The dataset contains 57 columns, with the first row serving as the header. Some of the columns include MINE_ID, CONTROLLER_ID, CONTROLLER_NAME, OPERATOR_ID, OPERATOR_NAME, CONTRACTOR_ID, DOCUMENT_NO, SUBUNIT_CD, ACCIDENT_TYPE, and NARRATIVE among others.

To identify the most common accident types, we need to focus on the column that represents the accident type. Let's inspect the column names to identify the correct column for accident types.

It appears that the dataset does not have column names loaded correctly. We need to manually assign the column names based on the provided preview.

Let's assign the column names and then identify the most common accident types.

It seems there is an encoding issue with the file. Let's try loading the file with a different encoding.

The data has been successfully loaded. The dataset contains 57 columns, including information about the mine, operator, accident type, date, and more.

Let's now identify the most common accident types.

The most common accident types in the dataset are:

These are the top 10 most common accident types recorded in the dataset.

The plot displays the top 10 most common accident types in the dataset. The most frequent accident type is 'Underground', followed by 'Strip, Quarry, Open Pit', and 'Mill Operation/Preparation Plant'. The plot provides a visual representation of the accident type distribution, making it easier to understand the prevalence of each type.

Feel free to ask any more questions or request further analysis!

The plot displays the trend of mining accidents over the years. There is a noticeable decline in the number of accidents from 2000 to 2024. The highest number of accidents occurred in the early 2000s, with a gradual decrease over the years.

If you have any more questions or need further analysis, feel free to ask!