https://github.com/ankitrai259/movielens-dataset-analysis-sql
Analysis of MovieLens Dataset Using SQL
https://github.com/ankitrai259/movielens-dataset-analysis-sql
postgresql sql
Last synced: 4 days ago
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Analysis of MovieLens Dataset Using SQL
- Host: GitHub
- URL: https://github.com/ankitrai259/movielens-dataset-analysis-sql
- Owner: AnkitRai259
- Created: 2024-10-18T14:14:06.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-10-18T19:37:14.000Z (over 1 year ago)
- Last Synced: 2025-02-23T13:03:32.457Z (over 1 year ago)
- Topics: postgresql, sql
- Homepage:
- Size: 1.15 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# MovieLens Dataset for SQL Project
# Summary
This project utilizes the MovieLens (ml-32m) dataset, which comprises over 32 million ratings and 2 million tag applications across 87,585 movies. The data was generated by 200,948 users between January 9, 1995, and October 12, 2023, and made publicly available on October 13, 2023. The dataset includes the following files:
* links.csv
* movies.csv
* ratings.csv
* tags.csv
# Dataset Overview
* Ratings: 32,000,204 ratings on a 5-star scale (with half-star increments).
* Tags: 2,000,072 user-generated tags.
* Users: All included users have rated at least 20 movies; no demographic data is provided.
For further details and access to the dataset, visit https://grouplens.org/.
# Project Structure
This project consists of SQL scripts that facilitate data analysis on the MovieLens dataset. The primary components include:
* SQL Queries: A collection of SQL scripts to extract insights from the dataset, such as average ratings, movie popularity, and tag usage.
# Getting Started
1. A SQL database (e.g., PostgreSQL, MySQL) set up on your machine.
2. Create a new database and tables in your machine.
3. Import Dataset: Use your SQL database tools to import the CSV files into corresponding tables. Ensure the following table structure:
* links: Contains columns movieId, imdbId, tmdbId
* movies: Contains columns movieId, title, genres
* ratings: Contains columns userId, movieId, rating, timestamp
* tags: Contains columns userId, movieId, tag, timestamp
4. Run SQL Queries: Execute the SQL scripts in the queries/ directory using your SQL client to analyze the dataset.
Queries are uploaded in pdf format : "[Analysis of Movie Lens Dataset.pdf](https://github.com/user-attachments/files/17439980/Analysis.of.Movie.Lens.Dataset.pdf)"