https://github.com/parneet-sandhu/nlp-kaggle-competition

Natural Language Processing with Disaster Tweets Predict which Tweets are about real disasters and which ones are not.I achieved Rank 15 with 0.87618 accuracy.
https://github.com/parneet-sandhu/nlp-kaggle-competition

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Natural Language Processing with Disaster Tweets Predict which Tweets are about real disasters and which ones are not.I achieved Rank 15 with 0.87618 accuracy.

• Host: GitHub
• URL: https://github.com/parneet-sandhu/nlp-kaggle-competition
• Owner: Parneet-Sandhu
• Created: 2024-07-27T15:13:17.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-08-20T02:55:00.000Z (5 months ago)
• Last Synced: 2024-08-21T06:02:30.508Z (5 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 1.81 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# NLP-Kaggle-Competition

I achieved Rank 15 with 0.87618 accuracy.

# Natural Language Processing with Disaster Tweets

Predict which Tweets are about real disasters and which ones are not

# Competition 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 agencies are interested in programatically monitoring Twitter (i.e. disaster relief organizations and news agencies). But, it’s not always clear whether a person’s words are actually announcing a disaster. Take this example:

The author explicitly uses the word “ABLAZE” but means it metaphorically. This is clear to a human right away, especially with the visual aid. But it’s less clear to a machine.

In this competition, you’re challenged to build a machine learning model that predicts which Tweets are about real disasters and which one’s aren’t. You’ll have access to a dataset of 10,000 tweets that were hand classified. If this is your first time working on an NLP problem, we've created a quick tutorial to get you up and running.

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

# Submission File

For each ID in the test set, you must predict 1 if the tweet is describing a real disaster, and 0 otherwise. The file should contain a header and have the following format:

#id,target

(0,0)

(2,0).....

# Visualization Using YData Profiling

https://github.com/user-attachments/assets/9557c7ba-3b04-4f0a-b531-aec2c28572c1

Using YData Profiling, I was able to quickly get a complete understanding of the dataset. The tool gave me a clear snapshot of important statistics, highlighted any data quality issues like missing values or duplicates, and provided visualizations that made it easier to see how the data was distributed.