Video-Game-Sales-Analysis-Using-R

This project performs data analysis and machine learning on video game sales data using R, including visualization, KNN regression, linear regression, and decision tree models.

🎮 Video Game Sales Analysis & Prediction (R)

📌 Overview

This project analyzes video game sales data and applies machine learning techniques to predict global sales. It includes data preprocessing, visualization, and multiple predictive models.

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🚀 Features

• 📊 Data cleaning and preprocessing

• 📈 Data visualization using ggplot2

• 🤖 Machine Learning models:

  • K-Nearest Neighbors (KNN)
  • Linear Regression
  • Decision Tree

• 📉 Model evaluation using MSE and MAE

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🛠️ Technologies Used

• R Programming
• tidyverse
• ggplot2
• FNN (KNN)
• caret
• tree
• corrplot

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📂 Project Structure

├── script.R        # Main R script
├── Video_Games_Sales_as_at_22_Dec_2016.csv   # Dataset

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⚙️ How to Run

1️⃣ Open RStudio or R environment

2️⃣ Install required libraries:

install.packages(c("tidyverse","FNN","corrplot","tree","caret"))

3️⃣ Run the script:

source("script.R")

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📊 Dataset

The dataset contains video game sales information including platform, genre, publisher, critic scores, and global sales.

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📈 Models Used

🔹 K-Nearest Neighbors (KNN)

• Used for regression prediction of global sales
• Optimal K value selected using MSE

🔹 Linear Regression

• Predicts sales based on multiple features
• Evaluated using Mean Squared Error

🔹 Decision Tree

• Visualizes decision-making process
• Pruned for better accuracy

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📉 Evaluation Metrics

• Mean Squared Error (MSE)
• Mean Absolute Error (MAE)

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🧠 Future Improvements

• Add more advanced models (Random Forest, XGBoost)
• Improve feature selection
• Build interactive dashboard

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🤝 Contributing

Feel free to fork and contribute!

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📄 License

This project is open-source.

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👨‍💻 Author

Aditya Yadav