/Netflix-Analysis-using-R

Primary LanguageHTML

Netflix Analysis using R

This project involves analyzing the Netflix dataset using R programming language. The dataset consists of two CSV files: show.csv and MaturityType.csv and one xlsx file Production.xlsx. The show.csv file contains information on Netflix movies and TV shows, such as title, description, release date, rating, and more. The MaturityType.csv file contains the description of maturity type. Production contains contains information on the production of TV shows and movies across various countries and maturity types, for the years 2017-2021.

The analysis involves answering several questions about the dataset, such as:

  • Are there any duplicate rows in the Show dataset? If so, how many of them and how to remove them?
  • How to load the dataset into two dataframes, rename columns, and make sure date values are in the correct type?
  • Which TV shows produced by a specific country are designed for "TV Show for Mature Audiences" in a specific year?
  • Which production countries have the highest number of TV shows that are designed for "TV Show Suitable for General Audiences" and what are the average IMDB scores?
  • How to compare the distribution of IMDB scores for movies produced after 2010 by the United States and by Canada using boxplots?
  • Which are the top 5 movies suitable for children less than 7 years old based on age-appropriateness and educational value?
  • Identify the top five Asian countries for Netflix to target in order to further grow their market?

The analysis is done using R programming language and several packages, such as tidyverse, lubridate, and ggplot2.

Requirements

To run the R script, you need to have the following:

  • R programming language installed (version 4.0.5 or higher)
  • RStudio Desktop installed (version 1.4.1106 or higher)
  • The following R packages installed: tidyverse, lubridate, and ggplot2

How to use

To run the analysis, follow these steps:

  1. Download the Show.csv and MaturityType.csv files from the dataset repository.
  2. Open the Netflix.Rmd file in RStudio.
  3. Install the necessary packages by running the following code in the R console:
install.packages(c("tidyverse", "lubridate", "ggplot2"))
  1. Load the necessary packages by running the following code in the R console:
library(tidyverse)
library(lubridate)
library(ggplot2)
  1. Run each code chunk in the Netflix.Rmd file to see the analysis results.

Conclusion

In conclusion, this project shows how R programming language can be used to analyze a dataset and answer several questions related to the dataset. The analysis includes data cleaning, data manipulation, and data visualization using various R packages. The code and results can be used as a starting point for further analysis or as a reference for similar projects.