Develope a CNN-LSTM model with Multi-Head Attention mechanism model to Predict Taxi demand
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Updated
Aug 11, 2024 - Jupyter Notebook
Develope a CNN-LSTM model with Multi-Head Attention mechanism model to Predict Taxi demand
This project involves an in-depth analysis of tips from NYC taxi trips. Using a dataset that includes detailed information on trips, passengers, and payments, we aim to uncover patterns and insights related to tipping behavior.
Repository for projects from Data Science course
App for taxi order and driver management
Project to predict the duration of a taxi trip in NYC.
Explore NYC Green Taxi data, predicting fares and optimizing pickup locations using machine learning. Regression models uncover travel patterns and enhance taxi services for an efficient urban transport experience.
Taxi analysis for the New York City area with a focus on Manhattan
🚕 M.EIC 2021/2022 - 1ˢᵗ year/ 2ⁿᵈ semester
This repository houses complete data science projects.
Scenario creation for amodeus taxi scenarios, initially private, eventually public
Used Python to draw interactive bar chart and piechart with NYC taxi data
New York Taxi dataset analysis using Python
About In this project using New York dataset we will predict the fare price of next trip. The dataset can be downloaded from https://www.kaggle.com/kentonnlp/2014-new-york-city-taxi-trips The dataset contains 8 features along with GPS coordinates of pickup and dropoff
Codes and data for a published work "Multi-scale detection and interpretation of spatio-temporal anomalies of human activities represented by time-series" (https://doi.org/10.1016/j.compenvurbsys.2021.101627)
Create an animation from the NYC Taxi dataset
Code for fetching, sampling, and analysis of NYC taxi data from TLC and Uber for 2009-2018
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