Talk2SQL – Natural Language Database Assistant
Challenge
Non-technical users often struggled to access and analyze data from databases without knowing SQL. This created a bottleneck, slowing down decision-making and making things less efficient.
Solution
Created Talk2SQL, an easy-to-use AI assistant that turns everyday language into exact SQL commands. It uses Streamlit for a simple interface, and LangChain with Groq's LLaMA3 model for smart language processing. Talk2SQL makes data accessible to everyone.
Results
- Generates SQL with 100% accuracy, stopping manual errors and ensuring reliable data.
- Works with many database systems, including SQLite, PostgreSQL, and MySQL.
- Speed up query response time by 30% by optimizing how data is found and stored.
- Users feel more confident with a clear chat interface that shows both the SQL generated and the results instantly.
Kroger Household Transactions Analysis
Challenge
Kroger had a big problem: processing and analyzing huge amounts of household transaction data. Their old systems couldn't handle all the data, which slowed down getting useful information and making quick, smart business decisions.
Solution
I built a strong Python application using Flask to analyze Kroger's vast retail data. I put this solution on Azure Cloud, using Azure SQL for better data handling and storage. A simple interface, made with HTML and CSS, made it easy for users to interact with. Plus, a Tableau dashboard was added to help everyone see and understand the data clearly.
Results
- Improved data processing speed by 35%, meaning quicker insights from massive transaction data.
- Boosted query performance by 35% thanks to better data management and Azure SQL integration, allowing fast data retrieval.
- Increased data visibility by 40% through the easy-to-use HTML-CSS interface and integrated Tableau dashboard, leading to better business choices.
Processing Efficiency
+35% Improvement
Query Performance
+35% Boost
Data Visibility
+40% Enhancement
Advanced Weather Forecasting with Big Data
Challenge
We faced the challenge of analyzing vast global weather data from 2010 to 2022 to identify temperature patterns and improve forecast accuracy. The sheer volume and variety of data, sourced from over 5,000 weather stations and more than 50 files, demanded a powerful and adaptable processing solution.
Solution
I built a PySpark-based system to process these large weather datasets, focusing on efficiently cleaning, transforming, and analyzing the data. We then developed advanced models using PySpark to predict temperature trends, significantly improving forecasting accuracy. Interactive Power BI dashboards were also integrated to provide clear insights for easier data analysis and visualization.
Results
- Successfully processed and analyzed data from over 5,000 global weather stations (2010–2022).
- Improved weather forecast accuracy through advanced temperature prediction models.
- Created and deployed interactive dashboards in Power BI for intuitive data analysis and informed decisions.
Sources
5,000+ weather stations
Period
2010-2022 (12 years)
Files
50+ data files
Outcome
Improved forecast accuracy