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.

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.

Customer Segmentation is splitting the organization's customer base into smaller groups that reflect similarities in their behavior. It helps businesses develop customer-focused strategies, make segment-wise decisions, and maximize the value of each customer to the company. In this blog, we explore the potential of clustering algorithms to accomplish the above task.

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.

In Machine Learning, Time Series Forecasting refers to the use of statistical models to predict future values using the previously recorded observations.

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.

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.

Machine learning technologies are now able to predict the individual's personality based on their social media usage. Personality-based communications are highly used in dating apps and recommendation systems.

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.

K-NN implementation and Gmail, Yahoo, and Outlook case studyIn 2019, on average, every person was receiving 130 emails each day, and overall, 296 Billion emails have been sent in that year.