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bmarket/dataSet.csv

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+Subject,Category
+Could you please ,SR
+let me know,SR
+Missing,Incident
+Need to ,Incident
+not accessible,Incident
+Send me ,SR
+Provide,SR
+Not working,Incident
+dispointed,Incident
+please,SR
+Slowness,Incident
+Request to fix,SR
+Solution,SR
+Still,Incident

bmarket/test.py

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+import numpy as np
+import pandas as pd
+
+from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
+from sklearn.linear_model import SGDClassifier
+from sklearn.model_selection import GridSearchCV
+from sklearn.pipeline import Pipeline
+
+emails = pd.read_csv('dataSet.csv')
+em = emails.dropna(axis=0)
+
+categories = ['SR', 'Incident']
+
+pipeline = Pipeline([
+    ('vect', CountVectorizer()),
+    ('tfidf', TfidfTransformer()),
+    ('clf', SGDClassifier()),
+]);
+
+parameters = {
+    'vect__max_df': (0.5, 1.0),
+    'vect__max_features': (None, 1000, 5000),
+    'vect__ngram_range': ((1, 1), (1, 2)),
+    'tfidf__use_idf': (True, False),
+    'tfidf__norm': ('l1', 'l2'),
+    'clf__alpha': (0.1, 0.01, 0.001),
+    'clf__penalty': ('l2', 'elasticnet'),
+}
+
+grid_search = GridSearchCV(pipeline, parameters, n_jobs=-1, verbose=1, refit=True)
+grid_search.fit(np.array(em['Subject']), np.array(em['Category']))
+best_parameters = grid_search.best_estimator_.get_params()
+
+input_test = input("Please enter the new content here: ")
+
+if input_test :
+    test_set = [input_test]
+    print("Prediction:", *grid_search.best_estimator_.predict(np.array(test_set)))
+