And just to kinda mess with it, I tried to, you know, I tried to give an AI an email that I just kind of fabricated up. Online course is fantastic way to learn a new skill and I take a lot of them myself. ZENVA courses consist mainly of video lessons that you can watch, re-watch, at your own pace as many times as you want. We also have downloadable source code and project files that contain everything that we build in a lesson.
And remember that these videos you can watch and re watch as many times as you want, so that really gives you more flexibility, so you can adapt to how you learn. Hello everybody. In in this video, I just wanna introduce you guys to the problem of text classification.
We want to be able to put it into one or in some cases more, different bins.
Spam Classifier in Python from scratch
Looking at large amounts of numerical data for humans can be kind of tedious and error prone. But for computers, they love that stuff. And so we have to find some way to convert the words into a numeric representation so that we can work with them a little bit better.
Like I mentioned before, supervised learning is a great way to accomplish this task. Spam filtering is just one of them. I wanna be able to tell is the sender angry or upset or other things like that. This is especially popular in social media so you can mine lots of social media data. This is something that we can delegate to a machine learning algorithm lists from previous inaccuracies.
Give it a book, you can tell what genre it is, for example or even categorize it even further. Another thing that I wanna mention, another popular use of this is called readability. And with readability, it is more like given a passage of text, I wanna determine things like what level of reading or comprehension level you need to understand this passage or more accurately, given some of the words in this passage, what is the expected reading level, for example. So, you might notice that if you have an elementary school or a primary school reading level, the words might be just one or two syllables and the sentence structure is fairly simple but as you go up to more scientific writing or graduate school writing for example, then you notice that there are longer words.
There are more complicated words. Maybe the sentence structure is different. Readability assessment, we can look at text. We can look at words, the sentence structure to access a passage and determine what level of reading comprehension would be assigned to that passage of text.It was first introduced in and gained popularity worldwide after its use during World War II.
Natural gelatin is formed during cooking in its tins on the production line. It has become the subject of several appearances in pop culture, notably a Monty Python sketchwhich led to its name being borrowed for unsolicited electronic messagesespecially email. Spam was introduced by Hormel on July 5, It became variously referred to as "ham that didn't pass its physical", "meatloaf without basic training",  and "Special Army Meat".
Immediately absorbed into native diets, it has become a unique part of the history and effects of U. British Prime Minister Margaret Thatcher later referred to it as a "wartime delicacy". The billionth can of Spam was sold in the seven billionth can was sold in and the eight billionth can was sold in Domestically, Spam's chief advantages were affordability, accessibility, and extended shelf life.
Spam is especially popular in the state of Hawaii, where residents have the highest per capita consumption in the United States. Its perception there is very different from on the mainland. A popular local dish in Hawaii is Spam musubiwhere cooked Spam is placed atop rice and wrapped in a band of noria form of onigiri.
Hawaiian Burger King restaurants began serving Spam in to compete with the local McDonald's chains which also serve Spam. InHawaii was plagued by a rash of thefts of Spam. Local retailers believe organized crime was involved. In Guamaverage per capita consumption is 16 tins cans per year.
It is also found on McDonald's menus there. The Spam Games also takes place in Guam, where locals sample and honor the best original, homemade Spam recipes. In the Northern Mariana Islandslawyers from Hormel have threatened to sue the local press for publishing articles alleging ill-effects of high Spam consumption on the health of the local population.
Sandwich de Mezcla is a party staple in Puerto Rico containing Spam, Velveetaand pimientos made into a spread between two slices of sandwich bread. The United Kingdom has adopted Spam into various recipes. Spam is commonly eaten with rice usually garlic fried rice and a sunny-side up egg for breakfast. It is prepared and used in a variety of ways, including being fried, served alongside condiments, or used in sandwiches.
It has also been featured in numerous Filipino fusion cuisine dishes including Spam burgers, Spam spaghettiSpam nuggets, and others. The popularity of Spam in the Philippines transcends economic class, and Spam gift sets are even used as homecoming gifts.
