Seaborn's 'axes_style()' function allows us to customize the style of plots by modifying various parameters. It returns a dictionary of parameters that control the appearance of the plot elements, such as colors, fonts, gridlines, and more. To view these parameters, we can print the dictionary returned by 'axes_style()'. Importing Required Libraries Before we begin, let's import the necessary libraries. We need to import Seaborn by using the following command. import seaborn as sns Viewing Parameters To view the available parameters in 'axes_style()', we can simply print the dictionary returned by the function. Example When we run the below code, ... Read More

Time complexity is a measure of time, which determines the time required for the algorithm to execute. It also checks how the running time is being increased when the size of the inputs is getting increased. The time complexity is represented with the notation capital O, which sets an upper bound in the worst case performance of an algorithm. Whenever we design an algorithm we should keep in mind not only about the correctness but also about the time complexity and performance characteristics. For example, if an algorithm has time complexity O(n) that would take twice of O(n) to execute ... Read More

The name-tools module is a python library which provides us the tools to work with the names. These are used in many applications likely data cleaning, text processing and Natural Language Processing etc. It has a several functions for comparing and managing the names. Installing name-tools Before working with the name-tools, we need to install it in the python environment. The following is the process for installing the name-tools. pip install name-tools After executing the above code, if you see the text below, then the installation is considered successful. Collecting name-toolsNote: you may need to restart the kernel ... Read More

Introduction Data mining is a powerful technique used to extract meaningful insights and patterns from large datasets. It involves the application of statistical and computational algorithms to uncover hidden relationships and trends within the data. One popular tool for data mining is the programming language R. In this article, we will delve into the world of data mining with R, exploring its capabilities and applications. Understanding Data Mining Data mining is the process of discovering patterns, relationships, and insights from large datasets. It involves several steps, including data preprocessing, exploratory data analysis, model building, and evaluation. Data mining techniques can ... Read More

The NumPy library has a wide range of tools which perform the comparison and filtering of the arrays. The comparison of arrays will be done element by element in row wise and column wise based on the dimension of the array. When we want to retrieve a particular array element, then we can apply the filtering. NumPy is abbreviated as Numerical python, which is used to perform mathematical and scientific calculations on multidimensional arrays and matrices. There are different functions and methods available in NumPy to perform the filtering and comparison of the arrays. Comparing NumPy Arrays Following are ... Read More

Introduction Big data analytics has become an integral part of decision-making and business intelligence across various industries. With the exponential growth of data, organizations need robust tools and techniques to extract meaningful insights. R, a powerful programming language and software environment, has gained popularity for its extensive capabilities in data analysis and statistical computing. In this comprehensive guide, we will explore how R can be effectively utilized for big data analytics, covering various aspects and techniques. Understanding R for Big Data Analytics R Programming Language: R is an open-source programming language that provides a wide range of statistical and graphical ... Read More

Introduction R, a powerful programming language, offers a wide range of packages specifically designed for financial analysis and modeling. These packages provide robust tools and functions to handle various aspects of finance, including data manipulation, statistical analysis, portfolio management, risk assessment, and visualization. In this article, we will explore some of the popular R packages for finance and delve into their key features and applications. Data Manipulation Packages dplyr − dplyr is a versatile package that simplifies data manipulation tasks in R. It provides a concise grammar for data manipulation, allowing users to easily filter, arrange, select, mutate, and summarize ... Read More

Introduction Machine learning has revolutionized the field of artificial intelligence and data analysis. With the ever-increasing availability of data and computational power, machine learning algorithms have become essential tools for extracting valuable insights and making predictions from large and complex datasets. Among the various programming languages used in machine learning, R stands out as a popular choice due to its versatility and extensive library support. In this article, we will provide a comprehensive introduction to R for machine learning, exploring its capabilities, libraries, and applications. What is R? R is a powerful statistical programming language widely used for data analysis, ... Read More

Introduction Data visualization plays a crucial role in analyzing and communicating insights from complex datasets. With the advent of advanced data visualization tools and techniques, researchers and analysts can now present data in more meaningful and engaging ways. In this article, we will explore advanced data visualization techniques using R, a powerful programming language widely used for data analysis and visualization. We will delve into various subheadings that cover different aspects of data visualization in R, providing detailed insights and examples. Exploratory Data Analysis (EDA) Visualizations Exploratory Data Analysis (EDA) involves visualizing and understanding the underlying patterns and relationships in ... Read More

Introduction Data analysis plays a crucial role in today's data-driven world. It involves extracting valuable insights from large and complex datasets to make informed decisions. R is a powerful programming language and software environment widely used for statistical computing and graphics. In this article, we will explore the fundamentals of data analysis with R, its benefits, and various techniques used in the process. What is R? R is an open-source programming language and software environment specifically designed for statistical computing and graphics. It provides a wide range of tools for data manipulation, visualization, and statistical analysis. R is highly extensible ... Read More