I'm a second-year IT student who loves turning raw data into meaningful stories. Currently diving deep into data analysis, visualisation & ML — making numbers make sense.

• 🎓 BE in Information Technology (Expected 2028)
• 📊 Focus: Data Analysis • Data Engineering • AI/ML
• 🌱 Learning: PySpark, NLP, Data Pipelines
• 🔍 Strong interest in real-world datasets & scalable systems

• 🚚 Built ETL pipelines processing 100K+ records using PySpark
• 📊 Analyzed 20K+ global data points to uncover environmental trends
• 🤖 Developed chatbot with 100% query accuracy (10/10 test cases)
• ⚙️ Strong foundation in data structures, OOP, and system design

• Data Cleaning & Preprocessing
• Feature Engineering & Transformation
• Exploratory Data Analysis (EDA)
• Scalable Data Pipelines
• Visualization & Insight Generation

Languages

Data & ML

Tools

End-to-end PySpark-based ETL pipeline built on the Olist dataset.

• Processed 100K+ orders across multiple tables
• Designed modular pipeline for scalability
• Implemented feature engineering for delivery performance
• Stored optimized output in Parquet format

Tech: PySpark • ETL • Parquet • Big Data

A retrieval-based chatbot using NLP techniques.

• Implemented TF-IDF + Cosine Similarity
• Built hybrid system for FAQs + small talk
• Applied confidence threshold filtering
• Visualized performance metrics

Tech: Python • NLP • scikit-learn • Matplotlib

EDA project analyzing pollution trends across global cities.

• Worked on 20K+ records across 50+ cities
• Identified seasonal PM2.5 trends
• Created insightful visualizations

Tech: Pandas • Matplotlib

Java-based system using OOP principles.

• Applied inheritance & polymorphism
• Implemented file-based persistence
• Automated stock tracking & reporting

Tech: Java • OOP • Serialization

• ⚙️ Advanced data pipelines using PySpark
• 🧠 NLP-based intelligent systems
• 🏗️ Data engineering concepts (warehousing, ETL optimization)
• 📈 Improving data visualization & storytelling