Empowering Health and Safety at Aurum Mining Corporation

What I have done:
In this project, I focused on analyzing datasets from a specific mining accident dataset using text mining techniques. My goal was twofold: first, to identify patterns and relationships within the textual data by extracting features such as word frequencies and common phrases; second, to apply machine learning models to predict accident types based on contextual information.

What i have used:
To achieve this, I utilized a variety of tools and techniques that are essential for text mining:

• Text Processing Tools: I performed basic text cleaning steps like tokenization and removal of irrelevant words.
• NLP Techniques:
  • TF-IDF (Term Frequency-Inverse Document Frequency): To quantify the importance of each word in the dataset.
  • Latent Dirichlet Allocation (LDA): To uncover hidden topics or categories within the text data.
  • Text Classification Models: I trained logistic regression and neural networks to classify accident types based on contextual features.
• Visualization Tools:
  • Wordclouds: To visualize the most common words and phrases in the dataset, such as "fingers (thumbs)" for thumb-pointing accidents or "back region" for back-side injuries.

What is the thing in there:
The key aspect of this project was its practical application. I analyzed real-world data from a mining accident to uncover patterns and relationships that could inform safety measures. For example, I identified that certain words like "fingers (thumbs)" were frequently associated with thumb-pointing accidents, which could help improve training programs for workers at the mine site.