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.

These days, the support of libraries and frameworks is easily accessible in machine learning. But in this article, we will implement a basic machine learning project without using frameworks like Scikit-learn, Keras, or Pytorch. We will use the NumPy library for numerical operations and Matplotlib to visualize the graphs.

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.

When we build a solution for any regression problem, we compare its performance with the existing work using standard metrics, like measuring distance in meters, plot size in square feet, etc. Similarly, we need some standard evaluation metrics to evaluate two regression models. Some of them are MAE, MSE, RMSE, and R-Squared.

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.

A Decision Tree (DT) is a hierarchical breakdown of a dataset from the root node to the leaf node based on the attributes to solve a classification or regression problem. They are non-parametric supervised learning algorithms that predict a target variable's value by learning rules inferred from the data features.

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.

The surrounding environment highly influences house prices but machine learning techniques can help us accurately predict the house price by taking account of all important features.

Bias, Variance, and Bias-Variance tradeoff are the most popular terms in machine learning and the most frequent questions asked in machine-learning interviews.

Text data pre-processing ensures optimal results when executed properly. Fortunately, Python has excellent support of NLP libraries such as NLTK, spaCy, and Gensim to ease our text analysis.

Logistic Regression is one of the most used machine learning algorithms in industry. It is a supervised learning algorithm where the target variable should be categorical, such as positive or negative, Type A, B, or C, etc. We can also say that it can only solve the classification problems. Although the name contains the term "regression", it is only used to solve the classification problem.

Linear Regression is a supervised machine learning algorithm used to solve regression problems.

Classification of movie reviews into positive and negative review categories using sentiment analysis and NLP is discussed in this article..

Prediction of wine quality is a challenging task for humans but using machine learning and data science techniques, it can be made easy.

Machine Learning has become a tool used in almost every task that requires estimation. Companies like Cars24 and Cardekho.com uses Regression analysis to estimate the used car prices. So we need to build a model to estimate the price of used cars. The model should take car-related parameters and output a selling price.

With ongoing advancements in Machine Learning and Data Science, we can precisely predict the remaining life span of a person given the essential parameters.

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.

Based on the nature of input that we provide to a machine learning algorithm, machine learning can be classified into 4 major categories - Supervised Learning, Unsupervised Learning, Semi-Supervised Learning, Reinforcement Learning.