kaggle-NLP-Disaster-Tweets

CONCEPT

The ubiquitousness of smartphones enables people to announce an emergency they’re observing in real time. Because of this, more agencies are interested in programmatically monitoring Twitter (i.e. disaster relief organizations and news agencies). But, it’s not always clear whether a person’s words are announcing a disaster. This project aims to build a machine-learning model that predicts which Tweets are about real disasters and which ones aren’t.

DATA

Each sample in the data has the following information:

The text of a tweet
A keyword from that tweet (although this may be blank!)
The location the tweet was sent from (may also be blank)

PROCESS

The development of a predictive model has involved the implementation of the following procedural steps:

Data Cleaning:

The tweets underwent multiple rounds of cleaning, encompassing tasks such as removing punctuations and eliminating non-English words.

Data Preparation:

Tweets have been vectorized and split into training and test sets.

Model Implementation:

A Naive Bayes model has been successfully implemented for the task, achieving a test accuracy of 78%.