https://github.com/mariamagro/unsupervisedtools_statisticallearning

The goal of this project was to explore different unsupervised tools available in R in order to get some useful insights about a dataset. In this case, Twitch information was analysed using data about the top 1000 streamers on this livestream platform.
https://github.com/mariamagro/unsupervisedtools_statisticallearning

Last synced: about 1 month ago
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The goal of this project was to explore different unsupervised tools available in R in order to get some useful insights about a dataset. In this case, Twitch information was analysed using data about the top 1000 streamers on this livestream platform.

• Host: GitHub
• URL: https://github.com/mariamagro/unsupervisedtools_statisticallearning
• Owner: mariamagro
• Created: 2023-11-07T19:20:04.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-08-18T16:12:52.000Z (5 months ago)
• Last Synced: 2024-08-18T17:39:51.184Z (5 months ago)
• Language: RMarkdown
• Size: 1.65 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Unsupervised Tools: An Insight into Twitch Streamers

**Author:** María Ángeles Magro Garrote  

**Year:** 2022

## Overview

This project aims to analyze a dataset of the top 1000 Twitch streamers from 2020 to answer key questions about what factors contribute to a streamer's success on the platform. Although Twitch is primarily known for its gaming content, it features a variety of categories such as "Just Chatting" and sports, attracting 31 million daily viewers. The top streamers earn substantial revenue through ads, subscriptions, and donations.

## Key Questions

1. How does the language of the stream impact viewership and partnership status?

2. Does having a peak viewership affect a channel's metrics?

3. Is there a correlation between the number of viewers and the number of followers?

4. What factors are indicative of a successful streamer?

## Dataset

The dataset used in this project was created by Aayush Mishra and contains data from 2020. You can access it [here](https://www.kaggle.com/datasets/aayushmishra1512/twitchdata).

## Required R Libraries

To run the analysis, ensure you have the following R libraries installed:

- `VIM`

- `mice`

- `tidyverse`

- `gridExtra`

- `GGally`

- `factoextra`

- `lubridate`

- `quantmod`

- `mclust`

- `cluster`

- `kernlab`

### Installation

You can install the required packages using the following commands in R:

```r

install.packages(c("VIM", "mice", "tidyverse", "gridExtra", "GGally", "factoextra", "lubridate", "quantmod", "mclust", "cluster", "kernlab"))

```

## Running the Analysis

### PART 1: Data Preprocessing

- Load the data and perform initial preprocessing.

- Tasks include renaming variables, checking for missing values, duplicates, outliers, scaling, and transforming boolean variables.

### PART 2: Exploratory Data Analysis (EDA)

- Perform exploratory analysis to understand the data better.

- Includes correlation analysis and visualization of various metrics.

- General conclusions are obtained and tested later.

### PART 3: Principal Component Analysis (PCA)

- Conduct PCA to reduce dimensionality and identify key components.

### PART 4: Factor Analysis

- Perform factor analysis to uncover underlying factors influencing the data.

### PART 5: Clustering

- Apply clustering methods (k-means, hierarchical, PAM) to uncover patterns and insights.

## Final Insights

The conclusions of this analysis can be seen in the notebook (Rmd) or HTML.