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Mindblown: a blog about philosophy.

  • Q.6 How are KNN and K-means clustering different?

    Firstly, KNN is a supervised learning algorithm. In order to train this algorithm, we require labeled data. K-means is an unsupervised learning algorithm that looks for patterns that are intrinsic to the data. The K in KNN is the number of nearest data points. On the contrary, the K in K-means specify the number of…

  • Q.5 How to find the positions of numbers that are multiples of 4 from a series?

    For finding the multples of 4, we will use the argwhere() function. First, we will create a list of 10 numbers – s1 = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) np.argwhere(ser % 4==0) Output > [3], [7]

  • Q.4 How will you verify if the items present in list A are present in series B?

    We will use the isin() function. For this, we create two series s1 and s2 –

  • Q.3 How will you handle missing values in data?

    There are several ways to handle missing values in the given data-

  • Q.2 How will you explain linear regression to a non-tech person?

    Linear Regression is a statistical technique of measuring the linear relationship between the two variables. By linear relationship, we mean that an increase in a variable would lead to increase in the other variable and a decrease in one variable would lead to attenuation in the second variable as well. Based on this linear relationship,…

  • Q.1 What do you understand by the term Normal Distribution?

    Normal Distribution is also known as Gaussian Distribution. It is a type of probability distribution that is symmetric about the mean. it shows that the data is closer to the mean and the frequency of occurrences in data are far from the mean.

  • Applications of Bayes’ Theorem

    1. Spam Filtering The first and foremost application of Naive Bayes is its ability to classify texts and in particular, spam emails from non-spam ones. It is one of the oldest spam filtering methodology, with the Naive Bayes spam filtering dating back to 1998. Naive Bayes takes two classes – Spam and Ham and classifies…

  • Naive Bayes Theorem

    Naive Bayes is a powerful supervised learning algorithm that is used for classification. The Naive Bayes classifier is an extension of the above discussed standard Bayes Theorem. In a Naive Bayes, we calculate the probability contributed by every factor. Most we use it in textual classification operations like spam filtering. Let us understand how Naive…

  • Bayes’ Theorem – The Forecasting Pillar of Data Science

    Are you planning to become a data scientist? If yes, you must read this extensive article on Bayes’ Theorem for Data Science. No data scientist can work without a complete understanding of conditional probability and Bayesian inference. So, today, we will discuss the same with the help of examples and applications. More importantly, we will discuss…

  • Data Science K-means Clustering

    One of the most popular Machine Learning algorithms is K-means clustering. It is an unsupervised learning algorithm, meaning that it is used for unlabeled datasets. Imagine that you have several points spread over an n-dimensional space. In order to categorize this data on the basis of their similarity, you will use the K-means clustering algorithm. In this…

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