All Projects → SalilVishnuKapur → Predicting-Transportation-Modes-of-GPS-Trajectories

SalilVishnuKapur / Predicting-Transportation-Modes-of-GPS-Trajectories

Licence: Apache-2.0 license
Understanding transportation mode from GPS (Global Positioning System) traces is an essential topic in the data mobility domain. In this paper, a framework is proposed to predict transportation modes. This framework follows a sequence of five steps: (i) data preparation, where GPS points are grouped in trajectory samples; (ii) point features gen…

Programming Languages

python
139335 projects - #7 most used programming language

Projects that are alternatives of or similar to Predicting-Transportation-Modes-of-GPS-Trajectories

AutoTS
Automated Time Series Forecasting
Stars: ✭ 665 (+1697.3%)
Mutual labels:  feature-engineering
anovos
Anovos - An Open Source Library for Scalable feature engineering Using Apache-Spark
Stars: ✭ 77 (+108.11%)
Mutual labels:  feature-engineering
50-days-of-Statistics-for-Data-Science
This repository consist of a 50-day program. All the statistics required for the complete understanding of data science will be uploaded in this repository.
Stars: ✭ 19 (-48.65%)
Mutual labels:  feature-engineering
EvolutionaryForest
An open source python library for automated feature engineering based on Genetic Programming
Stars: ✭ 56 (+51.35%)
Mutual labels:  feature-engineering
ReinforcementLearning Sutton-Barto Solutions
Solutions and figures for problems from Reinforcement Learning: An Introduction Sutton&Barto
Stars: ✭ 20 (-45.95%)
Mutual labels:  feature-engineering
AutoTabular
Automatic machine learning for tabular data. ⚡🔥⚡
Stars: ✭ 51 (+37.84%)
Mutual labels:  feature-engineering
clink
Clink is a library that provides APIs and infrastructure to facilitate the development of parallelizable feature engineering operators that can be used in both C++ and Java runtime.
Stars: ✭ 24 (-35.14%)
Mutual labels:  feature-engineering
Bike-Sharing-Demand-Kaggle
Top 5th percentile solution to the Kaggle knowledge problem - Bike Sharing Demand
Stars: ✭ 33 (-10.81%)
Mutual labels:  feature-engineering
PubMed-Best-Match
Machine-learning based pipeline relying on LambdaMART currently used in PubMed for relevance (Best Match) searches
Stars: ✭ 36 (-2.7%)
Mutual labels:  feature-engineering
Quora-Paraphrase-Question-Identification
Paraphrase question identification using Feature Fusion Network (FFN).
Stars: ✭ 19 (-48.65%)
Mutual labels:  feature-engineering
featuretoolsOnSpark
A simplified version of featuretools for Spark
Stars: ✭ 24 (-35.14%)
Mutual labels:  feature-engineering
exemplary-ml-pipeline
Exemplary, annotated machine learning pipeline for any tabular data problem.
Stars: ✭ 23 (-37.84%)
Mutual labels:  feature-engineering
NVTabular
NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.
Stars: ✭ 797 (+2054.05%)
Mutual labels:  feature-engineering
skrobot
skrobot is a Python module for designing, running and tracking Machine Learning experiments / tasks. It is built on top of scikit-learn framework.
Stars: ✭ 22 (-40.54%)
Mutual labels:  feature-engineering
hamilton
A scalable general purpose micro-framework for defining dataflows. You can use it to create dataframes, numpy matrices, python objects, ML models, etc.
Stars: ✭ 612 (+1554.05%)
Mutual labels:  feature-engineering
kaggle-berlin
Material of the Kaggle Berlin meetup group!
Stars: ✭ 36 (-2.7%)
Mutual labels:  feature-engineering
zca
ZCA whitening in python
Stars: ✭ 29 (-21.62%)
Mutual labels:  feature-engineering
go-featureprocessing
🔥 Fast, simple sklearn-like feature processing for Go
Stars: ✭ 81 (+118.92%)
Mutual labels:  feature-engineering
Feature-Engineering-for-Fraud-Detection
Implementation of feature engineering from Feature engineering strategies for credit card fraud
Stars: ✭ 31 (-16.22%)
Mutual labels:  feature-engineering
dominance-analysis
This package can be used for dominance analysis or Shapley Value Regression for finding relative importance of predictors on given dataset. This library can be used for key driver analysis or marginal resource allocation models.
Stars: ✭ 111 (+200%)
Mutual labels:  feature-engineering

Predicting-Transportation-Modes-of-GPS-Trajectories :

Understanding transportation mode from GPS (Global Positioning System) traces is an essential topic in the data mobility domain. In this project, a framework is proposed to predict transportation modes.

This framework follows a sequence of five steps:

1.) Data preparation, where GPS points are grouped in trajectory samples

2.) Point features generation

3.) Trajectory features extraction

4.) Hierarchical classification

5.) Classification Algorithm Validation

Dataset (geolife_raw.csv) :

https://drive.google.com/file/d/1DxXYVOLTgWcjN8IVW0O97au9USFBt4Zx/view?usp=sharing

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].