--- a
+++ b/code/ModelTraining.py
@@ -0,0 +1,191 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+import warnings
+warnings.filterwarnings('ignore')
+
+X_bow_train = pd.read_csv('X_bow_train.csv')
+X_bow_test  = pd.read_csv('X_bow_test.csv')
+y_bow_train = pd.read_csv('y_bow_train.csv')
+y_bow_test  = pd.read_csv('y_bow_test.csv')
+
+
+X_tf_train = pd.read_csv('X_tf_train.csv')
+X_tf_test  = pd.read_csv('X_tf_test.csv')
+y_tf_train = pd.read_csv('y_tf_train.csv')
+y_tf_test  = pd.read_csv('y_tf_test.csv')
+
+X_hash_train = pd.read_csv('X_hash_train.csv')
+X_hash_test  = pd.read_csv('X_hash_test.csv')
+y_hash_train = pd.read_csv('y_hash_train.csv')
+y_hash_test  = pd.read_csv('y_hash_test.csv')
+
+X_w2v_train = pd.read_csv('X_w2v_train.csv')
+X_w2v_test  = pd.read_csv('X_w2v_test.csv')
+y_w2v_train = pd.read_csv('y_w2v_train.csv')
+y_w2v_test  = pd.read_csv('y_w2v_test.csv')
+
+
+# In[2]:
+
+
+import pickle
+from sklearn.ensemble import RandomForestClassifier
+# train model with all features
+rf_bow = RandomForestClassifier(n_estimators=100,
+                                max_features=None,
+                                oob_score=True,
+                                n_jobs=-1,
+                                random_state=0)
+rf_tf = RandomForestClassifier(n_estimators=100,
+                                max_features=None,
+                                oob_score=True,
+                                n_jobs=-1,
+                                random_state=0)
+rf_hash = RandomForestClassifier(n_estimators=100,
+                                max_features=None,
+                                oob_score=True,
+                                n_jobs=-1,
+                                random_state=0)
+rf_w2v = RandomForestClassifier(n_estimators=100,
+                                max_features=None,
+                                oob_score=True,
+                                n_jobs=-1,
+                                random_state=0)
+
+rf_bow.fit(X_bow_train, y_bow_train)
+rf_tf.fit(X_tf_train, y_tf_train)
+rf_hash.fit(X_hash_train, y_hash_train)
+rf_w2v.fit(X_w2v_train, y_w2v_train)
+
+
+# In[3]:
+
+
+pickle.dump(rf_bow, open('rf_bow.pkl','wb'))
+pickle.dump(rf_tf, open('rf_tf.pkl','wb'))
+pickle.dump(rf_hash, open('rf_hash.pkl','wb'))
+pickle.dump(rf_w2v, open('rf_w2v.pkl','wb'))
+
+
+# In[4]:
+
+
+#train model for logistic Regression which is not inherently multiclass classifers. 
+#In this case, we use  defualt auto setting that if input is binary using OVR otherwise using multnomial
+from sklearn.linear_model import LogisticRegression
+
+lr_bow = LogisticRegression()
+lr_tf = LogisticRegression()
+lr_hash = LogisticRegression()
+lr_w2v = LogisticRegression()
+
+lr_bow.fit(X_bow_train, y_bow_train)
+lr_tf.fit(X_tf_train, y_tf_train)
+lr_hash.fit(X_hash_train, y_hash_train)
+lr_w2v.fit(X_w2v_train, y_w2v_train)
+
+
+# In[5]:
+
+
+pickle.dump(lr_bow, open('lr_bow.pkl','wb'))
+pickle.dump(lr_tf, open('lr_tf.pkl','wb'))
+pickle.dump(lr_hash, open('lr_hash.pkl','wb'))
+pickle.dump(lr_w2v, open('lr_w2v.pkl','wb'))
+
+
+# In[6]:
+
+
+#train model for linear svm, which is not inherently multiclass classifers. 
+#In this case, we use One VS Rest to save computing 
+from sklearn.svm import SVC
+
+svc_bow = SVC(decision_function_shape='ovr')
+svc_tf = SVC(decision_function_shape='ovr')
+svc_hash = SVC(decision_function_shape='ovr')
+svc_w2v = SVC(decision_function_shape='ovr')
+
+svc_bow.fit(X_bow_train, y_bow_train)
+svc_tf.fit(X_tf_train, y_tf_train)
+svc_hash.fit(X_hash_train, y_hash_train)
+svc_w2v.fit(X_w2v_train, y_w2v_train)
+
+
+# In[7]:
+
+
+pickle.dump(svc_bow, open('svc_bow.pkl','wb'))
+pickle.dump(svc_tf, open('svc_tf.pkl','wb'))
+pickle.dump(svc_hash, open('svc_hash.pkl','wb'))
+pickle.dump(svc_w2v, open('svc_w2v.pkl','wb'))
+
+
+# In[8]:
+
+
+#train model for KNN
+from sklearn.neighbors import KNeighborsClassifier
+
+knn_bow = KNeighborsClassifier(n_neighbors=3)
+knn_tf = KNeighborsClassifier(n_neighbors=3)
+knn_hash = KNeighborsClassifier(n_neighbors=3)
+knn_w2v = KNeighborsClassifier(n_neighbors=3)
+
+knn_bow.fit(X_bow_train, y_bow_train)
+knn_tf.fit(X_tf_train, y_tf_train)
+knn_hash.fit(X_hash_train, y_hash_train)
+knn_w2v.fit(X_w2v_train, y_w2v_train)
+
+
+# In[9]:
+
+
+pickle.dump(knn_bow, open('knn_bow.pkl','wb'))
+pickle.dump(knn_tf, open('knn_tf.pkl','wb'))
+pickle.dump(knn_hash, open('knn_hash.pkl','wb'))
+pickle.dump(knn_w2v, open('knn_w2v.pkl','wb'))
+
+
+# In[10]:
+
+
+#train model for Naive Bayes. 
+#Bernoulli NB can only focus on a single keyword, 
+#but will also count how many times that keyword does not occur in the document
+from sklearn.naive_bayes import BernoulliNB
+
+
+bnb_bow = BernoulliNB()
+bnb_tf = BernoulliNB()
+bnb_hash = BernoulliNB()
+bnb_w2v = BernoulliNB()
+
+bnb_bow.fit(X_bow_train, y_bow_train)
+bnb_tf.fit(X_tf_train, y_tf_train)
+bnb_hash.fit(X_hash_train, y_hash_train)
+bnb_w2v.fit(X_w2v_train, y_w2v_train)
+
+
+# In[11]:
+
+
+pickle.dump(bnb_bow, open('bnb_bow.pkl','wb'))
+pickle.dump(bnb_tf, open('bnb_tf.pkl','wb'))
+pickle.dump(bnb_hash, open('bnb_hash.pkl','wb'))
+pickle.dump(bnb_w2v, open('bnb_w2v.pkl','wb'))
+
+
+# In[ ]:
+
+
+
+