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Find suitable locations using Machine Learning
The 2019 Stack Overflow Developer Survey Results Are InDetecting Spam using Machine LearningUsing Machine Learning to Predict Musical ScalesAbbreviation Classification using machine learningSchema matching using machine learningUsing machine learning technique to predict commodity pricesFind which type of log using machine learning?Data Matching Using Machine LearningFraud detection using machine learningSports prediction using machine learningFind best machine learning for predicting category of products
$begingroup$
Just for fun, I am currently trying to find suitable locations to deploy new stores. So what I did so far is to take the actual sites of current stores and to assign surrounding variables to it. These features include for example: point of interest density, population density, region popularity etc. In total I have 9000, 100 dimensional points. 1000 of these points contain stores already, the remaining 8000 do not.
In the next step I want to perform dim reduction using PCA. However, I am not sure how to proceed afterwards. Should I try to cluster the points? Or how can I „predict“ which of the points are suitable candidates for new stores? Maybe using some kind of skip gram model?
Hoping to get some advise:)
Cheers,
Tom
machine-learning classification prediction
asked Mar 10 at 15:35
LossaLossa
11
$endgroup$
bumped to the homepage by Community♦ 9 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
Just for fun, I am currently trying to find suitable locations to deploy new stores. So what I did so far is to take the actual sites of current stores and to assign surrounding variables to it. These features include for example: point of interest density, population density, region popularity etc. In total I have 9000, 100 dimensional points. 1000 of these points contain stores already, the remaining 8000 do not.
In the next step I want to perform dim reduction using PCA. However, I am not sure how to proceed afterwards. Should I try to cluster the points? Or how can I „predict“ which of the points are suitable candidates for new stores? Maybe using some kind of skip gram model?
Hoping to get some advise:)
Cheers,
Tom
machine-learning classification prediction
asked Mar 10 at 15:35
LossaLossa
11
$endgroup$
bumped to the homepage by Community♦ 9 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
Just for fun, I am currently trying to find suitable locations to deploy new stores. So what I did so far is to take the actual sites of current stores and to assign surrounding variables to it. These features include for example: point of interest density, population density, region popularity etc. In total I have 9000, 100 dimensional points. 1000 of these points contain stores already, the remaining 8000 do not.
In the next step I want to perform dim reduction using PCA. However, I am not sure how to proceed afterwards. Should I try to cluster the points? Or how can I „predict“ which of the points are suitable candidates for new stores? Maybe using some kind of skip gram model?
Hoping to get some advise:)
Cheers,
Tom
machine-learning classification prediction
asked Mar 10 at 15:35
LossaLossa
11
$endgroup$
Just for fun, I am currently trying to find suitable locations to deploy new stores. So what I did so far is to take the actual sites of current stores and to assign surrounding variables to it. These features include for example: point of interest density, population density, region popularity etc. In total I have 9000, 100 dimensional points. 1000 of these points contain stores already, the remaining 8000 do not.
In the next step I want to perform dim reduction using PCA. However, I am not sure how to proceed afterwards. Should I try to cluster the points? Or how can I „predict“ which of the points are suitable candidates for new stores? Maybe using some kind of skip gram model?
Hoping to get some advise:)
Cheers,
Tom
machine-learning classification prediction
machine-learning classification prediction
asked Mar 10 at 15:35
LossaLossa
11
asked Mar 10 at 15:35
LossaLossa
11
asked Mar 10 at 15:35
LossaLossa
11
asked Mar 10 at 15:35
LossaLossa
11
asked Mar 10 at 15:35
LossaLossa
11
11
bumped to the homepage by Community♦ 9 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 9 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
Are you sure PCA is the correct way to go?
It's an analytical problem and being able to interpret the results are very important.
How about the correlation between the number of stores and nearby features? Find out what makes a good location. What are the most important features? Run forward or backward selection as an example, or use another model/feature selection technique.
It's not a pure machine learning case you have here. It's a typical analytical data science problem.
