There has been a high demand for online food orders after the introduction of Swiggy and Zomato in the market. Food delivery companies use your buying habits to make the delivery process faster. The food order prediction system is one…
MNIST Digits classification is one of the popular case studies in the data science community. It is based on the problem of Classification in Machine Learning. If you are a beginner in Machine Learning, you must try to solve this…
Breast cancer is one of the types of cancer that starts in the breast. It occurs in women, but men can get breast cancer too. It is the second leading cause of death in women. As the use of data…
The introduction of online payment systems has helped a lot in the ease of payments. But, at the same time, it increased in payment frauds. Online payment frauds can happen with anyone using any payment system, especially while making payments…
Text classification is a machine learning technique used to classify texts based on predefined labels. For example, classifying languages based on a dataset containing different languages with predefined labels. Text classification is used in various problems of natural language processing…
In machine learning, classification means categorizing the known classes, For example, categorizing the most profitable and non-interested customers from a dataset for advertising a particular product. You must have trained a classification model with a machine learning algorithm before. Here…
Stress, anxiety, and depression are threatening the mental health of people. Every person has a reason for having a stressful life. People often share their feelings on social media platforms like on Instagram in the form of posts and stories,…
In machine learning, multiclass classification is the problem of classifying between more than two classes. It is also known as multinomial classification. Simply put, when the target column in your dataset contains more than two classes, then you are working…
Binary classification is one of the types of classification problems in machine learning where we have to classify between two mutually exclusive classes. For example, classifying messages as spam or not spam, classifying news as Fake or Real. There are…
When there are only two classes in a classification problem, this is the problem of binary classification, just like that, classification with more than two classes is called multiclass classification. If you want to know the best algorithms for multiclass…
Language detection is a natural language processing task where we need to identify the language of a text or document. Using machine learning for language identification was a difficult task a few years ago because there was not a lot…
Iris Flower Classification is one of the most popular case studies among the data science community. Almost every data science newbie has solved this case study once in their life. Here, you are given the measurements associated with each species…
You must have seen the news divided into categories when you go to a news website. Some of the popular categories that you’ll see on almost any news website are tech, entertainment, and sports. If you want to know how…
There are many real-life use cases to create your unique machine learning projects. If you’re still struggling to work on an actual use case, find something practical and unique, like a machine learning project where you’ll show a comparison of…
Insurance is a contract whereby an individual obtains financial protection against losses from an insurance company against the risks of financial losses as mentioned in the insurance. Many types of insurance exist today and there are so many companies that…
Binary classification is one of the popular classification problems in machine learning where we have to predict the class of a sample based on two categories like 1 or 0, true or false, yes or no, etc. If you want…
Sarcasm has been part of our language for many years. It means being the opposite of what you mean, usually with a distinct tone of voice in a fun way. If you think that anyone can understand sarcasm, then you…
Access to safe drinking water is one of the essential needs of all human beings. From a legal point of view, access to drinking water is one of the fundamental human rights. Many factors affect water quality, it is also…
The Bernoulli Naive Bayes is one of the variations of the Naive Bayes algorithm in machine learning and it is very useful to use in a binary distribution where the output label may be present or absent. If you have…
Hate speech is one of the serious issues we see on social media platforms like Twitter and Facebook daily. Most of the posts containing hate speech can be found in the accounts of people with political views. So, if you…
A classification report is a performance evaluation metric in machine learning. It is used to show the precision, recall, F1 Score, and support of your trained classification model. If you have never used it before to evaluate the performance of…
Spam detection is one of the machine learning projects that every data science beginner must have tried once. Recently I shared an article on how to detect spam emails and messages with machine learning which you can find here. With its…
In machine learning, accuracy is one of the most important performance evaluation metrics for a classification model. The mathematical formula for calculating the accuracy of a machine learning model is 1 – (Number of misclassified samples / Total number of…
Fake news is one of the biggest problems with online social media and even some news sites. Most of the time, we see a lot of fake news about politics. So using machine learning for fake news detection is a…
Stochastic gradient descent is a machine learning algorithm that is used to minimize a cost function by iterating a weight update based on the gradients. If you’ve never used the SGD classification algorithm before, this article is for you. In…
Detecting spam alerts in emails and messages is one of the main applications that every big tech company tries to improve for its customers. Apple’s official messaging app and Google’s Gmail are great examples of such applications where spam detection…
