How to Build a Spam Filter using Python and Naive Bayes

In this blog post, we’re going to build a spam filter using Python and the multinomial Naive Bayes algorithm. Our goal is to code a spam filter from scratch that classifies messages with an accuracy greater than 80%.

To build our spam filter, we’ll use a dataset of 5,572 SMS messages. Tiago A. Almeida and José María Gómez Hidalgo put together the dataset, you can download it from the UCI Machine Learning Repository.

We’re going to focus on the Python implementation throughout the post, so we’ll assume that you are already familiar with multinomial Naive Bayes and conditional proability.

If you need to fill in any gaps before moving forward, Dataquest has a course that covers both conditional probability and multinomial Naive Bayes, as well as a broad variety of other course you could use to fill in gaps in your knowledge and earn a data science certificate.

Exploring the Dataset

Let’s start by opening the SMSSpamCollection file with the read_csv() function from the pandas package. We’re going to use:

• sep='\t' because the data points are tab separated
• header=None because the dataset doesn’t have a header row
• names=['Label', 'SMS'] to name the columns

#classification #naive bayes #python #text classification

What is GEEK

Buddha Community

Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

top 30 Python Tips and Tricks for Beginners

Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.

1) swap two numbers.

2) Reversing a string in Python.

3) Create a single string from all the elements in list.

4) Chaining Of Comparison Operators.

5) Print The File Path Of Imported Modules.

6) Return Multiple Values From Functions.

7) Find The Most Frequent Value In A List.

8) Check The Memory Usage Of An Object.

…

#python #python hacks tricks #python learning tips #python programming tricks #python tips #python tips and tricks #python tips and tricks advanced #python tips and tricks for beginners #python tips tricks and techniques #python tutorial #tips and tricks in python #tips to learn python #top 30 python tips and tricks for beginners

The Ironic Sophistication of Naive Bayes Classifiers

Filtering spam with Multinomial Naive Bayes (From Scratch)

In the first half of 2020 more than 50% of all email traffic on the planet was spam. Spammers typically receive 1 reply for every 12,500,000 emails sent which doesn’t sound like much until you realize more than 15 billion spam emails are being sent each and every day. Spam is costing businesses 20–200 billion dollars per year and that number is only expected to grow.

What can we do to save ourselves from spam???

Naive Bayes Classifiers

In probability theory and statistics, Bayes’ theorem (alternatively Bayes’s theorem, Bayes’s law or Bayes’s rule) describes the probability of an event, based on prior knowledge of conditions that might be related to the event.

For example, if the risk of developing health problems is known to increase with age, Bayes’s theorem allows the risk to an individual of a known age to be assessed more accurately than simply assuming that the individual is typical of the population as a whole.

Bayes Theorem Explained

A Naive Bayes Classifier is a probabilistic classifier that uses Bayes theorem with strong independence (naive) assumptions between features.

• Probabilistic classifier: a classifier that is able to predict, given an observation of an input, a probability distribution over a set of classes, rather than only outputting the most likely class that the observation should belong to.
• Independence: Two events are **independent **if the occurrence of one does not affect the probability of occurrence of the other (equivalently, does not affect the odds). That assumption of independence of features is what makes Naive Bayes naive! In real world, the independence assumption is often violated, but naive Bayes classifiers still tend to perform very well.

#naive-bayes-classifier #python #naive-bayes #naive-bayes-from-scratch #naive-bayes-in-python

How to Build a Spam Filter using Python and Naive Bayes

In this blog post, we’re going to build a spam filter using Python and the multinomial Naive Bayes algorithm. Our goal is to code a spam filter from scratch that classifies messages with an accuracy greater than 80%.

To build our spam filter, we’ll use a dataset of 5,572 SMS messages. Tiago A. Almeida and José María Gómez Hidalgo put together the dataset, you can download it from the UCI Machine Learning Repository.

We’re going to focus on the Python implementation throughout the post, so we’ll assume that you are already familiar with multinomial Naive Bayes and conditional proability.

If you need to fill in any gaps before moving forward, Dataquest has a course that covers both conditional probability and multinomial Naive Bayes, as well as a broad variety of other course you could use to fill in gaps in your knowledge and earn a data science certificate.

Exploring the Dataset

Let’s start by opening the SMSSpamCollection file with the read_csv() function from the pandas package. We’re going to use:

• sep='\t' because the data points are tab separated
• header=None because the dataset doesn’t have a header row
• names=['Label', 'SMS'] to name the columns

#classification #naive bayes #python #text classification

How To Compare Tesla and Ford Company By Using Magic Methods in Python

Magic Methods are the special methods which gives us the ability to access built in syntactical features such as ‘<’, ‘>’, ‘==’, ‘+’ etc…

You must have worked with such methods without knowing them to be as magic methods. Magic methods can be identified with their names which start with __ and ends with __ like init, call, str etc. These methods are also called Dunder Methods, because of their name starting and ending with Double Underscore (Dunder).

Now there are a number of such special methods, which you might have come across too, in Python. We will just be taking an example of a few of them to understand how they work and how we can use them.

1. init

class AnyClass:
    def __init__():
        print("Init called on its own")
obj = AnyClass()

The first example is _init, _and as the name suggests, it is used for initializing objects. Init method is called on its own, ie. whenever an object is created for the class, the init method is called on its own.

The output of the above code will be given below. Note how we did not call the init method and it got invoked as we created an object for class AnyClass.

Init called on its own

2. add

Let’s move to some other example, add gives us the ability to access the built in syntax feature of the character +. Let’s see how,

class AnyClass:
    def __init__(self, var):
        self.some_var = var
    def __add__(self, other_obj):
        print("Calling the add method")
        return self.some_var + other_obj.some_var
obj1 = AnyClass(5)
obj2 = AnyClass(6)
obj1 + obj2

#python3 #python #python-programming #python-web-development #python-tutorials #python-top-story #python-tips #learn-python