Data Science-Capstone Project

Real or Not? NLP with Disaster Tweets (Kaggle Challenge)

Table of Contents:

1. Description

2. Installation

3. File Descriptions

4. Dataset

5. Summary Blogpost

6. Licensing, authors and acknowledgement

1. Description:

Twitter has become an important communication channel in times of emergency. The ubiquitousness of smartphones enables people to announce an emergency they’re observing in real-time. Because of this, more agencie are interested in programatically monitoring Twitter (i.e. disaster relief organizations and news agencies).

Main goal of this project is to build a machine learning model that predicts which Tweets are about real disasters and which one’s aren’t.

2. Installation:

Anaconda Python distribution was used to create the jupyter notebook for this project.There were no additional liabraries installed in support of this project.

The version of the notebook server is: 5.7.4.

The version of Python us: Python 3.7.1 (default, Dec 10 2018, 22:54:23) [MSC v.1915 64 bit (AMD64)].

3. File Descriptions:

Following files are uploaded in the repository:

1. DS Capstone.ipynb: Contains all the analysis and modeling of the Boston and Seattle Airbnb datasets
2. train.csv - the training set
3. test.csv - the test set
4. sample_submission.csv - a sample submission file in the correct format

4. Dataset:

Dataset is provided by Kaggle and can be found at below links:

https://www.kaggle.com/c/nlp-getting-started/data

5. Summary Blogpost:

Summary of data analysis and results can be found at below link on the medium portal:

https://medium.com/real-or-not-nlp-with-disaster-tweets/real-or-not-nlp-with-disaster-tweets-a-data-science-capstone-project-fafa6c35c16f

6. Licensing,authors and acknowledgement:

This dataset was created by the company figure-eight and originally shared on their ‘Data For Everyone’ website. Kaggle hosted a challenge to develop machine learning models to classify tweets into real disaster or not.

Disclaimer: The dataset for this competition contains text that may be considered profane, vulgar, or offensive.