--- a
+++ b/code/ModelEvaluation.py
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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import pandas as pd
+import numpy as np
+import pickle
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, make_scorer
+import warnings
+warnings.filterwarnings('ignore')
+
+def matric_table(model_list, name_list,y_data, X_data):
+    result = []
+    for m,n,a,b in zip(model_list, name_list, y_data, X_data):
+        report = []
+        report.append(n)
+        report.append(accuracy_score(a[0], m.predict(b[0])) * 100)
+        report.append(accuracy_score(a[1], m.predict(b[1])) * 100)
+        report.append(recall_score(a[1], m.predict(b[1]),average = 'weighted') * 100)
+        report.append(precision_score(a[1], m.predict(b[1]),average = 'weighted') * 100)
+        report.append(f1_score(a[1], m.predict(b[1]),average = 'weighted') * 100)
+        result.append(report)
+    df = pd.DataFrame(data = result, columns=['Model', 'Training Accuracy %', 'Testing Accuracy %','Testing precision %', 'Testing recall %', 'Testing f1_score %'])
+    df = df.set_index('Model')
+    return df.style.highlight_max(color = 'lightgreen', axis = 0)
+
+
+# In[2]:
+
+
+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')
+
+
+# ### Random Forest with word2vec extracted data is the best among all Random Forest Classifers
+
+# In[3]:
+
+
+rf_bow = pickle.load(open('rf_bow.pkl','rb'))
+rf_tf = pickle.load(open('rf_tf.pkl','rb'))
+rf_hash = pickle.load(open('rf_hash.pkl','rb'))
+rf_w2v = pickle.load(open('rf_w2v.pkl','rb'))
+
+model_list = [rf_bow,rf_tf,rf_hash,rf_w2v]
+name_list = ["Random Forest with bow","Random Forest with tf_idf", "Random Forest with hash","Random Forest with word2vec"]
+y_data = [[y_bow_train,y_bow_test], [y_tf_train,y_tf_test], [y_hash_train,y_hash_test],[y_w2v_train,y_w2v_test]]
+X_data = [[X_bow_train,X_bow_test], [X_tf_train,X_tf_test], [X_hash_train,X_hash_test],[X_w2v_train,X_w2v_test]]
+matric_table(model_list, name_list, y_data, X_data)
+
+
+# ### Logistic Regression with bag-of-words extracted data is the best among all logistic Regression Classifers
+
+# In[4]:
+
+
+# print result, the warning indicate there are some type the classifers never predict. but since data is imbalence in that rare class so the accuracy won't be impacted
+lr_bow = pickle.load(open('lr_bow.pkl','rb'))
+lr_tf = pickle.load(open('lr_tf.pkl','rb'))
+lr_hash = pickle.load(open('lr_hash.pkl','rb'))
+lr_w2v = pickle.load(open('lr_w2v.pkl','rb'))
+
+model_list = [lr_bow,lr_tf,lr_hash,lr_w2v]
+name_list = ["Logistic Regression with bow","Logistic Regression with tf_idf", "Logistic Regression with hash","Logistic Regressiont with word2vec"]
+y_data = [[y_bow_train,y_bow_test], [y_tf_train,y_tf_test], [y_hash_train,y_hash_test],[y_w2v_train,y_w2v_test]]
+X_data = [[X_bow_train,X_bow_test], [X_tf_train,X_tf_test], [X_hash_train,X_hash_test],[X_w2v_train,X_w2v_test]]
+matric_table(model_list, name_list, y_data, X_data)
+
+