If you still want to do classification, just train a model. You have POI features and some others. You know if there is a store or not :) I might not fully understand the problem here. You train on a 50% a store exist location, and 50% a store does not exist in this location dataset. Train a classifier, and classify other areas.
I'd still start to visualize and understand the data as I mentioned first. It's much underrated and the way to start solving most problems.
Hope that gave you some hints,
Cheers
answered Mar 10 at 16:43
Carl RynegardhCarl Rynegardh
321112
$endgroup$
$begingroup$
Hi Carl, I mean I can still interpret PCA using a correlation analysis between the principal components and the original variables right? This should help to get an idea of how the data looks like. Still it would be a nice idea to use the analytical solution to validate the classification result. Thx for your help!
$endgroup$
– Lossa
Mar 10 at 17:18
$begingroup$
You can look at how much each feature adds to the principal components. I would not call that correlation analysis, but maybe that is something you can do. Looking at how much each feature adds to the principal components is not always very interpretable.
$endgroup$
– Carl Rynegardh
Mar 10 at 18:49
add a comment |
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$begingroup$
Are you sure PCA is the correct way to go?
It's an analytical problem and being able to interpret the results are very important.
How about the correlation between the number of stores and nearby features? Find out what makes a good location. What are the most important features? Run forward or backward selection as an example, or use another model/feature selection technique.
It's not a pure machine learning case you have here. It's a typical analytical data science problem.
If you still want to do classification, just train a model. You have POI features and some others. You know if there is a store or not :) I might not fully understand the problem here. You train on a 50% a store exist location, and 50% a store does not exist in this location dataset. Train a classifier, and classify other areas.
I'd still start to visualize and understand the data as I mentioned first. It's much underrated and the way to start solving most problems.
Hope that gave you some hints,
Cheers
answered Mar 10 at 16:43
Carl RynegardhCarl Rynegardh
321112
$endgroup$
$begingroup$
Hi Carl, I mean I can still interpret PCA using a correlation analysis between the principal components and the original variables right? This should help to get an idea of how the data looks like. Still it would be a nice idea to use the analytical solution to validate the classification result. Thx for your help!
$endgroup$
– Lossa
Mar 10 at 17:18
$begingroup$
You can look at how much each feature adds to the principal components. I would not call that correlation analysis, but maybe that is something you can do. Looking at how much each feature adds to the principal components is not always very interpretable.
$endgroup$
– Carl Rynegardh
Mar 10 at 18:49
add a comment |
$begingroup$
Are you sure PCA is the correct way to go?
It's an analytical problem and being able to interpret the results are very important.
How about the correlation between the number of stores and nearby features? Find out what makes a good location. What are the most important features? Run forward or backward selection as an example, or use another model/feature selection technique.
It's not a pure machine learning case you have here. It's a typical analytical data science problem.
If you still want to do classification, just train a model. You have POI features and some others. You know if there is a store or not :) I might not fully understand the problem here. You train on a 50% a store exist location, and 50% a store does not exist in this location dataset. Train a classifier, and classify other areas.
I'd still start to visualize and understand the data as I mentioned first. It's much underrated and the way to start solving most problems.
Hope that gave you some hints,
Cheers
answered Mar 10 at 16:43
Carl RynegardhCarl Rynegardh
321112
$endgroup$
$begingroup$
Hi Carl, I mean I can still interpret PCA using a correlation analysis between the principal components and the original variables right? This should help to get an idea of how the data looks like. Still it would be a nice idea to use the analytical solution to validate the classification result. Thx for your help!
$endgroup$
– Lossa
Mar 10 at 17:18
$begingroup$
You can look at how much each feature adds to the principal components. I would not call that correlation analysis, but maybe that is something you can do. Looking at how much each feature adds to the principal components is not always very interpretable.
$endgroup$
– Carl Rynegardh
Mar 10 at 18:49
add a comment |
$begingroup$
Are you sure PCA is the correct way to go?
It's an analytical problem and being able to interpret the results are very important.
