How to pass inputs (interactively) to a model?
$begingroup$
Let me give you a high-level design (blueprint) of my model.
Input data::
CATEGORY SERVICE TITLE DEPARTMENT
apple fruits i love eating fruits. fruitshop
mango fruits mangoes are yellow in color. fruitshop
cycle vehicle that cycle is really expensive. motorshop
I am trying to predict the value of DEPARTMENT (i.e. my output class), by consuming - CATEGORY, SERVICE and TITLE - as my input classes.
Pseudo code::
Converted CATEGORY and SERVICE columns into fixed numerical values -
df['CATEGORY'], mapp0 = df['CATEGORY'].factorize()
df['SERVICE'], mapp1 = df['SERVICE'].factorize()
For the TITLE, used NLTK and Tf-IDf techniques to create a sparse matrix with the individual word (tokens) as features and the weights forming the matrix values.
Then outputs of 1 and 2 are combined to form another sparse matrix, which is my main feature matrix (It has approximately 1100 features).
I'm so new to ML that not even sure if step#3 is even allowed (or not even a standard practice).Next, the usual steps are performed like CV, fit, predict, etc.
I am using LinearSVC and StratifiedKFold (n_splits = 10, random_state=777, shuffle=True) able to achieve a prediction accuracy of 73%.
Question::
I want to pass inputs (CATEGORY, SERVICE and TITLE) to my model and it should predict the DEPARTMENT. How can I achieve this part?
scikit-learn predictive-modeling pandas multiclass-classification
edited yesterday
Stephen Rauch♦
1,52551330
asked yesterday
ranit.branit.b
808
$endgroup$
add a comment |
$begingroup$
Let me give you a high-level design (blueprint) of my model.
Input data::
CATEGORY SERVICE TITLE DEPARTMENT
apple fruits i love eating fruits. fruitshop
mango fruits mangoes are yellow in color. fruitshop
cycle vehicle that cycle is really expensive. motorshop
I am trying to predict the value of DEPARTMENT (i.e. my output class), by consuming - CATEGORY, SERVICE and TITLE - as my input classes.
Pseudo code::
Converted CATEGORY and SERVICE columns into fixed numerical values -
df['CATEGORY'], mapp0 = df['CATEGORY'].factorize()
df['SERVICE'], mapp1 = df['SERVICE'].factorize()
For the TITLE, used NLTK and Tf-IDf techniques to create a sparse matrix with the individual word (tokens) as features and the weights forming the matrix values.
Then outputs of 1 and 2 are combined to form another sparse matrix, which is my main feature matrix (It has approximately 1100 features).
I'm so new to ML that not even sure if step#3 is even allowed (or not even a standard practice).Next, the usual steps are performed like CV, fit, predict, etc.
I am using LinearSVC and StratifiedKFold (n_splits = 10, random_state=777, shuffle=True) able to achieve a prediction accuracy of 73%.
Question::
I want to pass inputs (CATEGORY, SERVICE and TITLE) to my model and it should predict the DEPARTMENT. How can I achieve this part?
scikit-learn predictive-modeling pandas multiclass-classification
edited yesterday
Stephen Rauch♦
1,52551330
asked yesterday
ranit.branit.b
808
$endgroup$
$begingroup$
What kind of model do you have? scikit classifier, XGBoost, LGBM, Tensorflow, Keras or what?
$endgroup$
– Simon Larsson
yesterday
$begingroup$
Thanks Simon. Amidst the long post, had forgotten to put the most important thing. I've edited my post now with the requested detail. I'm using LinearSVC() with StratifiedKFold (10 splits).
$endgroup$
– ranit.b
yesterday
$begingroup$
Any advise please...
$endgroup$
– ranit.b
1 hour ago
add a comment |
$begingroup$
Let me give you a high-level design (blueprint) of my model.
Input data::
CATEGORY SERVICE TITLE DEPARTMENT
apple fruits i love eating fruits. fruitshop
mango fruits mangoes are yellow in color. fruitshop
cycle vehicle that cycle is really expensive. motorshop
I am trying to predict the value of DEPARTMENT (i.e. my output class), by consuming - CATEGORY, SERVICE and TITLE - as my input classes.
Pseudo code::
Converted CATEGORY and SERVICE columns into fixed numerical values -
df['CATEGORY'], mapp0 = df['CATEGORY'].factorize()
df['SERVICE'], mapp1 = df['SERVICE'].factorize()
For the TITLE, used NLTK and Tf-IDf techniques to create a sparse matrix with the individual word (tokens) as features and the weights forming the matrix values.
Then outputs of 1 and 2 are combined to form another sparse matrix, which is my main feature matrix (It has approximately 1100 features).
I'm so new to ML that not even sure if step#3 is even allowed (or not even a standard practice).Next, the usual steps are performed like CV, fit, predict, etc.
I am using LinearSVC and StratifiedKFold (n_splits = 10, random_state=777, shuffle=True) able to achieve a prediction accuracy of 73%.
