Spam Email DetectionUnderstanding spam email detection projects in Python and providing oriented solutions.

Spam is commonly defined as junk electronic content originating from various Spam email detection projects in Python. In most cases, it comes from promoting products that are delivered to mailing lists or even phone number lists. Today, the real problem is dual; spamming wastes people’s time, while it also “eats up” a lot of network bandwidth. As a result, many organizations and individuals are making continuous and immeasurable efforts to counterattack the Spam email detection project in Python. Of course, data science cannot be overlooked at the forefront.

The scope of this article is to demonstrate how Programming language open-source projects counter-attacking spamming activity, with great effect. 

Concept

When programming language open source projects developing and improving Spotify playlists using Python to better remember the concept, "Data Corp" will hold parties (and therefore playlists) for the benefit of young clients to reward sales. Prepared for a surge. In the youth marketing segment.

Data Corp's board of directors has decided to increase sales to the youth market by releasing a new mobile app in the communications category. Potential competitive advantages have been scrutinized to defeat competitors. Specifically, my data science department was tasked with contacting the engineering team, taking the user's SMS as input, and providing an algorithm to automatically exclude spam. This algorithm will be implemented in a spam email detection project in Python in new apps and must achieve a correct answer rate of at least 90% (the standard rate achieved by competitors).

To better communicate the outcomes, several assumptions were made:

• Following the GDPR obligations, the company does not use client messages. Instead, it only processes publicly available datasets, specifically spam email detection projects in Python collections uploaded to the UCI Machine Learning Repository.

• We are going to analyze the “core” of the algorithm, that is, the mechanisms that filter out spamming content.

Modus Operandi

To fulfill our mission, we need to follow the following roadmap. 

• Briefly explain the theory needed so that necessary equations to be expressed and coded.

• Set up the environment required to execute the code. 

• Examine and prepare the record. 

• Develop algorithms using Pandas, Numpy, NLTK, and many other Python libraries. 

• Run the algorithm to classify new messages and measure their accuracy.

1. Theory

There are several programming language open source projects with variations of the Naive Bayes algorithm, which distinguish between the three most popular versions, depending on the math and assumptions made. 

• Multinomial Naive Bays 

• Gauss Naive Bays 

• Berney Naive Bays 

Computers learn how humans classify messages as spam / non-spam and use that knowledge to estimate the probability of new (incoming) messages and classify them accordingly.

2. Set-Up

The following components/libraries are essential, to developing and running the algorithm:

Install Jupyter Notebook an open-source web application for creating/sharing documents containing live code, equations, visualizations, and descriptive text.

Install NLTK, a Python library that provides efficient modules for raw data preprocessing and cleaning (punctuation removal, tokenization, etc.). You can use either the CLI (command-line interface) or the Jupyter notebook.

Download stopword This is a commonly used set of words (such as "the", "a", "an") that appear frequently in sentences but have little weight.

Download Punkt a sentence tokenizer that divides a text into a list of sentences.

Download WordNet a large lexical database of English, whose structure makes it a useful tool for computational linguistics and NLP (Natural Language Processing).

3. Data Set Exploration & Split-Up

The complete dataset consists of 5.572 SMS messages (already classified by humans), with approximately 87% and 13% classified as ham and spam, respectively. We Randomly sort to maintain the original spam/ham percentage, then split into two subsets and choose to use the 80% - 20% split.

training_set: used to “train” the computer on how to classify messages

test_set: used to finally test how good the spam filter is (accuracy)

4. Algorithm Development

NLP aims to teach computers to understand and manipulate human language contextually, even when processing numerical data. After applying some preprocessing techniques, the dataset remains meaningful, high-weight artifacts. Then encode the message in the dataset into a numeric vector.

5. Algorithm Implementation & Accuracy Measurement

Finally, if you try to classify the 1.114 messages to determine the performance of the filter. This function is applied for each new SMS and registers its label in the new column sms_predicted. Our spam filter examined 1,114 unknown messages (not seen in training) and was able to correctly classify 1,087. The measured accuracy is about 97.6%, well above the company's target (90%), and as a result, our model is used in production.

Conclusion

Communication channels are constantly exposed to attacks by fraudulent mechanisms that tend to "steal" people and organizations' time and money. Data science can provide valuable solutions. In 2015, Google achieved  99.9% accuracy in preventing spam email detection projects in Python. In addition, by using that "neural network", we can close the remaining tenth percent of the failures. However, the selling point of data science is that it can provide valuable solutions even for amateur users at the beginner level.

Eventually, though we do acknowledge the existence and activity of spamming structures at the expense of our time and money, we should always keep in mind that Data Science already counteracts in favor of humanity.