How about the correlation between the number of stores and nearby features? Find out what makes a good location. What are the most important features? Run forward or backward selection as an example, or use another model/feature selection technique.
It's not a pure machine learning case you have here. It's a typical analytical data science problem.
If you still want to do classification, just train a model. You have POI features and some others. You know if there is a store or not :) I might not fully understand the problem here. You train on a 50% a store exist location, and 50% a store does not exist in this location dataset. Train a classifier, and classify other areas.
I'd still start to visualize and understand the data as I mentioned first. It's much underrated and the way to start solving most problems.
Hope that gave you some hints,
Cheers
answered Mar 10 at 16:43
Carl RynegardhCarl Rynegardh
321112
$endgroup$
Are you sure PCA is the correct way to go?
It's an analytical problem and being able to interpret the results are very important.
How about the correlation between the number of stores and nearby features? Find out what makes a good location. What are the most important features? Run forward or backward selection as an example, or use another model/feature selection technique.
It's not a pure machine learning case you have here. It's a typical analytical data science problem.
If you still want to do classification, just train a model. You have POI features and some others. You know if there is a store or not :) I might not fully understand the problem here. You train on a 50% a store exist location, and 50% a store does not exist in this location dataset. Train a classifier, and classify other areas.
I'd still start to visualize and understand the data as I mentioned first. It's much underrated and the way to start solving most problems.
Hope that gave you some hints,
Cheers
answered Mar 10 at 16:43
Carl RynegardhCarl Rynegardh
321112
answered Mar 10 at 16:43
Carl RynegardhCarl Rynegardh
321112
answered Mar 10 at 16:43
Carl RynegardhCarl Rynegardh
321112
answered Mar 10 at 16:43
Carl RynegardhCarl Rynegardh
321112
321112
$begingroup$
Hi Carl, I mean I can still interpret PCA using a correlation analysis between the principal components and the original variables right? This should help to get an idea of how the data looks like. Still it would be a nice idea to use the analytical solution to validate the classification result. Thx for your help!
$endgroup$
– Lossa
Mar 10 at 17:18
$begingroup$
You can look at how much each feature adds to the principal components. I would not call that correlation analysis, but maybe that is something you can do. Looking at how much each feature adds to the principal components is not always very interpretable.
$endgroup$
– Carl Rynegardh
Mar 10 at 18:49
add a comment |
$begingroup$
Hi Carl, I mean I can still interpret PCA using a correlation analysis between the principal components and the original variables right? This should help to get an idea of how the data looks like. Still it would be a nice idea to use the analytical solution to validate the classification result. Thx for your help!
$endgroup$
– Lossa
Mar 10 at 17:18
$begingroup$
You can look at how much each feature adds to the principal components. I would not call that correlation analysis, but maybe that is something you can do. Looking at how much each feature adds to the principal components is not always very interpretable.
$endgroup$
– Carl Rynegardh
Mar 10 at 18:49
$begingroup$
Hi Carl, I mean I can still interpret PCA using a correlation analysis between the principal components and the original variables right? This should help to get an idea of how the data looks like. Still it would be a nice idea to use the analytical solution to validate the classification result. Thx for your help!
$endgroup$
– Lossa
Mar 10 at 17:18
$begingroup$
Hi Carl, I mean I can still interpret PCA using a correlation analysis between the principal components and the original variables right? This should help to get an idea of how the data looks like. Still it would be a nice idea to use the analytical solution to validate the classification result. Thx for your help!
$endgroup$
– Lossa
Mar 10 at 17:18
$begingroup$
You can look at how much each feature adds to the principal components. I would not call that correlation analysis, but maybe that is something you can do. Looking at how much each feature adds to the principal components is not always very interpretable.
$endgroup$
– Carl Rynegardh
Mar 10 at 18:49
$begingroup$
You can look at how much each feature adds to the principal components. I would not call that correlation analysis, but maybe that is something you can do. Looking at how much each feature adds to the principal components is not always very interpretable.
$endgroup$
– Carl Rynegardh
Mar 10 at 18:49
add a comment |
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