Hello fellow learner! In this tutorial, we will talk about how to achieve the classification of spam emails with the help of the dataset which will be loaded using scikit-learn in Python programming language.<\/p>\n\n\n\n
We all know that billions of spam are sent every day to user email accounts and more than 90% of these spam emails are malicious and cause major harm to the user. <\/p>\n\n\n\n
Don\u2019t the spams get annoying to you as well? They get pretty annoying to me for sure! Sometimes even some important mails get transferred to spam and as a result, some important information is left unread with the fear of getting harmed by the spam emails.<\/p>\n\n\n\n
And did you know that one out of every 1,000 e-mails contains malware charges<\/strong>? And hence it is important for us to learn how can we ourselves classify our emails as safe and unsafe.<\/p>\n\n\n\n Let’s get right into the steps to implement an email spam classification algorithm using Python. This will help you understand the backend working of a very basic spam classifier. The algorithms used in the real world are way more advanced compared to the algorithm I’ve described below. But you sure can use this as a starting point for your journey.<\/p>\n\n\n\n First, we import all the necessary required modules into our program. The code for the same is as follows:<\/p>\n\n\n We would require some basic machine learning modules such as numpy<\/a>, pandas<\/a>, and matplotlib<\/a>. Along with these, we would require some The next step involves loading the dataset with the help of the pandas module imported earlier. The dataset we would be using is the The dataset we loaded has 5572 email samples along with 2 unique labels namely, After loading we have to separate the data into training and testing data<\/a>.\u00a0<\/p>\n\n\n\n The separation of data into training and testing data includes two steps:<\/p>\n\n\n\n The separation of data into x and y data is done in the following code:<\/p>\n\n\n The next step is to get only the important words\/features from the whole dataset. To achieve this, we will make use of the The most important step involves building and training the model for the dataset we created earlier. The code for the same is as follows:<\/p>\n\n\n The final step includes computing the overall accuracy of our model on the testing dataset.<\/p>\n\n\n We ended up achieving an accuracy of Implementing an email classification system is a great next step in developing the technology and making emails more secure. <\/p>\n\n\n\n I hope you loved the tutorial! Happy Learning! 😇<\/p>\n\n\n\n <\/p>\n","protected":false},"excerpt":{"rendered":" Hello fellow learner! In this tutorial, we will talk about how to achieve the classification of spam emails with the help of the dataset which will be loaded using scikit-learn in Python programming language. Introduction to Email Spam We all know that billions of spam are sent every day to user email accounts and more […]<\/p>\n","protected":false},"author":28,"featured_media":22198,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[9],"tags":[],"class_list":["post-22193","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-examples"],"blocksy_meta":[],"_links":{"self":[{"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/posts\/22193","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/users\/28"}],"replies":[{"embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/comments?post=22193"}],"version-history":[{"count":0,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/posts\/22193\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/media\/22198"}],"wp:attachment":[{"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/media?parent=22193"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/categories?post=22193"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/tags?post=22193"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Implementing Email Spam Classifier in Python<\/h2>\n\n\n\n
1. Importing Modules and Loading Data<\/h3>\n\n\n\n
\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.naive_bayes import MultinomialNB, GaussianNB\nfrom sklearn import svm\nfrom sklearn.model_selection import GridSearchCV\n<\/pre><\/div>\n\n\n
sklearn<\/code> models and features.<\/p>\n\n\n\nspam.csv<\/code> data file which can be found here<\/a>.<\/p>\n\n\n\ndata = pd.read_csv('.\/spam.csv')\n<\/pre><\/div>\n\n\nspam<\/code> and ham<\/code>. <\/p>\n\n\n\n2. Training and Testing Data<\/h3>\n\n\n\n
\nx_data=data['EmailText']\ny_data=data['Label']\n\nsplit =(int)(0.8*data.shape[0])\nx_train=x_data[:split]\nx_test=x_data[split:]\ny_train=y_data[:split]\ny_test=y_data[split:]\n<\/pre><\/div>\n\n\n
3. Extracting Important Features<\/h3>\n\n\n\n
CountVectorizer<\/code> function in order to vectorize the words of the training dataset.<\/p>\n\n\n\ncount_vector = CountVectorizer() \nextracted_features = count_vector.fit_transform(x_train)\n<\/pre><\/div>\n\n\n
4. Building and Training The Model<\/h3>\n\n\n\n
\ntuned_parameters = {'kernel': ['rbf','linear'], 'gamma': [1e-3, 1e-4],'C': [1, 10, 100, 1000]}\nmodel = GridSearchCV(svm.SVC(), tuned_parameters)\nmodel.fit(extracted_features,y_train)\n\nprint("Model Trained Successfully!")\n<\/pre><\/div>\n\n\n\nprint("Accuracy of the model is: ",model.score(count_vector.transform(x_test),y_test)*100)\n<\/pre><\/div>\n\n\n98.744%<\/b><\/code> <\/strong> which is great!!<\/p>\n\n\n\nConclusion<\/h2>\n\n\n\n
Also Read:<\/h2>\n\n\n\n