There are at least ten different varieties of Spam currently available in the country and an estimated 1. In China, Hormel decided to adopt a different strategy to market Spam, promoting it as a foreign, premium food product and changing the Spam formula to be meatier in order to accommodate local Chinese tastes.
For the 70th anniversary of Spam incans with special designs were sold in Japan due to its popularity, primarily in Okinawa. These small burgers are filled with slices of the canned meat and were an attempt by Burger King to capitalize on Spam's popularity in Japan. The luncheon meat has been incorporated into dishes such as macaroni with fried egg and spam in chicken soup, as well as ramen.
In later years, the surfeit of Spam in both North and South Korea during the Korean War led to the establishment of the Spam kimbap rice and vegetable filled seaweed roll. Because of a scarcity of fish and other traditional kimbap products such as kimchi or fermented cabbage, Spam was added to a rice roll with kimchi and cucumber and wrapped in seaweed.
Spam was also used by US soldiers in Korea as a means of trading for items, services or information around their bases.Text mining deriving information from text is a wide field which has gained popularity with the huge text data being generated. Automation of a number of applications like sentiment analysis, document classification, topic classification, text summarization, machine translation, etc has been done using machine learning models.
Spam box in your Gmail account is the best example of this. So lets get started in building a email spam filter on a publicly available mail corpus. I have extracted equal number of spam and non-spam emails from Ling-spam corpus.
The extracted subset on which we will be working can be downloaded from here. The data-set used here, is split into a training set and a test set containing mails and mails respectively, divided equally between spam and ham mails. In any text mining problem, text cleaning is the first step where we remove those words from the document which may not contribute to the information we want to extract.
Emails may contain a lot of undesirable characters like punctuation marks, stop words, digits, etc which may not be helpful in detecting the spam email. The emails in Ling-spam corpus have been already pre-processed in the following ways:. The context of the sentence is also preserved in lemmatization as opposed to stemming another buzz word in text mining which does not consider meaning of the sentence.
We still need to remove the non-words like punctuation marks or special characters from the mail documents. There are several ways to do it.
So cheers!! It can be seen that the first line of the mail is subject and the 3rd line contains the body of the email. We will only perform text analytics on the content to detect the spam mails. As a first step, we need to create a dictionary of words and their frequency. For this task, training set of mails is utilized. This python function creates the dictionary for you. Once the dictionary is created we can add just a few lines of code written below to the above function to remove non-words about which we talked in step 1.
I have also removed absurd single characters in the dictionary which are irrelevant here. If you are following this blog with provided data-set, make sure your dictionary has some of the entries given below as most frequent words. Here I have chosen most frequently used words in the dictionary. Once the dictionary is ready, we can extract word count vector our feature here of dimensions for each email of training set. Each word count vector contains the frequency of words in the training file.
Of course you might have guessed by now that most of them will be zero. Let us take an example. Suppose we have words in our dictionary.
Each word count vector contains the frequency of dictionary words in the training file.
Detect E-mail Spam Using Python
The below python code will generate a feature vector matrix whose rows denote files of training set and columns denote words of dictionary. For building email spam filter, we will train mathematical model that learns a decision boundary in features space between the two classes. Here, I will be using scikit-learn ML library for training classifiers.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. This is a tutorial post to show how can we build a email spam detection system from scratch using Python. For more detailed explaination, you can read my tutorial in this medium blog post or you can run it directly in the colab environment.
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Email-Spam-Detection-Python This is a tutorial post to show how can we build a email spam detection system from scratch using Python For more detailed explaination, you can read my tutorial in this medium blog post or you can run it directly in the colab environment.
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You signed out in another tab or window. Email Spam Medium Part 2. Email Spamming Medium Part 1.The most basic data structure in Python is the sequence.
