Cancer classification is one area where ML can deliver a robust predictive model based on given observations to identify the cancer possibility. In this article, we have built a cancer classification model to predict the presence of malignant (cancer-causing cells) or benign cells using a support vector classifier model.
Uber ride prices are not constant like public transport. We might have observed such variations while using the cab service. To calculate this variation, Uber uses a Machine Learning-powered Surge Pricing algorithm. In this article, we will build a machine learning model to predict the serge multiplier based on different weather conditions.
This article will guide you through the step of detecting fraudulent transactions performed on credit cards by developing a machine learning model. Several classification algorithms can perform best and are easily deployable, like support vector machines, logistic regression, etc. We will be using a Random Forest Classifier to build our fraud detector.
Classification problems are among the most used problem statements in Machine Learning. We evaluate our classification models with available models using standard evaluation metrics like Confusion matrix, Accuracy, Precision, Recall, ROC. In this article, we will discuss some of the popular evaluation metrics used to evaluate the classification models.
Naive Bayes is a popular supervised machine learning algorithm that predicts the categorical target variables. This algorithm makes some silly assumptions while making any predictions. But the most exciting thing is: It still performs better or equivalent to the best algorithms. So let's learn about this algorithm in greater detail.
K-Nearest Neighbor is a supervised learning algorithm that can be used to solve classification as well as regression problems. This algorithm learns without explicitly mapping input variables to the target variables. It is probably the first "machine learning" algorithm, and due to its simplicity, it is still accepted in solving many industrial problems.
To detect whether the player is genuine or false in the game, BGMI (PUBG) uses a state-of-the-art machine learning approach to predict the presence of cheaters. It collects players' data, draws meaningful results, and categorizes cheaters into separate categories. They use a supervised learning approach to predict the occurrence of impossible events.
Character recognition is a primary step in recognizing whether any text or character is present in the given image using machine learning. Google, Microsoft, and many more technical giants use Optical Character Recognition(OCR) techniques to solve various tasks, including spam classification, automatic reply, number-plate detection, etc.
SVM is one of the most popular algorithms in the domain of machine learning and data science. Since the discovery of this algorithm in the 1990s, it has been widely popular among experts. The idea behind this algorithm is very intuitive, and experts consider this one of the best “Out of the box” classifiers. In this article, we will try to develop the understanding of SVMs from a beginner level to an expert level.
Classification of movie reviews into positive and negative review categories using sentiment analysis and NLP is discussed in this article..
Optimization of error function is the respiratory process for Machine learning algorithms. But this error function varies for classification and regression problems.
The task of predicting soil fertility is not new to Machine Learning. Experts and consultants have tried several possibilities to use different machine learning methods to predict it. Classification algorithms have proven sufficient accuracy to deal with such a problem. ML algorithms such as k-NN, DTs, SVM, and, Random Forests have been used for different case studies on soil fertility. In this article, we will be using Gradient Boosting.
Recommender System refers to a kind of system that could predict the future preference for a user based on his/her previous behavior or by focusing on the behavior of similar kind of users.
Both classification and regression deal with the problem of mapping a function from input to output. However, when it comes to classification, the output is a discrete (non-continuous) class label or categorical output. While on the other hand, when the problem is a regression problem, the output is continuous.
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