Hi there! I'm Dewdu Sendanayake, a dedicated Data Science undergraduate at SLIIT with a CGPA of 3.76. I have a strong passion for AI/ML, big data, and transforming complex datasets into meaningful insights. Welcome to my GitHub profile!

• Current Focus 🔭: Advancing my expertise in machine learning, big data systems, and AI-powered applications. Passionate about using data to drive innovation and create meaningful impact in real-world settings.

• Education 🎓: 3rd year undergraduate consistently on the Dean's List, committed to academic excellence and lifelong learning.

• Learning 🌱: Continuously upskilling in areas like cloud computing, data engineering, optimization methods, and ethical AI to stay ahead in a rapidly evolving tech landscape.

• Ask me about 💬: Python, R, SQL, Power BI, Tableau, Cloud (AWS/Azure), Jupyter Notebook, OpenCV, TensorFlow, Pandas, Matplotlib, NumPy, Keras, Scikit-Learn, Seaborn, Hive, Kafka, Flask, SSAS/ SSMS/ SSIS, Git/ GitHub, Excel, Java, JavaScript, C, C++, MERN Stack, Natural Language Processing, Computer Vision and Image Processing, Hadoop, Spark, MySQL, PL/SQL, Data cleaning/preprocessing, Predictive modeling, ETL, Big Data Analysis, Oracle DB, SQL*Plus, SQLite, Data Structures & Algorithms, Data Visualization and Query Preprocessing

• Beyond Tech 🧙🏻‍♀️: Psychology diploma holder and mental health advocate, space nerd with a soft spot for NASA, aesthetic content creator and proud advocate for women in STEM.

• Web Platform for Understanding Earth's Systems: Built a React.js/ Flask platform with TensorFlow LSTM models on multisource NASA data, achieving 94% accuracy and a 91% scenario success with <250 ms real-time simulations.

• Yummy-Crew: A Flask-based multi-agent system powered by CrewAI, combining food recommendations, vector search from S3, and conversational intelligence.

• Semantic-Pet-Vision: A lightweight semantic image search engine for cats and dogs, built using Hugging Face image encoders and DocArray.

• ChatMyPDF: Local PDF Chatbot which uses Retrieval-Augmented Generation (RAG), FAISS vector indexing, and a locally running Large Language Model (Mistral 7B via llama.cpp) and built with LangChain and Streamlit.

• Handwritten Digit Recognition: Developed a MNIST pipeline (60 k train/10 k test), boosting accuracy from ~92% (logistic regression) to 99.2% (Convolutional Neural Network- CNN with augmentation & dropout), deployed via Flask.

• Retail Insight 360: ETL to Analytics Pipeline and Power BI Reports: Engineered an SSIS-driven ETL data pipelines for 96K+ monthly records, ensured 99.9% accuracy with SCD 2, and delivered SSAS/Power BI solutions that sped query performance by 40%.

• Spotify Power BI Dashboard: Built an interactive Power BI dashboard using Python and HTML to analyze and visualize 1,000+ top-streamed Spotify songs (up to 2023), reducing manual analysis time by 60% and boosting engagement by 75% through visuals and custom-designed album artwork embeds.

• Privacy Censor Bot: Built a desktop app using OpenCV and Tkinter that ingests live webcam video (30 FPS, 640×480), detects faces with 96% accuracy, applies adjustable Gaussian blur (kernel sizes up to 201×201) in <0.05 s per frame, and outputs a real-time censored feed.

• Video Games Sales Tableau Dashboard: Developed an interactive Tableau dashboard for video game sales, implementing dynamic regional and temporal parameters, layered line and area charts, top 10 visualizations, KPI summary tiles and published to Tableau Public, reducing analysis time by roughly 25%

• Customer Churn Prediction: Built Streamlit web app using XGBoost (81.8% AUC) and Logistic Regression (84.0% AUC), with live inference, scaled inputs over a 7,000+ telecom customer churn dataset.

• Netflix Insights Dashboard: Engineered a Power Query ETL pipeline to clean and enrich 8000+ Netflix records and delivered a Power BI dashboard, cutting report prep time by 40% and boosting insight speed by 25%.

• ODI Insights: Snowflake ELT & Analytics Pipeline: Designed a Snowflake ELT pipeline that ingested 2,460 ODI match JSON files, transformed them into a star schema with 1.8 million fact rows and 15 000 dimension records, and analytical query performance (average 0.4 s) for cricket match and ball by ball reporting.

• Titanic Exploratory Data Analysis: EDA using Python (pandas, seaborn, matplotlib) in Jupyter Notebook with 100% coverage.

• Empirical Analysis of Income and Social Media Engagement: Analyzed a 5K-user dataset with correlation and regression.

• Coconut Cultivation and Operations System: MERN-based platform for real-time ops, pest detection and analytics.

• Sleep and Dream Analysis App: Kotlin app using psychology and analytics to enhance sleep and lucid dreaming.

• Culinary Skill Sharing & Learning Platform: Spring Boot/React.js web app for learning, progress tracking and recipe sharing.

• Aurora Mobile App: A Kotlin-based mobile app that boosts productivity by managing tasks, utilizing a timer, and featuring a convenient home widget.

• Event Management System: A comprehensive solution for managing online events using Java, JSP, Servlets, and MySQL.

• Tourism and Travel Management System: A web-based application utilizing HTML, CSS, JavaScript, and SQL for seamless travel planning.

Thanks for stopping by! Feel free to explore my repositories, check out my projects, or connect with me on LinkedIn to collaborate on innovative data science solutions.

• LinkedIn: www.linkedin.com/in/dewdusendanayake
• Email: [email protected]

Let's build the future with data! ✨