Each element of a sequence is assigned a number - its position or index. The first index is zero, the second index is one, and so forth. Python has six built-in types of sequences, but the most common ones are lists and tuples, which we would see in this tutorial. There are certain things you can do with all sequence types. These operations include indexing, slicing, adding, multiplying, and checking for membership.
In addition, Python has built-in functions for finding the length of a sequence and for finding its largest and smallest elements. The list is a most versatile datatype available in Python which can be written as a list of comma-separated values items between square brackets.
Important thing about a list is that items in a list need not be of the same type. Creating a list is as simple as putting different comma-separated values between square brackets. Similar to string indices, list indices start at 0, and lists can be sliced, concatenated and so on. To access values in lists, use the square brackets for slicing along with the index or indices to obtain value available at that index. You can update single or multiple elements of lists by giving the slice on the left-hand side of the assignment operator, and you can add to elements in a list with the append method.
To remove a list element, you can use either the del statement if you know exactly which element s you are deleting or the remove method if you do not know. In fact, lists respond to all of the general sequence operations we used on strings in the prior chapter.
Because lists are sequences, indexing and slicing work the same way for lists as they do for strings. Python - Lists Advertisements. Previous Page. Next Page. Live Demo. Previous Page Print Page.We all face the problem of spams in our inboxes. It is mathematically expressed as. We need to find.
If we assume that occurrence of a word are independent of all other words, we can simplify the above expression to. In order to classify we have to determine which is greater. We are going to make use of NLTK for processing the messages, WordCloud and matplotlib for visualization and pandas for loading data, NumPy for generating random probabilities for train-test split.
Email Spam Filtering : A python implementation with scikit-learn
Finally we obtain the following dataframe. To test our model we should split the data into train dataset and test dataset. We shall use the train dataset t0 train the model and then it will be tested on the test dataset. Let us see which are the most repeated words in the spam messages!
We are going to use WordCloud library for this purpose. This results in the following. Similarly the wordcloud of ham messages is as follows:. I shall explain them one by one. Let us first start off with Bag of words. Preprocessing : Before starting with training we must preprocess the messages. First of all, we shall make all the character lowercase. Then we tokenize each message in the dataset.
Tokenization is the task of splitting up a message into pieces and throwing away the punctuation characters.Build a Spammer Bot With Python - So Fun!
For eg. This is called stemming. We are going to use Porter Stemmerwhich is a famous stemming algorithm. We then move on to remove the stop words. Stop words are those words which occur extremely frequently in any text.
These words do not give us any information about the content of the text. Thus it should not matter if we remove these words for the text. Optional: You can also use n-grams to improve the accuracy. As of now, we only dealt with 1 word. But when two words are together the meaning totally changes. Therefore, sometimes accuracy is improved when we split the text into tokens of two or more words than only word.
Thus for word w. In addition to Term Frequency we compute Inverse document frequency. For example, there are two messages in the dataset. If a word occurs a lot, it means that the word gives less information.
Probability of each word is counted as:. Additive Smoothing :So what if we encounter a word in test dataset which is not part of train dataset? In that case P w will be 0, which will make the P spam w undefined since we would have to divide by P w which is 0. Remember the formula? To tackle this issue we introduce additive smoothing.
I am new to python, just got the learning python book and got stuck with the spam. The book says to make a file named spam. The error I receive is. You seem to be trying to run the spam. You are trying to execute Python script file within the interpreter. Then execute with the command :.
To change the directory:. As you said you have added Python to PATH and followed my instructions, the statement below should work perfectly. Learn more. Beginner Python spam. Asked 6 years, 2 months ago. Active 4 years, 1 month ago. Viewed 2k times. Active Oldest Votes. Thank you for the help.
When i run this from CMD after exiting python i still receive an error. I type python spam. The only way I could get it to run is by typing in the entire file path ex. I was just trying to following the book to use the python spam. Could you post the code from spam.
Anand Anand 6 6 silver badges 13 13 bronze badges. To change the directory: use dir to display the folders and files in current place use cd to change your directory e.
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