RESHAPE THE FUTURE WITH DATA SCIENCE

MACHINE LEARNING  REGRESSION MODELLING WITH R 👉 To mention briefly,    EDA (Exploratory Data Analysis) is a method for examining datasets to highlight their significant characteristics, frequently using visual techniques.  EDA library(ggplot2) Importing the dataset t_data = read.csv('C:/Users/Asus/Desktop/VERİ BİLİMİ YÜKSEK LİSANS/R -Data Visualisation/blog/blog-7/train.csv') The "head()" function displays the first n (default value n=6) rows of a dataset. head(t_data)    x        y 1 24 21.54945 2 50 47.46446 3 15 17.21866 4 38 36.58640 5 87 87.28898 6 36 32.46387 The "tail()" function displays the last n (default value n=6) rows of a dataset. If you provide a number inside the function like "tail(t_data,10)" it will display the last ten rows. > tail(t_data)         x        y 695 81 81.45545 696 58 58.59501 697 93 94.62509 698 82 88.60377 699 66 63.64869 700 97 94.97527 "str(t_data)" will di...

                                           REGRESSION ANALYSIS-R Regression analysis is a statistical method used to model the relationship between a dependent variable and one or more independent variables. In R, the most commonly used package for regression analysis is lm() (linear model) which is part of the base R installation. Another popular package is glm() (generalized linear model) which is also part of the base R installation. Both of these functions can be used to perform a variety of regression analyses including simple linear regression, multiple linear regression, and logistic regression. Additionally, there are several specialized packages in R such as lme4 and nlme that can be used to perform more advanced types of regression analysis such as mixed-effects and non-linear models. Here is an example of how to perform a simple linear regression using the lm() funct...

The most time-consuming and tedious data science endeavor is cleaning and organizing data. DataExplorer is one of the resources available that has the express goal of reducing the 80% and making it pleasurable. As a result, being exceedingly user-friendly is a basic design principle. One function call is typically all you need. DataExplorer is an R package that provides a set of functions for creating summaries and visualizations of data. It is designed to make it easy for users to quickly get an overview of their data and identify patterns and trends. Here are some key features of DataExplorer:   👉Provides a variety of summary statistics and visualizations for different types of data, including numerical, categorical, and text data.   👉Allows users to easily create plots, tables, and summary statistics for a single variable or for multiple variables.   👉Offers options for customizing the appearance and formatting of plots and tables.   👉Can handle large datasets...

There are several built-in themes available in R. Some common themes include: 👉 theme_grey() applies a simple grey background to the plot. 👉 theme_bw() removes colors and grid lines from the plot. 👉 theme_minimal() and theme_light() provide a more modern and clean look to the plot.  👉 theme_classic() has a white background, grid lines, and a black axis line. It also includes a title, axis labels, and a legend, if applicable. This theme is often used for traditional data visualizations and is a good choice for plots that will be printed or included in reports. 👉 theme_dark()  i s often used for data visualizations that will be displayed on a screen, such as in a dashboard or web application. The dark background helps to make the data points and other visual elements stand out, making the plot easier to read. 👉 theme_linedraw() is a function in the R programming language that is used to apply the "linedraw" theme to a plot or graph. This theme is based on the "li...