I am a Computer Application undergraduate specializing in Data Analysis, focused on building data-driven applications, business intelligence solutions, and AI-powered tools.
I enjoy working with data from end to end — from SQL querying and data cleaning to Python analysis, Power BI dashboards, and machine learning applications.
I'm currently strengthening my skills in Machine Learning, and Python Libraries while building practical projects.
| Category | Technologies |
|---|---|
| Programming | |
| SQL & Databases | MSSQL · T-SQL |
| Data Analytics | Excel · Power BI · Pandas · NumPy · Data Visualization · Statistics |
| Tools | Git-Github |
| AI Tools | Claude · ChatGPT · Gemini . AI Automation |
| Category | Technologies |
|---|---|
| AI / ML | Machine Learning · NLP · Semantic Similarity · Prompt Engineering · RAG · Vector Databases |
| AI Development | LangChain · Agentic AI · AI Agents · LLM Applications |
| Development | Streamlit . TensorFlow . PyTorch |
| Cloud & Deployment | Cloud Deployment · Model Deployment |
🤖 AI Resume Ranker — Python • Streamlit • NLP
- Overview: Evaluates and ranks resumes against job descriptions using NLP, skill extraction, and semantic similarity.
- Key Highlights: Built modular resume-processing and scoring pipelines, automated PDF text extraction, and developed an interactive Streamlit interface.
- Repository: View on GitHub
📊 Customer Shopping Behavior Analysis — SQL • Python • Power BI
- Overview: Analyzed customer shopping behavior to identify purchasing patterns, customer segments, revenue trends, and business insights.
- Key Highlights: Combined data cleaning, SQL analysis, Python/Pandas processing, and interactive Power BI visualization.
- Repository: View on GitHub
🧹 Dirty Cafe Dataset — Python • Pandas • Data Cleaning
- Overview: Cleaned and transformed a messy cafe dataset into a structured and analysis-ready dataset.
- Key Highlights: Handled missing values, inconsistent records, data formatting issues, and exploratory analysis.
- Repository: View on GitHub
🗄️ MSSQL Employee Database Questions & Answers — MSSQL • T-SQL
- Overview: A structured collection of practical SQL problems and solutions based on an Employee database.
- Key Highlights: Covers joins, aggregations, subqueries, CTEs, window functions, stored procedures, functions, ranking, salary analysis, and business-oriented SQL problems.
- Repository: View on GitHub
- Data Analysis: Analyzed structured and unstructured datasets using SQL, Python, Pandas, and Excel.
- BI Development: Designed Power BI dashboards with KPIs, trends, business metrics, and interactive reporting.
- SQL Development: Practiced MSSQL and T-SQL through real-world business problems involving joins, CTEs, window functions, procedures, and analytical queries.
- Data Cleaning: Built Python and Pandas workflows for cleaning, transforming, and preparing messy datasets.
- AI Development: Built document-analysis applications using NLP and semantic similarity techniques.
- AI & Data Science: Ongoing
- Bachelor of Computer Applications: Ongoing
- ISRO Hack2Skills: Completed
- Microsoft SQL: Completed
- Claude101: Completed
