K-Nearest Neighbor is a supervised learning algorithm that can be used to solve classification and 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.
Cancer classification is one area where machine learning 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 using ml to predict the presence of malignant (cancer-causing cells) or benign cells using a support vector classifier model.
Classification problems are among the most used problem statements in machine learning. We evaluate classification models using standard evaluation metrics like confusion matrix, accuracy, precision, recall, ROC and the AUC curves. In this article, we will discuss all these popular evaluation metrics to evaluate the classification models along with their inbuilt functions present in Scikit-learn.
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.
To detect whether player is genuine or false, 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.
Logistic Regression is one of the most used machine learning algorithms. It is a supervised learning algorithm where target variables should be categorical, such as positive or negative, Type A, B, or C, etc. Although the name contains the term "regression", we can also say that it can only solve classification problems.
Optimization of error function is the respiratory process for machine learning algorithms. But this error function varies for classification and regression problems. In this blog, we have discussed: 1) Definition and importance of loss function 2) Loss functions used for regression 3) Loss functions used for binary classification 4) Loss functions used for multiple classification, etc.
We categorize supervised learning into two different classes: Classification Problems and Regression Problems. Both classification and regression in machine learning deal with the problem of mapping a function from input to output. However, in classification problems, the output is a discrete (non-continuous) class label or categorical output, whereas, in regression problems, the output is continuous.
SVM, also known as support vector machines, is one of the most popular algorithms in machine learning and data science. Experts consider this one of the best "Out of box" classifiers. In this article, we will develop a thorough understanding of SVMs, relating it with SVC and Maximal Margin Classifier along with step-wise implementation.
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 blog will guide you through steps 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. In this blog, we use random forest classifier to build fraud detector.
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 is equivalent to the best algorithms. So let's learn about this algorithm in greater detail.
In this blog, you will learn classification of movie reviews into positive and negative review categories using sentiment analysis. We have also discussed: 1) Use case of sentiment analysis? 2) Data analysis for the IMDB movie review dataset 3) Steps of text or data processing, including tokenization, lemmatization, word embedding, etc.
Experts have tried several ways to predict soil fertility using machine learning methods. But classification algorithms have proven sufficient accuracy to deal with such a problem. ML algorithms like k-NN, DTs, SVM, and random forests have been used for different case studies. In this blog, we have implemented this using gradient boosting.
Recommender System is a system that could predict the future preference for a user based on their previous behavior or by focusing on the behavior of similar users. In this blog, we have discussed: 1) What is a recommender system? 2) Their various types 3) Implementation of content-based recommender system 4) Various use cases in industry.
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