plans effectively. This predictive capability not only enhances overall company performance but also plays a pivotal role in optimizing taxi fleet management and reducing wait times for passengers and drivers alike. Leveraging the realm of predictive analytics, our study delves into the utilization of data analytics to scrutinize Uber’s transaction dataset. By exploring historical data and employing a novel machine-learning model, we propose a method to forecast taxi demand across specific areas. Through rigorous analysis of various machine learning algorithms such as AdaBoost, XGBoost, Gradient Boosting, K-Nearest Neighbors (KNN), and Bagging with extra tree classifiers, we evaluate their performance using key metrics like r2 score, mean squared error (MSE), and mean absolute error (MAE). Our findings highlight XGBoost’s remarkable accuracy across all metrics, underscoring the effectiveness of machine learning methodologies in devising robust solutions to enhance urban mobility. Additionally, visual aids such as histograms and heat maps provide a clear representation of data trends, aiding in informed decision-making and facilitating actionable changes.