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 Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsRegression Model for explained model(Details inside)Scikit Learn: KMeans Clustering 3D data over a time period (dimentionality reduction?)Techniques for dealing with unevenly spaced time series data that have missing time-stamps?How to predict Estimated Time for Arrival given only trajectory data and time?ML algorithm to predict the state of state of a device (ON/ OFF) based on day, time and locationRetrieve user features in real time from UserId for predictionCurse of dimensionality: Data set with 1 million rows, max number of features can I have approximately?Best model for Machine LearningHow decision trees work in PythonCan we predict when an event will occur in the future from time series data?
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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
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsRegression Model for explained model(Details inside)Scikit Learn: KMeans Clustering 3D data over a time period (dimentionality reduction?)Techniques for dealing with unevenly spaced time series data that have missing time-stamps?How to predict Estimated Time for Arrival given only trajectory data and time?ML algorithm to predict the state of state of a device (ON/ OFF) based on day, time and locationRetrieve user features in real time from UserId for predictionCurse of dimensionality: Data set with 1 million rows, max number of features can I have approximately?Best model for Machine LearningHow decision trees work in PythonCan we predict when an event will occur in the future from time series data?
$begingroup$
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
machine-learning scikit-learn predictive-modeling pandas
edited Apr 3 at 9:06
Harman
378212
asked Apr 3 at 2:28
David ArriagaDavid Arriaga
61
$endgroup$
add a comment |
$begingroup$
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
machine-learning scikit-learn predictive-modeling pandas
edited Apr 3 at 9:06
Harman
378212
asked Apr 3 at 2:28
David ArriagaDavid Arriaga
61
$endgroup$
add a comment |
$begingroup$
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
machine-learning scikit-learn predictive-modeling pandas
edited Apr 3 at 9:06
Harman
378212
asked Apr 3 at 2:28
David ArriagaDavid Arriaga
61
$endgroup$
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
machine-learning scikit-learn predictive-modeling pandas
machine-learning scikit-learn predictive-modeling pandas
edited Apr 3 at 9:06
Harman
378212
asked Apr 3 at 2:28
David ArriagaDavid Arriaga
61
edited Apr 3 at 9:06
Harman
378212
asked Apr 3 at 2:28
David ArriagaDavid Arriaga
61
edited Apr 3 at 9:06
Harman
378212
edited Apr 3 at 9:06
Harman
378212
edited Apr 3 at 9:06
Harman
378212
378212
asked Apr 3 at 2:28
David ArriagaDavid Arriaga
61
asked Apr 3 at 2:28
David ArriagaDavid Arriaga
61
asked Apr 3 at 2:28
David ArriagaDavid Arriaga
61
61
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
So, remember the uses of Regression and Classification. Basicaly if you want o predict a discrete variable you should use classification, if you want to predict a continuous variable you should use regression. You can also use Regression for discrete variables if you quantize it after using some threshoulding method or something like that.
Time and location are continuous variables, you will predict them with Regression.
Call_type is a discrete variable, you will predict it with Classification.
Full address is dependent on lat and long, you should probably remove this from your model.
answered Apr 3 at 13:32
Pedro Henrique MonfortePedro Henrique Monforte
541117
$endgroup$
$begingroup$
It might make sense for OP to bin location into regions (streets, districts etc) in which case the transformed feature would fall under classification.
$endgroup$
– ukemi
Apr 3 at 14:08
$begingroup$
The problem in that is that it will generate too many classes and lat and long can be mapped back into regions
$endgroup$
– Pedro Henrique Monforte
Apr 3 at 14:13
$begingroup$
But, yes if want to go that way it is totally valid
$endgroup$
– Pedro Henrique Monforte
Apr 3 at 14:13
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
So, remember the uses of Regression and Classification. Basicaly if you want o predict a discrete variable you should use classification, if you want to predict a continuous variable you should use regression. You can also use Regression for discrete variables if you quantize it after using some threshoulding method or something like that.
Time and location are continuous variables, you will predict them with Regression.
Call_type is a discrete variable, you will predict it with Classification.
Full address is dependent on lat and long, you should probably remove this from your model.
answered Apr 3 at 13:32
Pedro Henrique MonfortePedro Henrique Monforte
541117
$endgroup$
$begingroup$
It might make sense for OP to bin location into regions (streets, districts etc) in which case the transformed feature would fall under classification.
