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I am a Business Analytics student, keenly interested in Supply Chain and Marketing Analytics.

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K-Means Clustering – One rule to group them all

Photo by Ellen Qin on Unsplash

3 Pandas functions to rule the data frames

Introduction Pandas is an extensive Python library used by Data Scientists in a varied number of fields. If you have worked in Microsoft Excel or MySQL, you are already familiar with relational data structures. You must be comfortable with the data arranged in rows and columns. I came to know about the library while learning machine learning in Python. Pandas not only offer you tools to carry out extensive data analysis and statistical inferences but because it is built upon Numpy (an another important Python library) it proves to be a platform to build highly complex machine learning and artificial neural network algorithms. The three main data structures provided by Pandas are Data Series (One dimensional), Data Frames (Two dimensional) and Data Panels (multi-dimensional). Pandas is equipped with an enormous number of functions and classes that give you enough power to deal with any data analysis problem. We will in this article discuss the three main functions for data frames tha

Decision Trees – How to draw them on paper

This article will discuss: Decision Trees – A famous classification algorithm in supervised machine learning The basics behind decision trees and how to develop one without a computer What you are already supposed to know: Basics of Statistics When it comes to classification problems in machine learning, algorithms like Logistic Regression , Discriminant Analysis etc. are the ones that come into one’s mind. There is another type of classification algorithm that is highly intuitive, easy to interpret and understand called the Decision Tree algorithm. The tree-based models can be used for both regression and classification but we will discuss only the case of classification here. A typical decision tree is drawn below to make you familiar with the concept: The above decision tree shows the chances of finding a TV in a random household. As it can be easily read, the above tree suggests that if a person has a monthly income of more than 1000$ he will be hav

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