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Handling Mixed Numbers

Handling Mixed Numbers Mixed numbers are a combination of a whole number and a fraction. They are different from improper fractions, which have a numerator that is larger than or equal to the denominator. Definition of Mixed Numbers Mixed numbers are written as a whole number followed by a fraction. For example, 3 1/2 is a mixed number. It means three wholes and one half. Converting Mixed Numbers to Improper Fractions To convert a mixed number to an improper fraction, you can follow these steps: Multiply the whole number by the denominator of the fraction Add the numerator of the fraction to the result of the previous step Write the result as the numerator over the denominator of the fraction For example, to convert 3 1/2 to an improper fraction: 3 x 2 = 6 6 + 1 = 7 7/2 Converting Improper Fractions to Mixed Numbers To convert an improper fraction to a mixed number, you can follow these steps: Divide the numerator by the denominator Write th...
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Implementation of a machine learning model

Let's implement a machine learning model So, now we study many things about ml. Its time to use it and build a machine learning model. We are gonna train a model on  House Prices - Advanced Regression Techniques You can find it on kaggle by this link Implementation Use this link to access the kaggle notebook read it this will give you great understanding of how ml models created Click here to go notebook
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The most powerful kernel trick of SVM

 The kernel trick is most important and powerful technique of SVM . Linear VS Non-Linear dataset linear vs non-linear figure Problem Statement Currently we have learn how to apply SVM algorithm at linear datasets, but what if we have non linear dataset. Solution of Problem Solution is kernel trick. Kernel Trick The Kernel trick is trick where we add  many SVMS  models by bagging,voting,stacking and boosting or we can use SVM class to implement it. Implementation To implement it follow code given below- from sklearn.svm import SVC svc=SVC() svc.fit(X_train,y_train) svc.score(X_test,y_test)
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SVM Intuition

SVM Intuition SVM(Support Vector Machine) is knows as family of linear models but it is really good than linear models it can draw line also and work as well in non-linear dataset also NOTE-Linear models<SVM always this is not true remember The target of LinearSVM classifier is to maximize margin
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