This repository contains machine learning models and analyses for the Kaggle competition "Natural Language Processing with Disaster Tweets". The goal of the competition is to build a machine learning model that predicts which tweets are about real disasters and which ones are not.
The task is to create a machine learning model that can accurately classify text data—in this case, tweets—into two categories: tweets about real disasters and tweets that are not. This is a classic binary text classification problem with a real-world application of understanding how social media can play a crucial role in effective disaster response.
Here are the results of the models implemented so far:
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Logistic Regression: A simple yet powerful linear model used as a baseline for binary classification tasks. The model performed with an accuracy score of 0.73143 on the competition's test set.
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Naive Bayes: A probabilistic classifier based on applying Bayes' theorem with strong (naive) independence assumptions between the features. It is particularly suited for high-dimensional data, and it performed better than logistic regression with an accuracy score of 0.77873.
| Model | Accuracy Score |
|---|---|
| Logistic Regression | 0.73143 |
| Naive Bayes | 0.77873 |
Note: The accuracy scores are reflective of the model's performance on the Kaggle competition's test dataset.