Python's Dictionary in Details
Dictionary is a composite datatype in the Python programming language. In a way, it is similar to lists. Lists are a collection of elements. Dictionaries are a collection of key,…
Natural Language Processing
Text Files Processing, Cleaning, and Classification of Documents in R
With the increasing number of text documents, text document classification has become an important task in data science. At the same time, machine learning and data mining techniques are also…
Exploratory data Analysis in Python
Exploratory Data Analysis, Visualization, Prediction Model in Python
This article focuses on a data storytelling project. In other words Exploratory data analysis. After looking at a big dataset or even a small dataset, it is hard to make…
ANOVA and ANCOVA
Dissecting 1-Way ANOVA and ANCOVA with Examples in R
ANOVA (Analysis of Variance) is a process to compare the means of more than two groups. It can also be used for comparing the means of two groups. But that’s unnecessary.…
A Complete Guide to Confidence Interval, t-test, and z-test in R
The confidence interval, t-test, and z-test are very popular and widely used methods in inferential statistics. They are so important because, for any research or data analysis, we can only…
Take Full Control Over the Subplots in Matplotlib
Using subplots and putting multiple plots in one figure can be very useful in summarizing a lot of information in a small space. They are helpful in making reports or…
A Complete Tutorial on Logistic Regression, and Inference in R
One of the most basic, popular, and powerful statistical models is logistic regression. If you are familiar with linear regression, logistic regression is built upon linear regression. It uses the…
A Complete Image Classification Project Using Logistic Regression Algorithm
Logistic regression is very popular in machine learning and statistics. It can work on both binary and multiclass classification very well. I wrote tutorials on both binary and multiclass classification…
A Complete Overview of the Probability Distributions with Examples, R Implementation
Our end goal is to draw inferences from the population. We first need to learn about probability because it is the underlying base for statistical inferences, predictive models, and machine…
Detailed Guide to Multiple Linear Regression Model, Assessment, and Inference
Linear regression is one of those old-school statistical modeling approaches that are still popular. With the development of new languages and libraries, it is now in a much-improved version and…