$endgroup$
– ukemi
Apr 3 at 14:08
$begingroup$
The problem in that is that it will generate too many classes and lat and long can be mapped back into regions
$endgroup$
– Pedro Henrique Monforte
Apr 3 at 14:13
$begingroup$
But, yes if want to go that way it is totally valid
$endgroup$
– Pedro Henrique Monforte
Apr 3 at 14:13
add a comment |
$begingroup$
So, remember the uses of Regression and Classification. Basicaly if you want o predict a discrete variable you should use classification, if you want to predict a continuous variable you should use regression. You can also use Regression for discrete variables if you quantize it after using some threshoulding method or something like that.
Time and location are continuous variables, you will predict them with Regression.
Call_type is a discrete variable, you will predict it with Classification.
Full address is dependent on lat and long, you should probably remove this from your model.
answered Apr 3 at 13:32
Pedro Henrique MonfortePedro Henrique Monforte
541117
$endgroup$
$begingroup$
It might make sense for OP to bin location into regions (streets, districts etc) in which case the transformed feature would fall under classification.
$endgroup$
– ukemi
Apr 3 at 14:08
$begingroup$
The problem in that is that it will generate too many classes and lat and long can be mapped back into regions
$endgroup$
– Pedro Henrique Monforte
Apr 3 at 14:13
$begingroup$
But, yes if want to go that way it is totally valid
$endgroup$
– Pedro Henrique Monforte
Apr 3 at 14:13
add a comment |
$begingroup$
So, remember the uses of Regression and Classification. Basicaly if you want o predict a discrete variable you should use classification, if you want to predict a continuous variable you should use regression. You can also use Regression for discrete variables if you quantize it after using some threshoulding method or something like that.
Time and location are continuous variables, you will predict them with Regression.
Call_type is a discrete variable, you will predict it with Classification.
Full address is dependent on lat and long, you should probably remove this from your model.
answered Apr 3 at 13:32
Pedro Henrique MonfortePedro Henrique Monforte
541117
$endgroup$
So, remember the uses of Regression and Classification. Basicaly if you want o predict a discrete variable you should use classification, if you want to predict a continuous variable you should use regression. You can also use Regression for discrete variables if you quantize it after using some threshoulding method or something like that.
Time and location are continuous variables, you will predict them with Regression.
Call_type is a discrete variable, you will predict it with Classification.
Full address is dependent on lat and long, you should probably remove this from your model.
answered Apr 3 at 13:32
Pedro Henrique MonfortePedro Henrique Monforte
541117
answered Apr 3 at 13:32
Pedro Henrique MonfortePedro Henrique Monforte
541117
answered Apr 3 at 13:32
Pedro Henrique MonfortePedro Henrique Monforte
541117
answered Apr 3 at 13:32
Pedro Henrique MonfortePedro Henrique Monforte
541117
541117
$begingroup$
It might make sense for OP to bin location into regions (streets, districts etc) in which case the transformed feature would fall under classification.
$endgroup$
– ukemi
Apr 3 at 14:08
$begingroup$
The problem in that is that it will generate too many classes and lat and long can be mapped back into regions
$endgroup$
– Pedro Henrique Monforte
Apr 3 at 14:13
$begingroup$
But, yes if want to go that way it is totally valid
$endgroup$
– Pedro Henrique Monforte
Apr 3 at 14:13
add a comment |
$begingroup$
It might make sense for OP to bin location into regions (streets, districts etc) in which case the transformed feature would fall under classification.
$endgroup$
– ukemi
Apr 3 at 14:08
$begingroup$
The problem in that is that it will generate too many classes and lat and long can be mapped back into regions
$endgroup$
– Pedro Henrique Monforte
Apr 3 at 14:13
$begingroup$
But, yes if want to go that way it is totally valid
$endgroup$
– Pedro Henrique Monforte
Apr 3 at 14:13
$begingroup$
It might make sense for OP to bin location into regions (streets, districts etc) in which case the transformed feature would fall under classification.
$endgroup$
– ukemi
Apr 3 at 14:08
$begingroup$
It might make sense for OP to bin location into regions (streets, districts etc) in which case the transformed feature would fall under classification.
$endgroup$
– ukemi
Apr 3 at 14:08
$begingroup$
The problem in that is that it will generate too many classes and lat and long can be mapped back into regions
$endgroup$
– Pedro Henrique Monforte
Apr 3 at 14:13
$begingroup$
The problem in that is that it will generate too many classes and lat and long can be mapped back into regions
$endgroup$
– Pedro Henrique Monforte
Apr 3 at 14:13
$begingroup$
But, yes if want to go that way it is totally valid
$endgroup$
– Pedro Henrique Monforte
Apr 3 at 14:13
$begingroup$
But, yes if want to go that way it is totally valid
$endgroup$
– Pedro Henrique Monforte
Apr 3 at 14:13
add a comment |
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