https://github.com/magnusrodseth/disaster-tweets

📚 Assignments in the course IT3212 - Data Driven Software at NTNU. Our task is to classify whether a tweet is related to a disaster or not.
https://github.com/magnusrodseth/disaster-tweets

adaboost jupyter-notebook logistic-regression numpy pandas python python3 random-forest support-vector-machines xgboost

Last synced: about 1 month ago
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📚 Assignments in the course IT3212 - Data Driven Software at NTNU. Our task is to classify whether a tweet is related to a disaster or not.

• Host: GitHub
• URL: https://github.com/magnusrodseth/disaster-tweets
• Owner: magnusrodseth
• Created: 2023-09-13T10:32:37.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-12-15T10:56:03.000Z (about 1 year ago)
• Last Synced: 2024-03-01T10:48:35.789Z (10 months ago)
• Topics: adaboost, jupyter-notebook, logistic-regression, numpy, pandas, python, python3, random-forest, support-vector-machines, xgboost
• Language: Jupyter Notebook
• Homepage:
• Size: 11.9 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
# Data Driven Software

## Description

This repository contains the code for the project in the course IT3212 - Data Driven Software at NTNU.

The rapid spread of information on social media platforms like Twitter is revolutionizing how we learn about emergencies and disasters. However, the sheer volume of data makes manual monitoring impractical. Hence, developing an automated system to filter and verify posts about genuine disaster events could be invaluable for different emergency services. This could enable faster responses to crises and help medical facilities prepare for possible surges in patient numbers, allowing for more efficient resource management in urgent situations.

**Based on this problem to solve, our task is to classify whether a tweet is related to a disaster or not.**

## Running the code

### Prerequisites for running the code

You can install dependencies however you like. However, it is recommended to use `conda`, as it comes with a lot of useful packages pre-installed. Read more about `conda` [here](https://docs.conda.io/en/latest/).

### Installing dependencies

```sh

# Navigate to the project directory

cd it3212

# Create a new conda environment

conda create --name it3212

# Activate the environment

conda activate it3212

# Install dependencies

conda install --file requirements.txt

```

### Executing the code

It is recommended to run the Jupyter Notebook in VSCode. Ensure the `it3212` conda environment is selected as the Python interpreter.

## Developer Information

Developed by [Haakon Tideman Kanter](https://github.com/haakonka), [Henrik Skog](https://github.com/henrikskog), [Mattis Hembre](https://github.com/CzternastyHembre), [Max Gunhamn](https://github.com/maxhamn), [Sebastian Sole](https://github.com/Sebastian-Sole), and [Magnus Rødseth](https://github.com/magnusrodseth).