Steam-Game-recommendation-Engine

This project implements a recommendation engine for Steam games using collaborative filtering (ALS), content-based filtering (TF-IDF), and graph-based analysis with NetworkX. Features:

1. Collaborative Filtering: Recommends games based on user gameplay history.
2. Content-Based Filtering: Suggests similar games using game title metadata.
3. Graph-Based Analysis: Uses NetworkX to visualize and analyze relationships between games and users. Setup:

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1. Prerequisites:

• Databricks environment with Spark enabled.
• Python libraries: PySpark, NumPy, Matplotlib, SciPy, NetworkX.

2. Datasets:

• Upload the following CSV files to /FileStore/tables/ in your Databricks workspace:
• games.csv
• users.csv
• recommendations_1.csv Steps to Run:

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1. Create a Databricks cluster and initialize a Spark session.
2. Load the datasets into Spark DataFrames from the provided paths.
3. Perform data preprocessing:

• Handle missing values.
• Tokenize game titles and compute TF-IDF.
• One-hot encode categorical features and scale numerical features.

4. Train a collaborative filtering model using ALS and generate recommendations.
5. Calculate cosine similarity for content-based filtering to find similar games.
6. Build and visualize graphs:

• Create a game similarity graph based on cosine similarity.
• Create a user-game interaction graph based on collaborative filtering results.

7. Display results:

• Top 5 game recommendations for each user.
• Most similar games based on title metadata.
• Visualizations and graph metrics (e.g., centrality, communities). Requirements:

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See requirements.txt for a list of required libraries.