SVM is one of the simplest and coolest ML algorithms. For non-linear problems, it literally follows the saying “looking at a problem from different perspectives”. Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection. : sklearn 1.4. Support Vector Machines Support vector machines (SVMs) are a set of supervised learning methods used for classification , regression and…scikit-learn.org

Linear regression is a supervised machine learning technique used to determine the relation between dependent and independent variables. It is useful in predicting continuous numerical variables, for example predicting the price of house, or sales value for a product. Linear regression tries to discover the strength and the relation between…

Clustering is an unsupervised learning process of segmenting observations, or dataset into number of groups such that data points have high similarity within the group . It is one of the most frequently used analytical process. It helps tremendously in finding of homogeneous groups across the dataset. Clustering algorithms uses…

Ensemble learning refers to process of combining predictions from two or more models. Intuitively it could be imagined as suppose you’re planning to go on a trip with your friends and you’re confused over your destination being mountain or beach. You go to your friends and consult with them about…

KNN is perhaps one of the simplest ML models out there. It is a non-parametric model, which means it does not make any assumption on underlying data distribution. It is a lazy learning algorithm as it does not learn from the training set immediately instead the learning process is performed…

Decision trees are a group of divide and conquer method that uses inverted tree-like structure to predict the outcome for our problem. The model predicts the value of a target variable by using simple decision rules inferred from the available features. Decision tree is one of the most powerful predictive…

The primary objective of any classification model is to predict the probability of an observation belonging to a particular class, also called as class probability. …

Model evaluation is one of the crucial part in data science projects. It is through evaluation that we understand how our model has performed and the kind of tuning that is required in order to achieve desired results. In case of classification, we have our target variable (y) in discrete…

Rapid prototyping is the process to quickly develop a viable working model. It need not be the final solution, or even the best of all probable ones but it rapidly provides us with the framework and a baseline to improve our efficiency. We are frequently asked to quickly develop models…