Video Game Sales Prediction

This project is a machine learning application designed to predict video game sales using historical data. The dataset contains information about various video games, including genre, publisher, release year, platform, and sales in different regions. By leveraging this data, we build models to forecast sales and gain insights into key factors that influence game performance.

Features

Exploratory Data Analysis (EDA): Gain insights into video game sales trends and identify patterns in the data. Data Preprocessing: Handle missing values, encode categorical data, and normalize numeric features. Model Building and Evaluation: Train and evaluate multiple machine learning models to predict sales with high accuracy. Feature Importance Analysis: Determine the key factors that influence video game sales. Technologies Used

Programming Language: Python 3 Libraries and Frameworks: Pandas, NumPy: Data manipulation and analysis Matplotlib, Seaborn: Data visualization Scikit-learn: Machine learning algorithms and evaluation metrics Dataset

The dataset used in this project contains information on video game sales across various platforms, including details like:

Game name Platform Genre Year of release Publisher Sales in North America, Europe, Japan, and other regions Ensure you have the dataset ready in the appropriate format (e.g., CSV) before running the project.

How to Use

Clone the Repository git clone https://github.com/pranavprasad7/videogamesales.git cd videogamesales Install Dependencies Ensure you have Python 3 installed, then install the required libraries: pip install -r requirements.txt Run the Project Execute the main script to perform EDA, preprocess data, train models, and view predictions. python main.py Visualize Results View graphical outputs to analyze trends, model performance, and feature importance. Project Structure

data/ - Contains the dataset(s) used for the project notebooks/ - Jupyter notebooks for EDA and model development src/ - Core Python scripts for preprocessing, model training, and evaluation requirements.txt - List of dependencies required to run the project README.md - Project overview and instructions