📊 Big Data Analysis

This repository contains multiple data analysis projects implemented in R. The projects focus on extracting insights from real-world datasets using data visualization and statistical techniques.

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📁 Repository Structure

• Big Data Analysis Using R.R → Main R script containing analysis for multiple datasets.
• Big Data Analysis Using R.docx → Documentation of the analysis and results.
• README.md → Project overview (this file).

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🔑 Key Analyses

1. 🎮 Video Game Sales Analysis

• Dataset: vgsales.csv
• Objective: Identify sales distribution across genres and find the highest selling game genre.
• Techniques Used:
  • Pie chart visualization of global sales by genre
  • Percentage contribution analysis

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2. 🚴‍♂️ Seoul Bike Sharing Demand

• Dataset: SeoulBikeData.csv
• Objectives:
  • Analyze seasonal and monthly demand for bike rentals
  • Study relationships with weather conditions
• Techniques Used:
  • Line charts for bike count and temperature trends
  • Pie chart of bike rentals by season
  • Scatter plots for bike count vs temperature and bike count vs rainfall
  • Bar charts for holiday vs non-holiday usage

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3. 📱 Call Voice Quality Analysis

• Datasets:
  • CallVoiceQualityExperience-2018-April.csv
  • CallVoiceQuality_Data_2018_May.csv
• Objectives:
  • Evaluate call quality across states, operators, network types, and conditions (indoor/outdoor/travelling).
• Techniques Used:
  • Bar charts for operator quality ratings, state-wise performance, and network type analysis
  • Heatmap of state vs network type ratings
  • Horizontal bar charts for call drop categories

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🛠️ Technologies Used

• R Language
• Libraries:
  • ggplot2 → Data visualization
  • dplyr → Data manipulation
  • lubridate → Date handling
  • reshape2 → Data reshaping

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🚀 How to Run

1. Clone the repository:

   git clone https://github.com/Mahak0747/Big-Data-Analysis.git
   cd Big-Data-Analysis

2. Open Big Data Analysis Using R.R in RStudio or run in R console.
3. Make sure the datasets (vgsales.csv, SeoulBikeData.csv, CallVoiceQualityExperience-2018-April.csv, CallVoiceQuality_Data_2018_May.csv) are available in your working directory. Update the file paths in the script if needed.
4. Install required libraries if not already installed:

   install.packages(c("ggplot2", "dplyr", "lubridate", "reshape2"))
   

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📈 Sample Visualizations

• Video game sales by genre

  

• Bike rentals vs temperature

  

• Call quality ratings across states

  

  

• Heatmap of call quality (State × Network Type)

  

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📌 Author

👩‍💻 Mahak Goswam

CSE (AI) Student

Interests: Data Analysis, Machine Learning, Game Development