supervised-learning

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Cancer Classification Model Using Machine Learning

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.

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How Uber uses Machine Learning to Facilitate Surge Price?

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.

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Learn to build Machine Learning Model using Python

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.

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Credit Card Fraud Detection Using Machine Learning

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.

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Methods to Check the Performance of Classification Models

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.

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Evaluation Metrics for Regression Models in Machine Learning

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.

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Naive Bayes Classifier in Machine Learning

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.

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K-Nearest Neighbors (KNN) Algorithm

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.

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PUBG Cheaters' Detection using Machine Learning

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.

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Decision Tree: A Tree-Based Algorithm in Machine Learning

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.

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Optical Character Recognition using Logistic Regression (Linear Models)

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.

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Support Vector Machines: An "out of the box" classifier

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.

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Boston House Price Prediction Using Support Vector Regressor

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.

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Bias-Variance Tradeoff in Machine Learning

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

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Pre-processing of Text Data in Machine Learning Part 1

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.

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Logistic Regression in Machine Learning

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.

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Linear Regression

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

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Sentiment Analysis for Classifying Sentiment of Movie Reviews

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

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Wine Quality Prediction Using k-NN Regressor

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

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Car Resale Value Prediction Using Random Forest Regressor

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.

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Life Expectancy Prediction Using Linear Regression

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

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Soil Fertility Prediction Using Machine Learning

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.

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How to build Recommender System using Machine Learning?

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.

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Classification and Regression Problems in Machine Learning

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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Supervised, Unsupervised, And Semi-Supervised Learning With Real-Life Usecase

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.

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