Question::
I want to pass inputs (CATEGORY, SERVICE and TITLE) to my model and it should predict the DEPARTMENT. How can I achieve this part?
scikit-learn predictive-modeling pandas multiclass-classification
edited yesterday
Stephen Rauch♦
1,52551330
asked yesterday
ranit.branit.b
808
$endgroup$
Let me give you a high-level design (blueprint) of my model.
Input data::
CATEGORY SERVICE TITLE DEPARTMENT
apple fruits i love eating fruits. fruitshop
mango fruits mangoes are yellow in color. fruitshop
cycle vehicle that cycle is really expensive. motorshop
I am trying to predict the value of DEPARTMENT (i.e. my output class), by consuming - CATEGORY, SERVICE and TITLE - as my input classes.
Pseudo code::
Converted CATEGORY and SERVICE columns into fixed numerical values -
df['CATEGORY'], mapp0 = df['CATEGORY'].factorize()
df['SERVICE'], mapp1 = df['SERVICE'].factorize()
For the TITLE, used NLTK and Tf-IDf techniques to create a sparse matrix with the individual word (tokens) as features and the weights forming the matrix values.
Then outputs of 1 and 2 are combined to form another sparse matrix, which is my main feature matrix (It has approximately 1100 features).
I'm so new to ML that not even sure if step#3 is even allowed (or not even a standard practice).Next, the usual steps are performed like CV, fit, predict, etc.
I am using LinearSVC and StratifiedKFold (n_splits = 10, random_state=777, shuffle=True) able to achieve a prediction accuracy of 73%.
Question::
I want to pass inputs (CATEGORY, SERVICE and TITLE) to my model and it should predict the DEPARTMENT. How can I achieve this part?
scikit-learn predictive-modeling pandas multiclass-classification
scikit-learn predictive-modeling pandas multiclass-classification
edited yesterday
Stephen Rauch♦
1,52551330
asked yesterday
ranit.branit.b
808
edited yesterday
Stephen Rauch♦
1,52551330
asked yesterday
ranit.branit.b
808
edited yesterday
Stephen Rauch♦
1,52551330
edited yesterday
Stephen Rauch♦
1,52551330
edited yesterday
Stephen Rauch♦
1,52551330
1,52551330
asked yesterday
ranit.branit.b
808
asked yesterday
ranit.branit.b
808
asked yesterday
ranit.branit.b
808
808
$begingroup$
What kind of model do you have? scikit classifier, XGBoost, LGBM, Tensorflow, Keras or what?
$endgroup$
– Simon Larsson
yesterday
$begingroup$
Thanks Simon. Amidst the long post, had forgotten to put the most important thing. I've edited my post now with the requested detail. I'm using LinearSVC() with StratifiedKFold (10 splits).
$endgroup$
– ranit.b
yesterday
$begingroup$
Any advise please...
$endgroup$
– ranit.b
1 hour ago
add a comment |
$begingroup$
What kind of model do you have? scikit classifier, XGBoost, LGBM, Tensorflow, Keras or what?
$endgroup$
– Simon Larsson
yesterday
$begingroup$
Thanks Simon. Amidst the long post, had forgotten to put the most important thing. I've edited my post now with the requested detail. I'm using LinearSVC() with StratifiedKFold (10 splits).
$endgroup$
– ranit.b
yesterday
$begingroup$
Any advise please...
$endgroup$
– ranit.b
1 hour ago
$begingroup$
What kind of model do you have? scikit classifier, XGBoost, LGBM, Tensorflow, Keras or what?
$endgroup$
– Simon Larsson
yesterday
$begingroup$
What kind of model do you have? scikit classifier, XGBoost, LGBM, Tensorflow, Keras or what?
$endgroup$
– Simon Larsson
yesterday
$begingroup$
Thanks Simon. Amidst the long post, had forgotten to put the most important thing. I've edited my post now with the requested detail. I'm using LinearSVC() with StratifiedKFold (10 splits).
$endgroup$
– ranit.b
yesterday
$begingroup$
Thanks Simon. Amidst the long post, had forgotten to put the most important thing. I've edited my post now with the requested detail. I'm using LinearSVC() with StratifiedKFold (10 splits).
$endgroup$
– ranit.b
yesterday
$begingroup$
Any advise please...
$endgroup$
– ranit.b
1 hour ago
$begingroup$
Any advise please...
$endgroup$
– ranit.b
1 hour ago
add a comment |
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$begingroup$
What kind of model do you have? scikit classifier, XGBoost, LGBM, Tensorflow, Keras or what?
$endgroup$
– Simon Larsson
yesterday
$begingroup$
Thanks Simon. Amidst the long post, had forgotten to put the most important thing. I've edited my post now with the requested detail. I'm using LinearSVC() with StratifiedKFold (10 splits).
$endgroup$
– ranit.b
yesterday
$begingroup$
Any advise please...
$endgroup$
– ranit.b
1 hour ago