Project Overview

The project aims to develop a machine learning model capable of classifying tweets as either "disaster-related" or "non-disaster-related." This analysis assists in automating the identification of critical information on social media, especially during emergencies, where quick detection of disaster-related content can significantly aid in crisis management.

Objective

The objective is to create a predictive model that achieves high accuracy in differentiating between disaster-related and non-disaster tweets, leveraging natural language processing (NLP) techniques and machine learning algorithms.

Methodology

• Data Preprocessing: Text preprocessing steps such as tokenization, cleaning, and vectorization were likely applied to transform tweet text into machine-readable features.
• Model Selection and Training: The notebook indicates a model training phase that utilizes accuracy metrics. The model was fine-tuned to ensure it balances recall and precision, particularly for disaster tweets.
• Evaluation Metrics: Performance was evaluated using accuracy, precision, recall, and F1-score. These metrics are essential in understanding how well the model distinguishes between disaster and non-disaster tweets, particularly focusing on disaster-related content.

Key Findings

• Model Performance: The model achieved an accuracy of 81%.
• Class-Specific Insights: The model performs better at identifying disaster tweets than non-disaster tweets, with higher recall and F1-scores for disaster tweets. This suggests that the model effectively captures critical tweets, minimizing false negatives.
• Overall Balance: Precision, recall, and F1-scores were reasonably balanced, indicating reliable and consistent performance across both classes.

This project highlights the model's potential for rapid identification of disaster-related content on social media, aiding in timely response and resource allocation during emergencies.

Pandas, nltk, matplotlib, keras, tensorflow, TextBlob