Summarizing the quantitative data can help us understand them better. In this article, we’ll see various methods to summarize quantitative data by the measure of central tendency(such as mean, median, and mode) and by the measure […]

Continue readingMore Tag## Why High Dimensional Data are a Curse?

In the era of big data, massive datasets(not only in the number of samples being collected but also in the number of features) are increasingly prevalent in many disciplines and are often difficult to interpret. In […]

Continue readingMore Tag## Converting Raw Text to Numerical Vectors using Bag of Words, N_Grams and TF-IDF

If we are dealing with text documents and want to perform machine learning on text, we can’t directly work with raw text. We first need to convert the text into numbers or vectors of numbers. In this article, […]

Continue readingMore Tag## Introduction to Logistic Regression

In this article, we’ll discuss a supervised machine learning algorithm logistic regression. Logistic regression is a probabilistic classifier. It outputs the probability of a point belonging to a specific class. The output lies between [0,1]. Logistic […]

Continue readingMore Tag## Assumptions Made by Ordinary Least Squares(OLS)

Introduction: Ordinary Least Squares(OLS) is a commonly used technique for linear regression analysis. OLS makes certain assumptions about the data like linearity, no multicollinearity, no autocorrelation, homoscedasticity, normal distribution of errors. Violating these assumptions may reduce […]

Continue readingMore Tag## Introduction to K-Nearest Neighbors

The K-nearest neighbor(K-NN) classifier is one of the easiest classification methods to understand and is one of the most basic classification models available. K-NN is a non-parametric method which classifies based on the distance to the […]

Continue readingMore Tag## Linear Regression Using Statsmodels

Introduction: In this tutorial, we’ll discuss how to build a linear regression model using statsmodels. Statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as […]

Continue readingMore Tag## Machine Learning Pipeline

A machine learning pipeline bundles up the sequence of steps into a single unit. For example, in text classification, the documents go through an imperative sequence of steps like tokenizing, cleaning, extraction of features and training. […]

Continue readingMore Tag## Cross Validation

Cross-validation is an important evaluation technique used to assess the generalization performance of a machine learning model. It helps us to measure how well a model generalizes on a training data set. There are two main […]

Continue readingMore Tag## Pandas Introduction

In this article, we’ll discuss Pandas, which is the most popular python data analysis library. Data analysis is a process of cleaning, exploring, organizing, describing, and visualizing data. Pandas is mainly used for cleaning and exploring […]

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