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.

Linear Regression is a supervised machine learning algorithm used to solve regression problems. In this blog, we have discussed: 1) What is linear regression? 2) Various types 3) The loss function for linear regression 4) Ordinary Least Squares (OLS) method 5) Polynomial regression 6) Python implementation of linear regression.

We evaluate the performance of our regression models in Machine learning using standard metrics. In this article, we will be discussing all the standard evaluation metrics namely MAE, MAPE, MSE, RMSE, R-Squared and Adjusted R-Squared, for regression models used to compare two different models on the same dataset.

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.

Prediction of wine quality can be made easy with machine learning and data science techniques. In this blog, we have discussed: 1) Why do we need a machine learning model for wine quality assessment? 2) Factors that affect wine quality 3) Various ML models to predict wine quality 4) Implementation of predicting wine quality using k-NN regressor

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.

Both classification and regression in machine learning deal with the problem of mapping a function from input to output. However, when it comes to classification problems, 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.

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.

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 two python libraries: NumPy library for numerical operations and Matplotlib library to visualize graphs.

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.

Decision tree algorithm in machine learning is a hierarchical breakdown of a dataset from root to leaf nodes based on attributes to solve a classification or regression problem. They are non-parametric supervised learning algorithms that predict a target variable's value. We have discussed various decision tree implementations with python.

Machine learning techniques can accurately predict the house price by considering all important features. In this blog, we have discussed: 1) Why do we need machine learning models for house price prediction? 2) What are the factors that affect house prices? 3) Implementation of predicting house prices using Support Vector Regressor

When we talk about the machine learning model, one question comes to mind: What are the errors associated with that prediction? Bias and Variance are those error-causing elements, and ideas about these errors will help to diagnose the model. Bias, Variance and Bias-Variance tradeoffs are frequently asked questions in machine learning interviews.

We need to clean the text data before feeding it to machine learning algorithms. Fortunately, Python has excellent support for NLP libraries (NLTK, spaCyto) to ease text analysis. In this blog, we will learn: 1) Real-time working on the sentiment analysis dataset 2) Techniques for cleaning text data. 3) Exploratory analysis of text data.

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.

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.

Using machine learning, we can predict the life expectancy of a person. In this blog, we will explore parameters affecting the lifespan of individuals living in different countries and learn how life span can be estimated with the help of machine learning models. We will also focus on the application of linear regression in predicting life expectancy.

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.

Based on the nature of input that we provide to a machine learning algorithm, machine learning can be classified into four major categories: Supervised learning, Unsupervised learning, Semi-supervised learning, and Reinforcement learning. In this blog, we have discussed each of these terms, their relation, and popular real-life applications.