## Correlation

In many practical scenarios, we might come across the situation where observations are available on two or more variables like heights and weights of the person ,expenditure on advertisement and Read more…

In many practical scenarios, we might come across the situation where observations are available on two or more variables like heights and weights of the person ,expenditure on advertisement and Read more…

Sometimes it is difficult to find the linear decision boundary for some classification problems and if we project the data into higher dimension then we will get the hyperplane that Read more…

In last article we learned about the types of machine learning algorithm. Lets us explore one of those techniques – Simple Linear Regression. In simple linear regression, we need to Read more…

SVM (Support Vector Machine) is a supervised machine learning algorithm which is mainly used to classify data into different classes. SVM can be used for both classification and regression problems.Though Read more…

In many practical scenarios, we might come across the situation where observations are available on two or more variables like heights and weights of the person ,expenditure on advertisement and Read more…

Clustering – As the word is self explanatory that here we have group of similar points. Clustering is the method of unsupervised learning and is used for statistical data analysis Read more…

KNN(K-Nearest Neighbours) is the non-parametric and supervised machine learning algorithm used for classification as well as regression. By non-parametric, we meant that it does not make any assumptions about the Read more…

Multicollinearity It occurs when two or more independent variables in dataset are correlated to each other or to the linear combination of two or more independent variables. Multicollinearity plays an Read more…

In last article we learned about the types of machine learning algorithm. In this article let us explore one of those techniques. Simple linear regression. We learnt that in———– y Read more…

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