I am a beginner to data science. I have this dataset on natural disaster events in Afghanistan from 2016 - 2017. Columns:
- REGION (ex. North, North West, etc)
- PROVINCE_NAME (kind of like US 50 states)
- DISTRICT_NAME (kind of like US counties)
- INCIDENT_DATE (5 types: Flood, Earthquake, Land slide, Avalanche, and Heavy Snowfall)
I need to do any basic ML model on this dataset. I thought of predicting the disaster type given the other features using classification, or predicting the number of persons killed using regression. But I think some of these ideas are silly because they aren't useful in real life. For example, if I'm predicting Persons_killed, would I realistically have access to Persons_injured? (I don't know, if you have a good scientific question I can answer using regression etc, please let me know.)
A more meaningful experiment to try might be clustering. Since clustering is unsupervised, I'm just looking for any patterns. Does this mean I put all 13 columns into my model? I am a bit stuck on how to design this model but here is my thought process:
- I have checked my dataset for missing values, typos, etc. I have done some EDA.
- Do I need to encode categorical vars like REGION, PROVINCE_NAME, DISTRICT_NAME, and INCIDENT_TYPE? And what do I do with INCIDENT_DATE? Should I make a new feature called "Season", since I am not sure how to work with dates, or if I should just leave it out?
- Another issue is there are 2 natural disasters that are outliers (Earthquakes), they had a very large number of Persons_killed. This was factual, so would I leave these in the dataset or remove them? Because they cause the plot to zoom out very far and then you can't see the other data.
- I would then scale the data using StandardScaler (am I using all 13 columns in my model?)
- I don't fully understand dimensionality reduction with PCA, so I may leave this out for now and repeat this whole experiment with applying PCA at this step.
- Then I would create the model object and fit the model to the scaled data, and predict the cluster assignments. For the number of clusters, I could try a random number to start, but in class I learned about silhouette analysis and using a range of k to loop through and find the best k value.
- Now I'm confused on how to analyze the clusters. I would be analyzing the characteristics of each cluster. Perhaps use mean values of features. I am not sure how I would look at this on a map to see geographic patterns.
I apologize, as a beginner this is my first project, I would appreciate ANY advice, even if you cannot answer the whole series of questions.