In this article, we implement Naive-Bayes classifier from scratch, yeah — straight from scratch, no libraries. There might be number of libraries out there implementing this within one or two lines of code, but that’s not what we’re looking for here. This article is to strengthen your grasp on the topic, and how better to do that other than building from mud and clay? And one more thing, I won’t be focusing on the theory here, you may have a look at this if need to revise, gonna be an awesome read!
The scenario is such that, we’re given a csv data set of 10 patients in suspicion of being COVID-19 infected. We are given five health conditions viz. have fever?, have cough? have respiratory discomfort?, have runny nose? and have any throat ache — each denoted by 0 or 1. The last column is the tests result, also containing 0 for negative and 1 for positive.
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???
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
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Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.
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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:
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No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas.
By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities.
Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly.
Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.
Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions.
Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events.
Simple to read and compose
Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building.
The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties.
Utilized by the best
Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player.
Massive community support
Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions.
Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking.
Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.
The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.
Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential.
The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.
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Naïve Bayes Algorithm is one of the popular classificationmachine learning algorithms and is included in supervised learning. that helps to classify the data based upon the conditional probability values computation. This algorithm is quite popular to be used in Natural Language Processingor NLP also real-time prediction, multi-class prediction, recommendation system, text classification, and sentiment analysis use cases. the algorithm is scalable and easy to implement for the large data set.
The algorithm based on **Bayes theorem. **Bayes Theorem helps us to find the probability of a hypothesis given our prior knowledge.
Let’s look at the equation for Bayes Theorem,
Naïve Bayes is a simple but surprisingly powerful predictive modeling algorithm. Naïve Bayes classifier calculates the probabilities for every factor. Then it selects the outcome with the highest probability.
1. Real-time prediction: Naïve Bayes Algorithm is fast and always ready to learn hence best suited for real-time predictions.
2. Multi-class prediction: The probability of multi-classes of any target variable can be predicted using a Naïve Bayes algorithm.
3. Text Classification where Naïve Bayes is mostly used is Spam Filtering in Emails (Naïve Bayes is widely used for text classification)
4. Text classification/ Sentiment Analysis/ Spam Filtering: Due to its better performance with multi-class problems and its independence rule, Naïve Bayes algorithm perform better or have a higher success rate in text classification, Therefore, it is used in Sentiment Analysis and Spam filtering.
5.**Recommendation System: **Naïve Bayes Classifier and Collaborative Filtering together build a Recommendation System that uses machine learning and data mining techniques to filter unseen information and predict whether a user would like a given resource or not.
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