What the KNN algorithm is and how it is used in classification tasks.
KNN algorithm is a supervised classification algorithm that is mainly used to predict which category a query point belongs to, given a bunch of data values with respect to its corresponding categories (class labels). Talking from the perspective of the classification tasks, KNN is very much similar to Naive Bayes but slightly different in terms of technicality and implementation. In Naive Bayes, we compute the likelihood by probabilistic methods, whereas in KNN, we compute distance measurements along with other extensions added.
One great advantage of KNN is…
Classification is a task of grouping things together on the basis of the similarity they share with each other. It helps organize things and thus makes the study more easy and systematic. In statistics, classification refers to the problem of identifying to which set of categories an observation or data value belongs to.
For humans, it can be very easy to do the classification task assuming that he/she has proper domain-specific…
In this article, we will see the complete analysis of “Crimes Against Women” that took place in India from 2001 to 2014.
The main agenda of this article to analyze crime data by following all the steps required for data analysis. The steps include Data Preparation, Data Cleaning, Data Wrangling, Feature Selection, Data Visualization & Comparison.
The data is about the crimes committed against women in India. The data is being recorded from 2001 to 2014. It includes crimes like -
In this article, we will step by step procedure to convert a regular matrix into a sparse matrix easily using Python.
Matrix is a type of data structure similar to an array where values are stored in rows and columns. Here, the values are of a unique type. When dealing with matrices (linear algebra) in Machine Learning and NLP, we often hear about two types of matrices as -
In this article, we will learn different ways of multiplying matrices from an easy-to-read function to an optimized code.
If you had read my previous articles on matrix operations, by now you would have already know what a matrix is. Yes, a matrix is a
2D representation of an array with
M rows and
N columns. The shape of the matrix is generally referred to as dimension. Thus the shape of any typical matrix is represented or assumed to have (
In this article, I will share my experience (the errors and issues) while deploying the app which I developed.
For a while till now, I have been working on my basic image processing app developed in Python using the frameworks and libraries like -
Let me explain how the journey of developing this app began.
First, I didn’t have any idea or plan to develop an app (that too for image processing). It is when one of my colleagues asked in our common group -
How do I re-mirror…
In this article, we will learn 3 ways of transposing a matrix from an easy to read function to an optimized code without needing any for loops.
A matrix is a 2D representation of an array with M rows and N columns. An array is a collection of identical elements or objects stored in 1 row and N columns. There are so many mathematical operations and properties that can be implemented in a matrix. One such operation is the transpose operation. Transposing a matrix is easy, just converting rows into columns and vice-versa.
The word intersection in mathematics is termed as the similar (smaller) objects between two different objects. Intuitively, we can say the intersection of objects is that it belongs to all of them.
Geometrically speaking, if we have two distinct lines (assuming these lines are two objects), the intersection of these two lines would be the point where both the lines meet. Well, in the case of parallel lines, the intersection doesn’t exist. Geographically, the common junction between two or more roads can be taken as the area or region of intersection.
In Set theory, the intersection of two objects such…
In the previous articles on morphological transformations, we learned the two important transformations namely erosion and dilation. In this article, we will implement the other transformations which are built on top of these two. They are -
We have seen a step-by-step implementation of erosion and dilation explaining the convolution method with simple matrix operations. In all of these transformations, we rely on the binary input image, structuring element, or kernel. The structuring element needs to be a square matrix which is again a binary matrix.
Like Image Erosion, Image Dilation is another important morphological operation used to increase or expand shapes contained in the input image. Think of this as “ diluting ” the image. Diluting anything requires water, here we need a structuring element or kernel.
Note: We are not expanding or increasing the image size. We are increasing the pixel strength and the size remains the same.
Mathematically, we can represent this operation in the following way -