I have this data set for crimes of a 12 month time period, over 250k rows. I want to predict future crimes by date and location I have this 250k data set with these features 

    date_time       FullAddress             call_type priority   lat       long
0   6/14/17 21:54   10 14TH ST\, San Diego\, CA 1151    2.0 32.705449   -117.151870
1   3/29/17 22:24   10 14TH ST\, San Diego\, CA 1016    2.0 32.705449   -117.151870
2   6/3/17 18:04    10 14TH ST\, San Diego\, CA 1016    2.0 32.705449   -117.151870
3   3/17/17 10:57   10 14TH ST\, San Diego\, CA 1151    2.0 32.705449   -117.151870
4   3/3/17 23:45    10 15TH ST\, San Diego\, CA 911P    2.0 32.705722   -117.15035


Date and time , full address , lat and long , and call type , and level of the seriousness of the crime. 
I want to predict the time when Future crimes will happen or predict the location it will happen again. How can I make that happen, will I use regression or classification? I already predicted the priority, but how can I predict the time it will happen or the location? 

I predicted the priority but doesn't really give me anything. I want to predict time and location or either or. 

this is some code i have for my priority prediction 

from sklearn.ensemble import RandomForestClassifier
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
my_RandomForest = RandomForestClassifier(n_estimators=100, random_state=0)

my_RandomForest.fit(X_train, y_train)
y_predict_fr = my_RandomForest.predict(X_test)
from sklearn.metrics import accuracy_score
print(y_predict_fr)
accuracy_fr = accuracy_score(y_test, y_predict_fr)
print(accuracy_fr)

[4. 3. 2. ... 3. 1. 2.]
0.95100761598545
Also I would say think about the implications of your model (even if it is just some toy data). 

If your model has overtly data from one location and not the other you might end up introducing some bias in your data - some crimes are not always known to the authorities. Crime is a very complex topics are your features relevent to the problem? Maybe some other features should be in the dataset and they are not?

Models applied in "high stake scenarios" often discussed on topics such as the fairness of AI should be very carefully validated. Once again that is just some toy data, but I thought it was noteworthy.