This model will use the information about power usage to predict what type of landmine is being described. This uses information from the Land Mines dataset from UC Irvine Machine Learning Repository.

This model was created for a university assignment and is not ready to be used for production purposes.

from huggingface_hub import hf_hub_download
hf_hub_download(repo_id="CSC310-fall25/landmines_clustering_training", filename="model.pkl",local_dir='.')
dt_loaded = sio.load('model.pkl')

Data Source: UCI Machine Learning Repository

There were no missing values in this dataset and there was no otlier detection or removal done on the dataset. I selected the features by taking the first two columns with float values, and I removed the column with the target variable from the lm_X set alltogether. There was also no scaling or normalization done to the dataset. I used the KMeans method to cluster this data as it is what we learned in class. The K value 5 was selected because there are 5 mine types described in the data initially. This model was trained using many packages including seaborn, numpy, sklearn, pandas and matplotlib. There are two random seeds used, 1108 and 118.

Land Mines Data Set:
lm_X size: (338, 3)
V: float64
H: float64
S: float64
lm_y size: (338,1)
M: int64

There is no senstive data nor missing data in this dataset originally.

This data doesn't form very spherical or separated clusters. A lot of the clusters seem to layer on top of eachother in some way. Due to the silhouette score, you may thing that there are well separated and dense clusters, but the ARI and NMI both point to there being no relationship between the predicted and the true labels.

Silhouette Score: 0.324
Adjusted Rand Index (ARI): -0.027
Normalized Mutual Information (NMI): 0.049

Brianna Landry

Contact Brianna Landry @:
briannalandry@uri.edu
briannalandry24@gmail.com
github: briannalandry8