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Classification (1).py
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Classification (1).py
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#!/usr/bin/env python
# coding: utf-8
# In[1]:
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split
from sklearn.linear_model import SGDClassifier
from sklearn.pipeline import Pipeline
from sklearn.externals import joblib
# In[2]:
def general_classification(data):
df=pd.read_csv(r'/home/aakash/Downloads/Anxiety - Sheet1.csv')
X=df.Questions
y = df.type
text_clf_svm = Pipeline([('vect', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf-svm', SGDClassifier(loss='hinge', penalty='l2',
alpha=1e-3, n_iter=5, random_state=42)),
])
text_clf_svm.fit(X,y)
predicted_svm = text_clf_svm.predict(data)
predicted_svm=joblib.dumb(predicted_svm,'General_classifiaction.plk')
return predicted_svm
# In[3]:
def GAD(data):
#df=pd.read_csv(r'')
#X=df.Questions
#y = df.type
#text_clf_svm = Pipeline([('vect', CountVectorizer()),
# ('tfidf', TfidfTransformer()),
# ('clf-svm', SGDClassifier(loss='hinge', penalty='l2',
# alpha=1e-3, n_iter=5, random_state=42)),
#])
#text_clf_svm.fit(X,y)
#predicted_svm = text_clf_svm.predict(data)
#return predicted_svm
return
# In[4]:
def emotional_change(data):
#df=pd.read_csv(r'')
#X=df.Questions
#y = df.type
#text_clf_svm = Pipeline([('vect', CountVectorizer()),
# ('tfidf', TfidfTransformer()),
# ('clf-svm', SGDClassifier(loss='hinge', penalty='l2',
# alpha=1e-3, n_iter=5, random_state=42)),
#])
#text_clf_svm.fit(X,y)
#predicted_svm = text_clf_svm.predict(data)
#return predicted_svm
return ""
# In[5]:
def physical_change(data):
#df=pd.read_csv(r'')
#X=df.Questions
#y = df.type
#text_clf_svm = Pipeline([('vect', CountVectorizer()),
# ('tfidf', TfidfTransformer()),
# ('clf-svm', SGDClassifier(loss='hinge', penalty='l2',
# alpha=1e-3, n_iter=5, random_state=42)),
#])
#text_clf_svm.fit(X,y)
#predicted_svm = text_clf_svm.predict(data)
#return predicted_svm
return ""
# In[6]:
def social_change(data):
#df=pd.read_csv(r'')
#X=df.Questions
#y = df.type
#text_clf_svm = Pipeline([('vect', CountVectorizer()),
# ('tfidf', TfidfTransformer()),
# ('clf-svm', SGDClassifier(loss='hinge', penalty='l2',
# alpha=1e-3, n_iter=5, random_state=42)),
#])
#text_clf_svm.fit(X,y)
#predicted_svm = text_clf_svm.predict(data)
#return predicted_svm
return ""
# In[ ]:
# In[7]:
def sleep_disturbances(data):
#df=pd.read_csv(r'')
#X=df.Questions
#y = df.type
#text_clf_svm = Pipeline([('vect', CountVectorizer()),
# ('tfidf', TfidfTransformer()),
# ('clf-svm', SGDClassifier(loss='hinge', penalty='l2',
# alpha=1e-3, n_iter=5, random_state=42)),
#])
#text_clf_svm.fit(X,y)
#predicted_svm = text_clf_svm.predict(data)
#return predicted_svm
return ""
# In[8]:
def poor_school_performance(data):
#df=pd.read_csv(r'')
#X=df.Questions
#y = df.type
#text_clf_svm = Pipeline([('vect', CountVectorizer()),
# ('tfidf', TfidfTransformer()),
# ('clf-svm', SGDClassifier(loss='hinge', penalty='l2',
# alpha=1e-3, n_iter=5, random_state=42)),
#])
#text_clf_svm.fit(X,y)
#predicted_svm = text_clf_svm.predict(data)
#return predicted_svm
return ""
# In[9]:
def panic_attack(data):
#df=pd.read_csv(r'')
#X=df.Questions
#y = df.type
#text_clf_svm = Pipeline([('vect', CountVectorizer()),
# ('tfidf', TfidfTransformer()),
# ('clf-svm', SGDClassifier(loss='hinge', penalty='l2',
# alpha=1e-3, n_iter=5, random_state=42)),
#])
#text_clf_svm.fit(X,y)
#predicted_svm = text_clf_svm.predict(data)
#return predicted_svm
return ""