Text to SQL Dataset Analysis

[abhisheks008]

ML-Crate Repository (Proposing new issue)

🔴 Project Title : Text to SQL Dataset Analysis
🔴 Aim : The aim is to analyze the dataset using machine learning methods.
🔴 Dataset : https://www.kaggle.com/datasets/meryentr/text-to-sql
🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

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📍 Follow the Guidelines to Contribute in the Project :

• You need to create a separate folder named as the Project Title.
• Inside that folder, there will be four main components.
  • Images - To store the required images.
  • Dataset - To store the dataset or, information/source about the dataset.
  • Model - To store the machine learning model you've created using the dataset.
  • requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
• Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.

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🔴🟡 Points to Note :

• The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
• "Issue Title" and "PR Title should be the same. Include issue number along with it.
• Follow Contributing Guidelines & Code of Conduct before start Contributing.

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✅ To be Mentioned while taking the issue :

• Full name :
• GitHub Profile Link :
• Participant ID (If not, then put NA) :
• Approach for this Project :
• What is your participant role? (Mention the Open Source Program name. Eg. HRSoC, GSSoC, GSOC etc.)

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Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

[github-actions]

Thank you for creating this issue! We'll look into it as soon as possible. Your contributions are highly appreciated! 😊

[VanshGupta-2404]

Full name : VANSH GUPTA
GitHub Profile Link : https://github.com/VanshGupta-2404
Participant ID (If not, then put NA) : NA
Approach for this Project :To achieve the aim, the project will begin with an extensive exploratory data analysis (EDA) to understand the dataset's structure, distribution, and key features. This step will involve data cleaning, normalization, and visualization to extract meaningful patterns and insights. Following the EDA, multiple machine learning algorithms will be implemented to build the models. The proposed algorithms include Support Vector Machines (SVM), Random Forests, Long Short-Term Memory (LSTM) networks, and Transformer-based models such as BERT. Each model's performance will be evaluated using accuracy scores and other relevant metrics to determine the best-fitted algorithm for translating text to SQL queries. The results will be compared, and the most effective model will be selected based on its accuracy and overall performance.
What is your participant role? (Mention the Open Source Program name. Eg. HRSoC, GSSoC, GSOC etc.) : VSOC

Interested in the project.

[abhisheks008]

Implement 6-7 models for this problem statement.
@VanshGupta-2404 assigned to you.