avikumart / datahour_netflix_sql

Natural language to SQL tutorial to query Netflix Movies dataset

Analytics Vidhya Data Hour - Webinar Edition

Unlocking Insights with Language: Converting Natural Language to SQL for Netflix Movie Analysis using LLMs

Introduction

Join us for an enlightening webinar that will demonstrate the integration of Large Language Models (LLMs) with SQL for in-depth Netflix movie data analysis. Our approach simplifies data querying, making it accessible to a wider audience, including non-technical professionals.

Prerequisites

Before we get started, ensure you're prepared with the following:

1. Code Editor: Visual Studio Code is recommended to ease the coding process. Feel free to use an editor you're comfortable with.
2. GitHub Repository Access: Obtain the code and resources from our designated GitHub repository. Set up your account and clone the repository beforehand.
3. Dependencies: Install all necessary dependencies prior to the webinar. Instructions and the list of dependencies are located on the repository's README.
4. Dataset: Acquire the Netflix Movie Data or applicable dataset for use in the webinar. Dataset download instructions are listed in the repository README.

Installation Guide

Set up your environment for the webinar with the following steps:

1. OpenAI Key Configuration: You'll need an OpenAI key with GPT-4 access. Rename the .env.template file to .env and input your key after OPENAI_API_KEY= (e.g., OPENAI_API_KEY=sk-123...).
2. Make Command Issues: If you encounter issues with make <commands>, consult the troubleshooting guides for Mac and Windows.

• Clone: Execute git clone https://github.com/seduerr91/datahour_netflix_sql.
• Dependencies: Run make install to install necessary dependencies.
• Database Ingestion: Prepare the database with make ingest.
• Service Launch: Start the service using make run.
• Testing: In a separate terminal, perform a quick test with make query.
• Validation: Ensure everything is in order using make validate.

A comprehensive guide for installing brew, Python, VSC, and other prerequisites is available at the conclusion of this document.

Webinar Details

• Presenter: Seb Duerr from Cordial.com
• Date: Wednesday, 7th February 2024
• Time: 8:00 PM - 9:00 PM IST | 6:30 AM - 7:30 AM Pacific Time (PT)

Session Outline

Within the scope of today's information-rich environment, the potential of data is often constrained by technical barriers in its analysis. This webinar targets these barriers, showcasing how LLMs deftly translate conversational language into SQL commands for actionable insights into Netflix's movie database.

We'll explore groundbreaking progress in the field, including the Yale Spider challenge, and conduct a practical coding session using readily available GitHub resources.

The webinar caters to business professionals seeking to leverage data without advanced SQL proficiency, as well as AI enthusiasts interested in natural language processing applications. Join us to bridge the gap between natural language and data querying, unlocking the full potential of LLMs in your professional and personal projects.

Presenter Bio

Seb Duerr, a Generative AI Engineer at Cordial.com, is recognized for his expertise in AI and data processing technologies that enhance enterprise-level marketing platforms. With an MSc in Information Systems from the University of Bamberg, Seb has a track record of academic excellence, authoring numerous peer-reviewed articles and conducting significant research in NLP and data analytics at MIT's Center for Collective Intelligence.

Beyond tech innovation, Seb is an avid runner, pickleball player, yoga practitioner, culinary experimenter, and board game aficionado.

Connect with Seb

Engage with Seb on LinkedIn: Seb Duerr

We look forward to welcoming you to the webinar!

Quick Start Guide for Mac Users

If you're starting from scratch on a Mac, these commands will get you going:

• Install Homebrew: Run /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
• Initialize Homebrew: Add eval "$(/opt/homebrew/bin/brew shellenv)" to your ~/.zprofile and then run the command to set it up.
• Install Python: Use brew update && brew upgrade; brew install python3
• Install Xcode Command Line Tools: Apply xcode-select --install
• Install Visual Studio Code: Execute brew install --cask visual-studio-code