The classification of social media ads is all about analyzing the ads for classifying whether your target audience will buy the product or not. It’s a great use case for data science in marketing. So, if you want to learn…
Working on case studies is one of the best practices that will help you improve your problem-solving skills as a data scientist. In this article, I’m going to introduce you to some of the best data science case studies based…
In Machine Learning, binary classification consists of distinguishing two classes. For example, classifying messages as spam or not as spam. We have a bunch of machine learning algorithms for the binary classification task, so to help you choose the best…
Classification means categorizing something from a group based on some features. For example, spam detection is one of the most popular tasks of classification in the machine learning community, here we need to classify emails as spam or not spam.…
In Machine Learning, classification means predicting the class of data points. In simple words, when we are predicting the category of the target label using machine learning algorithms then this is known as classification. There are mainly 3 types of…
The price of a product is the most important attribute of marketing that product. One of those products where price matters a lot is a smartphone because it comes with a lot of features so that a company thinks a…
Linear and logistic regression are excellent statistical techniques used as machine learning algorithms to understand the relationship between features and labels in machine learning. In this article, I will take you through the difference between linear regression and logistic regression…
Clustering is used to divide data into subsets, and classification is used to create a predictive model that can be used to categorize the values of future data points. In this article, I’ll walk you through the difference between clustering…
Passive Aggressive Classifier belongs to the category of online learning algorithms in machine learning. It works by responding as passive for correct classifications and responding as aggressive for any miscalculation. In this article, I will walk you through what Passive…
In machine learning, the Naive Bayes algorithm is based on Bayes’ theorem with naïve assumptions. This makes it easier to train a model by assuming that the features are independent of each other. In this article, I will give you…
Multiclass classification means a classification problem where the task is to classify between more than two classes. Multilabel classification means a classification problem where we get multiple labels as output. In this article, I will explain the difference between multiclass…
In classification algorithms, a computer is programmed to specify to which category an entry belongs. Object detection is one of the problems where a classification algorithm can be used. In this article, I will introduce you to all the machine…
In this article, I will take you through how we can predict the US presidential elections with Python. Here, I will not train any machine learning model. I will analyze the sentiments of people for the candidates and then at…
Fake Currency Detection is a real problem for both individuals and businesses. Counterfeiters are constantly finding new methods and techniques to produce counterfeit banknotes, which are essentially indistinguishable from real money. At least for the human eye. In this article,…
In this article, I will take you through how we can classify the nationalities of people by using their names. You will be thinking about how we can classify nationalities by using just names. There is a lot about how…
In this article, I will introduce you to a text classification model with TensorFlow on movie reviews as positive or negative using the text of the reviews. This is a binary classification problem, which is an important and widely applicable…
In this article, I am going to explain how we can train a neural network model for the task of image classification with TensorFlow. For those new to TensorFlow, TensorFlow is an end-to-end open-source platform for machine learning. It has…
The first galaxy was observed by a Persian astronomer Abd al-Rahman over 1,000 years ago, and it was first believed to be an unknown extended structure. which is now known as Messier-31 or the infamous Andromeda Galaxy. From that point…
In this article, I will take you through a very famous case study for machine learning practitioners which is to predict titanic survival with Machine Learning. I will first introduce you to this case study and then I will show…
Fraud is one of the major issues we come up majorly in banks, life insurance, health insurance, and many others. These major frauds are dependent on the person who is trying to sell you the fake product or service, if…
Indeed, two people can never have the same fingerprints, it is unique in every human. But using fingerprints we can classify gender, whether it’s male or female. In this article, I will take you through a Gender Classification Model which…
Skin Cancer is one of the most common types of disease in the United States. Up to 4 Million cases have been reported dead due to skin cancer in the United States over the year. In this article, I will…
Introduction to CNN Convolutional neural networks (CNN) are primarily used to classify images or identify pattern similarities between them. So a convolutional network receives a normal color image as a rectangular box whose width and height are measured by the…
This Article is based on SMS Spam detection classification with Machine Learning. I will be using the multinomial Naive Bayes implementation. This particular classifier is suitable for classification with discrete features (such as in our case, word counts for text classification). It…
In this article, I will create a model for credit card fraud detection using machine learning predictive model Autoencoder and python. Lets start with importing libraries The data set I am going to use contains data about credit card transactions that…