Video Games Sales Prediction 🎮

This repository contains a project focused on predicting global video game sales using various machine learning models. By analyzing a comprehensive dataset, we explore the factors that influence video game sales and develop predictive models to estimate sales figures with high accuracy.

📂 Repository Link

Video Games Sales Prediction

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📜 Project Overview

Video game sales data from Kaggle was analyzed using machine learning techniques to understand the factors influencing sales. The project aims to assist stakeholders in the gaming industry by providing actionable insights and accurate predictions.

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🎯 Goals

1. Develop powerful machine learning models to predict global sales of video games.
2. Explore and compare the performance of various regression models.
3. Gain insights into key factors influencing video game sales for better decision-making.

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⚙️ Methodology

Data Preprocessing

• Handled missing values and ensured consistent data types.
• Transformed categorical features like Platform and Genre into numerical values using Label Encoding.

Model Development

The following machine learning models were employed:

• K-Nearest Neighbors (KNN)
• Multiple Linear Regression (MLR)
• Decision Tree Regression (DTR)
• Random Forest Regression (RFR)
• Gradient Boosting Regression (GBR)

Evaluation Metrics

• Mean Squared Error (MSE)
• R-squared Score

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🏆 Results

The performance of all models was evaluated, and the Gradient Boosting Regression model was identified as the most effective for sales prediction.

Key Findings

• Gradient Boosting Regression achieved the highest R-squared score, demonstrating superior predictive ability.
• Insights from feature analysis revealed the significant impact of platform and genre on sales performance.

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📊 Visualizations

• Scatter plots comparing actual vs. predicted values for all models.
• Bar chart showcasing R-squared scores for all models.

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🚀 Technologies Used

• Programming Language: Python
• Libraries:
  • Pandas
  • Scikit-learn
  • Matplotlib
  • Seaborn

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📂 Dataset Overview

The dataset used in this project was sourced from Kaggle. It provides detailed information about video games, including their platform, genre, release year, and sales data across various regions.

📊 Dataset Features

The dataset contains the following columns:

Column Name	Description
Name	Name of the video game.
Platform	Platform on which the game was released (e.g., PS4, Xbox, PC).
Year_of_Release	Year the game was released.
Genre	Genre of the video game (e.g., Action, Sports, Strategy).
Publisher	Publisher of the game.
NA_Sales	Sales in North America (in millions of units).
EU_Sales	Sales in Europe (in millions of units).
JP_Sales	Sales in Japan (in millions of units).
Other_Sales	Sales in other regions (in millions of units).
Global_Sales	Total global sales (in millions of units).

🔍 Dataset Insights

1. Sales Data:
   The Global_Sales column serves as the target variable for this project. Regional sales (NA, EU, JP, Other) are analyzed to identify their contributions to global sales.

2. Categorical Features:
   Features like Platform and Genre were encoded to make them usable for machine learning models.

3. Missing Data:
   Missing values in Year_of_Release and Publisher were carefully handled during preprocessing to maintain data integrity.

4. Temporal Trends:
   The dataset includes data spanning several years, allowing for the analysis of trends over time.

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📥 Dataset Source

The dataset can be accessed on Kaggle: Video Game Sales Dataset.

📧 Contact

For questions or suggestions, feel free to reach out:
GitHub Profile

🌟 If you found this project helpful, please give it a ⭐ on GitHub!

📖 How to Run

1. Clone the repository:

   git clone https://github.com/usk2003/VideoGamesSalesPrediction.git
   cd VideoGamesSalesPrediction
   

2. Run the script in Jupyter Notebook or in Google Colab.