Panda’s Series- In the last post, I explained how to create a panda’s series. Further, a pandas series has a lot of you often need to analyze, visualize and clean data. In this post, I will be explaining min(), max(), mean(), median(), and mode functions. min() Function- import pandas as pd lst=[2, 4, 6, 8, […]
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Pandas Series Series is like one dimensional array or a column in a table and can be hold any type data. Furthermore, pandas series can be created in two ways. 1- Create a Pandas Series with Default Indices- In this method, you can create a series by passing list inside Series() method. As a result,
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K-Means clustering is an unsupervised machine learning algorithm which partitions n instances into k clusters by similarity. As K-Means clustering is an unsupervised learning algorithm, therefore instances will not have labels. As K-Means clustering is an unsupervised learning algorithm, training instances will not have labels. Furthermore, to make you understand K-Means clustering algorithm, I will
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K-Nearest Neighbors algorithm (KNN) K-Nearest Neighbors algorithm (KNN) is a very important supervised machine learning algorithm and one should start from this algorithm. It is easy to understand compare to other algorithms and does not involve complex mathematical concepts. In this post, I will explain k-Nearest Neighbors algorithm using Irish flowers data set. From
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Covariance and Correlation- Covariance and correlation both measure linear relationship between two variables. However, they differ at some points. In this post I will explain covariance and correlation and how they differ from each other. Covariance between two variables is written as Cov(X,Y) and is defined as Calculation of Covariance If you look at the
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Group in Algebra Let G be a non-empty set and * (multiplication) be a binary operation defined on G. Then algebraic structure (G, *) is called group if it satisfies the following axioms. 1- Closure property If a∈G, and b∈G then a*b∈G it is called closure property. of G 2- Associativity If a∈G, b∈G ,
Variance of a Continuous Random Variable If X is a random variable having mean μ , then variance of random variable X is denoted as Var(X) and read as variance of X and defined as. Var(X)=E[(X-μ)²] Where Variance of a random variable measures dispersion, that means how for values spread out from mean or expected
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Expectation of a Continuous Random Variable Expectation of a continuous random variable is defined as Suppose a continuous random variable X is uniformly distributed on [a, b]. Density function of uniformly distributed random variable X is Expectation of uniformly distributed random variable X is See Video See Also: Expectation in Statistics Expectation of a Discrete
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Mathematical or Classical Probability- If a random experiment is conducted which results in n exhaustive, mutually exclusive, and equally likely outcomes and there are m favorable cases of occurrence of an event E. Then probability of happening event E is P(E)=m/n Suppose you are tossing a coin and want to get probability of getting head
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Joint and Marginal Probability Mass Function For UploadingIf (X,Y) is a two-dimensional discrete random variable, then joint probability mass function of X and Y denoted by pxy and is defined as pxy(xi,yj)=P(X=xi,Y=yj) If you toss three coins the following sample space you will get. S={TTT, TTH, THT, THH, HTT, HTH, HHT,HHH} X—- Occurrence of heads
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