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Author: arthimj

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+import pandas as pd
+import numpy as np
+import random
+
+
+# Machine Learning Packages
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import train_test_split
+
+# Load Url Data 
+urls_data = pd.read_csv("url_features.csv")
+
+type(urls_data)
+
+urls_data.head()
+
+def makeTokens(f):
+    tkns_BySlash = str(f.encode('utf-8')).split('/')	# make tokens after splitting by slash
+    total_Tokens = []
+    for i in tkns_BySlash:
+        tokens = str(i).split('-')	# make tokens after splitting by dash
+        tkns_ByDot = []
+        for j in range(0,len(tokens)):
+            temp_Tokens = str(tokens[j]).split('.')	# make tokens after splitting by dot
+            tkns_ByDot = tkns_ByDot + temp_Tokens
+        total_Tokens = total_Tokens + tokens + tkns_ByDot
+    total_Tokens = list(set(total_Tokens))	#remove redundant tokens
+    if 'com' in total_Tokens:
+        total_Tokens.remove('com')	#removing .com since it occurs a lot of times and it should not be included in our features
+    return total_Tokens
+
+y = urls_data["label"]
+url_list = urls_data["url"]
+
+ #Using Default Tokenizer
+#vectorizer = TfidfVectorizer()
+
+# Using Custom Tokenizer
+vectorizer = TfidfVectorizer(tokenizer=makeTokens)
+
+X = vectorizer.fit_transform(url_list)
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Model Building
+#using logistic regression
+print("TRAINING PHASE")
+logit = LogisticRegression()	
+logit.fit(X_train, y_train)
+print("Accuracy ",logit.score(X_test, y_test))
+#print("coefficient :\n",logit.coef_)
+#print("Intercept:\n",logit.intercept_)
+
+PRINT("TESTING PHASE")
+X_predict = ["google.com/search=jcharistech",
+"google.com/search=faizanahmad",
+"pakistanifacebookforever.com/getpassword.php/", 
+"www.radsport-voggel.de/wp-admin/includes/log.exe", 
+"ahrenhei.without-transfer.ru/nethost.exe ",
+"www.itidea.it/centroesteticosothys/img/_notes/gum.exe"]
+
+X_predict = vectorizer.transform(X_predict)
+New_predict = logit.predict(X_predict)
+
+print("THE GIVEN URLS ARE : ",New_predict)
+
+"""X_predict1 = ["www.buyfakebillsonlinee.blogspot.com", 
+"www.unitedairlineslogistics.com",
+"www.stonehousedelivery.com",
+"www.silkroadmeds-onlinepharmacy.com" ]
+
+X_predict1 = vectorizer.transform(X_predict1)
+New_predict1 = logit.predict(X_predict1)
+print(New_predict1)
+
+vectorizer = TfidfVectorizer()
+X = vectorizer.fit_transform(url_list)
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+logit = LogisticRegression()	#using logistic regression
+logit.fit(X_train, y_train)
+
+print("Accuracy ",logit.score(X_test, y_test))"""