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+title = "🌪️ Classifying Disaster Tweets with Deep Learning"
+description = "A comparative study of GRU and LSTM architectures for binary classification of disaster-related tweets."
+date = 2025-11-03
+[taxonomies]
+tags = ["machine_learning"]
+[extra]
+styles = ["notebooks.css", ]
++++
+
+## Project Overview
+
+Social media platforms like Twitter often serve as early indicators of
+real-world events. This project explores how machine learning can be used to
+automatically classify tweets as either disaster-related or not—an application
+with potential value for emergency response teams, journalists, and public
+safety organizations.
+
+The dataset comes from the Kaggle NLP with Disaster Tweets competition, and
+includes over 7,600 labeled tweets. The challenge lies in the ambiguity of
+language: disaster-related terms are often used metaphorically, making
+classification non-trivial.
+
+## Approach
+
+The analysis began with a baseline model using metadata features (like location
+and keywords), followed by a more advanced pipeline using text-based features
+and pretrained GloVe embeddings. The text was cleaned and tokenized to align
+with GloVe’s training conventions, and padded for input into recurrent neural
+networks.
+
+Two types of RNN architectures were explored:
+
+* **LSTM (Long Short-Term Memory)**
+* **GRU (Gated Recurrent Unit)**
+
+Each was tested across multiple configurations, varying the number of layers,
+units, and dropout rates. Early stopping and custom callbacks were used to
+optimize for F1-score, which was chosen as the primary evaluation metric.
+
+## Key Findings
+
+* **GRU outperformed LSTM**, achieving an F1-score of **0.857** and an accuracy
+of **88.2%**.
+* **Dropout** helped reduce overfitting but significantly increased training
+time due to limitations in GPU optimization.
+* **Model performance fluctuated**, suggesting that randomness and training
+dynamics played a larger role than expected.
+* **Metadata alone** (via Random Forest) was insufficient, with an F1-score of
+just **0.617**.
+
+## Reflections
+
+While GRU emerged as the best model, the results showed sensitivity to
+hyperparameters and training conditions. Dropout and early stopping helped
+mitigate overfitting, but the trade-offs in training time and reproducibility
+were notable.
+
+Future improvements could include:
+
+* Exploring alternative regularization techniques
+* Leveraging attention mechanisms or transformer-based models
+* Incorporating tweet length and other linguistic features more explicitly
+
+***
+
+If you're curious about the details, the full notebook is embedded below 👇
+
+<!-- markdownlint-disable MD033 -->
+<iframe title="Spam Email Classification notebook" class="notebook-embed" src="notebook.html"></iframe>
+
+You can also view the notebook in [a separate page](notebook.html), or check it
+on [GitHub](https://github.com/Farzat07/Kaggle-Mini-Project-NLP-Disaster-Tweets).