this repository has datasets containing information of Uber pickups in NYC from April 2014 to September 2014 and January to June 2015. data Analysis , virtualization and some insights are gathered here

Related tags

Text Data & NLPuber-pickups-analysis
Overview

uber-pickups-analysis

Data Source: https://www.kaggle.com/fivethirtyeight/uber-pickups-in-new-york-city

Information about data set

The dataset contains, roughly, TWO groups of files: ● Uber trip data from 2014 (April - September), separated by month, with detailed location information. ● Uber trip data from 2015 (January - June), with less fine-grained location information.

Uber trip data from 2014 There are six files of raw data on Uber pickups in New York City from April to September 2014. The files are separated by month and each has the following columns: ● Date/Time : The date and time of the Uber pickup ● Lat : The latitude of the Uber pickup ● Lon : The longitude of the Uber pickup ● Base : The TLC base company code affiliated with the Uber pickup. These files are named: ● uber-raw-data-apr14.csv ● uber-raw-data-aug14.csv ● uber-raw-data-jul14.csv ● uber-raw-data-jun14.csv ● uber-raw-data-may14.csv ● uber-raw-data-sep14.csv

Uber trip data from 2015

Also included is the file uber-raw-data-janjune-15.csv This file has the following columns: ● Dispatching_base_num : The TLC base company code of the base that dispatched the Uber. ● Pickup_date : The date and time of the Uber pickup ● Affiliated_base_num : The TLC base company code affiliated with the Uber pickup. ● locationID : The pickup location ID affiliated with the Uber pickup These files are named:

  • uber-raw-data-janjune-15.csv

motive of Project

To analyze the data of the customer rides and visualize the data to find insights that can help improve business. Data analysis and visualization is an important part of data science. They are used to gather insights from the data and with visualization you can get quick information from the data.

How to Run the Project

In order to run the project just download the data from above mentioned source then run any file.

Prerequisites

You need to have installed following softwares and libraries in your machine before running this project.

Python 3 Anaconda: It will install ipython notebook and most of the libraries which are needed like sklearn, pandas, seaborn, matplotlib, numpy, scipy.

Installing

Python 3: https://www.python.org/downloads/ Anaconda: https://www.anaconda.com/download/

Authors

KILARI JASWANTH and DEVA DEEKSHITH(https://github.com/deva025) - combined work

Owner
GitHub Repository
Easy to start. Use deep nerual network to predict the sentiment of movie review.

Easy to start. Use deep nerual network to predict the sentiment of movie review. Various methods, word2vec, tf-idf and df to generate text vectors. Various models including lstm and cov1d. Achieve f1

1 Nov 19, 2021
Research code for ECCV 2020 paper "UNITER: UNiversal Image-TExt Representation Learning"

UNITER: UNiversal Image-TExt Representation Learning This is the official repository of UNITER (ECCV 2020). This repository currently supports finetun

Yen-Chun Chen 680 Dec 24, 2022
Contact Extraction with Question Answering.

contactsQA Extraction of contact entities from address blocks and imprints with Extractive Question Answering. Goal Input: Dr. Max Mustermann Hauptstr

Jan 2 Apr 20, 2022
Signature remover is a NLP based solution which removes email signatures from the rest of the text.

Signature Remover Signature remover is a NLP based solution which removes email signatures from the rest of the text. It helps to enchance data conten

Forges Alterway 8 Jan 06, 2023
A Streamlit web app that generates Rick and Morty stories using GPT2.

Rick and Morty Story Generator This project uses a pre-trained GPT2 model, which was fine-tuned on Rick and Morty transcripts, to generate new stories

₸ornike 33 Oct 13, 2022
Pytorch NLP library based on FastAI

Quick NLP Quick NLP is a deep learning nlp library inspired by the fast.ai library It follows the same api as fastai and extends it allowing for quick

Agis pof 283 Nov 21, 2022
Pytorch code for ICRA'21 paper: "Hierarchical Cross-Modal Agent for Robotics Vision-and-Language Navigation"

Hierarchical Cross-Modal Agent for Robotics Vision-and-Language Navigation This repository is the pytorch implementation of our paper: Hierarchical Cr

44 Jan 06, 2023
Visual Automata is a Python 3 library built as a wrapper for Caleb Evans' Automata library to add more visualization features.

Visual Automata Copyright 2021 Lewi Lie Uberg Released under the MIT license Visual Automata is a Python 3 library built as a wrapper for Caleb Evans'

Lewi Uberg 55 Nov 17, 2022
Multi Task Vision and Language

12-in-1: Multi-Task Vision and Language Representation Learning Please cite the following if you use this code. Code and pre-trained models for 12-in-

Meta Research 711 Jan 08, 2023
NLP and Text Generation Experiments in TensorFlow 2.x / 1.x

Code has been run on Google Colab, thanks Google for providing computational resources Contents Natural Language Processing（自然语言处理） Text Classificati

1.5k Nov 14, 2022
PyTorch original implementation of Cross-lingual Language Model Pretraining.

XLM NEW: Added XLM-R model. PyTorch original implementation of Cross-lingual Language Model Pretraining. Includes: Monolingual language model pretrain

Facebook Research 2.7k Dec 27, 2022
PortaSpeech - PyTorch Implementation

PortaSpeech - PyTorch Implementation PyTorch Implementation of PortaSpeech: Portable and High-Quality Generative Text-to-Speech. Model Size Module Nor

Keon Lee 276 Dec 26, 2022
Linear programming solver for paper-reviewer matching and mind-matching

Paper-Reviewer Matcher A python package for paper-reviewer matching algorithm based on topic modeling and linear programming. The algorithm is impleme

Titipat Achakulvisut 66 Jul 05, 2022
Auto_code_complete is a auto word-completetion program which allows you to customize it on your needs

auto_code_complete is a auto word-completetion program which allows you to customize it on your needs. the model for this program is one of the deep-learning NLP(Natural Language Process) model struc

RUO 2 Feb 22, 2022
IMDB film review sentiment classification based on BERT's supervised learning model.

IMDB film review sentiment classification based on BERT's supervised learning model. On the other hand, the model can be extended to other natural language multi-classification tasks.

Paris 1 Apr 17, 2022
NLP topic mdel LDA - Gathered from New York Times website

NLP topic mdel LDA - Gathered from New York Times website

1 Oct 14, 2021
Sequence Modeling with Structured State Spaces

Structured State Spaces for Sequence Modeling This repository provides implementations and experiments for the following papers. S4 Efficiently Modeli

HazyResearch 902 Jan 06, 2023
A CRM department in a local bank works on classify their lost customers with their past datas. So they want predict with these method that average loss balance and passive duration for future.

Rule-Based-Classification-in-a-Banking-Case. A CRM department in a local bank works on classify their lost customers with their past datas. So they wa

ÖMER YILDIZ 4 Mar 20, 2022
Implementation of paper Does syntax matter? A strong baseline for Aspect-based Sentiment Analysis with RoBERTa.

RoBERTaABSA This repo contains the code for NAACL 2021 paper titled Does syntax matter? A strong baseline for Aspect-based Sentiment Analysis with RoB

106 Nov 28, 2022
A simple recipe for training and inferencing Transformer architecture for Multi-Task Learning on custom datasets. You can find two approaches for achieving this in this repo.

multitask-learning-transformers A simple recipe for training and inferencing Transformer architecture for Multi-Task Learning on custom datasets. You

Shahrukh Khan 48 Jan 02, 2023