+# ### Support Vectors Machine with tf-idf extracted data is the best among all SVCs 
+
+# In[5]:
+
+
+svc_bow = pickle.load(open('svc_bow.pkl','rb'))
+svc_tf = pickle.load(open('svc_tf.pkl','rb'))
+svc_hash = pickle.load(open('svc_hash.pkl','rb'))
+svc_w2v = pickle.load(open('svc_w2v.pkl','rb'))
+# print result
+model_list = [svc_bow,svc_tf,svc_hash,svc_w2v]
+name_list = ["SVC with bow","SVC with tf_idf", "SVC with hash","SVC with word2vec"]
+y_data = [[y_bow_train,y_bow_test], [y_tf_train,y_tf_test], [y_hash_train,y_hash_test],[y_w2v_train,y_w2v_test]]
+X_data = [[X_bow_train,X_bow_test], [X_tf_train,X_tf_test], [X_hash_train,X_hash_test],[X_w2v_train,X_w2v_test]]
+matric_table(model_list, name_list, y_data, X_data)
+
+
+# ### K Nearest Neighours with bag-of-words extracted data is the best among all KNNs 
+
+# In[6]:
+
+
+knn_bow = pickle.load(open('knn_bow.pkl','rb'))
+knn_tf = pickle.load(open('knn_tf.pkl','rb'))
+knn_hash = pickle.load(open('knn_hash.pkl','rb'))
+knn_w2v = pickle.load(open('knn_w2v.pkl','rb'))
+
+model_list = [knn_bow,knn_tf,knn_hash,knn_w2v]
+name_list = ["KNN with bow","KNN with tf_idf", "KNN with hash","KNN with word2vec"]
+y_data = [[y_bow_train,y_bow_test], [y_tf_train,y_tf_test], [y_hash_train,y_hash_test],[y_w2v_train,y_w2v_test]]
+X_data = [[X_bow_train,X_bow_test], [X_tf_train,X_tf_test], [X_hash_train,X_hash_test],[X_w2v_train,X_w2v_test]]
+matric_table(model_list, name_list, y_data, X_data)
+
+
+# ### Binomial Naive Bayes with tf_idf extracted data is the best among all BNBs 
+
+# In[7]:
+
+
+bnb_bow = pickle.load(open('bnb_bow.pkl','rb'))
+bnb_tf = pickle.load(open('bnb_tf.pkl','rb'))
+bnb_hash = pickle.load(open('bnb_hash.pkl','rb'))
+bnb_w2v = pickle.load(open('bnb_w2v.pkl','rb'))
+
+model_list = [bnb_bow,bnb_tf,bnb_hash,bnb_w2v]
+name_list = ["Binomial Naive Bayes with bow","Binomial Naive Bayes with tf_idf", "Binomial Naive Bayes with hash","Binomial Naive Bayes with word2vec"]
+y_data = [[y_bow_train,y_bow_test], [y_tf_train,y_tf_test], [y_hash_train,y_hash_test],[y_w2v_train,y_w2v_test]]
+X_data = [[X_bow_train,X_bow_test], [X_tf_train,X_tf_test], [X_hash_train,X_hash_test],[X_w2v_train,X_w2v_test]]
+matric_table(model_list, name_list, y_data, X_data)
+
+
+# ### In conclusion, Random Forest with word2vec dataset is the winner among all classifers with highest score on test accuracy, precision, recall and F1 scores
+
+# In[8]:
+
+
+### Find the best classifer among all classifers
+model_list = [rf_w2v,lr_bow,svc_tf,knn_bow,bnb_tf]
+name_list = ["Random Forest with word2vec","Logistic Regression with bag-of-words", "SVC with tf_idf","KNN with bag-of-words","Binomial Naive Bayes with tf_idf"]
+y_data = [[y_w2v_train,y_w2v_test], [y_bow_train,y_bow_test], [y_tf_train,y_tf_test],[y_bow_train,y_bow_test],[y_tf_train,y_tf_test]]
+X_data = [[X_w2v_train,X_w2v_test], [X_bow_train,X_bow_test], [X_tf_train,X_tf_test],[X_bow_train,X_bow_test],[X_tf_train,X_tf_test]]
+matric_table(model_list, name_list, y_data, X_data)
+
+
+# In[ ]:
+
+
+
+