In the rapidly growing video game industry, players often face the challenge of discovering new games that match their interests and preferences. With thousands of games released across various platforms and genres, finding the next game to play can be overwhelming. This project aims to address this challenge by developing a comprehensive video game recommendation system.
Leveraging the Video Game Sales with Ratings dataset from
Kaggle (https://www.kaggle.com/datasets/rush4ratio/video-game-sales-with-ratings), our objective is to create a
recommender system that suggests video games based on game similarities. The dataset includes key information about
video games such as titles, platforms, genres, user scores, and critic scores, providing a rich source of data for
building our models.
We will explore multiple recommendation approaches to ensure a robust and versatile system:
- Nearest Neighbors Model with Cosine Similarity: A content-based approach that measures the similarity between games based on their attributes.
- Content-Based Recommender: Utilizes text-based features like game titles to find similar games.
The project will be structured as follows:
- Dataset Overview: Introducing the dataset and its features.
- Exploratory Data Analysis (EDA): Performing a detailed analysis to understand data distributions, trends, and patterns.
- Data Cleaning and Preprocessing: Preparing the data by handling missing values, imputing scores, and encoding categorical variables.
- Feature Selection: Identifying the most relevant features for building effective recommendation models.
- Model Training: Implementing and training the Nearest Neighbors and Content-Based recommendation models.
- Model Evaluation: Evaluating the performance of each model using similarity scores and selecting the best performing one.
Through this comprehensive approach, we aim to deliver a recommendation system that enhances the gaming experience by providing personalized and relevant game suggestions.