Electricity Cost Prediction
A regression-based ML project that predicts electricity cost from structural, resource-usage, and environmental features. Includes EDA, statistical analysis, model comparison, evaluation, and Streamlit deployment.
AI Engineering student building a practical path from Python and data science to machine learning, AI agents, and Ai automation.
I’m an AI Engineer in progress, focused on turning what I learn into practical, intelligent systems. I enjoy building with Python, exploring machine learning and AI agents, and connecting AI with real-world applications through automation.
My goal is to keep learning, building, and growing into an AI Engineer capable of creating useful systems that solve real problems.
2025 -> 2029
A practical progression from programming foundations to intelligent systems and automation.
Programming fundamentals, OOP, algorithms, and structured problem solving.
Working with data through analysis, visualization, preprocessing, and practical exploration.
Building supervised learning models, evaluating performance, and applying ML to real datasets.
Building AI systems that use models, tools, memory, and workflows to complete tasks.
Connecting AI to real workflows and services using automation, APIs, and integrations.
Retrieval-Augmented Generation is the next step in the roadmap.
The tools and technologies I use while building my AI engineering foundation.
Tools shown reflect current learning and hands-on project experience.
Seven practical projects that turn what I learn into working systems.
A regression-based ML project that predicts electricity cost from structural, resource-usage, and environmental features. Includes EDA, statistical analysis, model comparison, evaluation, and Streamlit deployment.
An AI-powered Telegram assistant for dental clinics that checks real availability, handles booking and cancellation, and keeps appointment records synchronized.
An n8n workflow that manages restaurant orders, calculates totals, updates ingredient stock, sends Telegram notifications, and triggers Gmail low-stock alerts.
A responsive AI chat interface built with HTML, CSS, and JavaScript and connected to an AI API, with chat management, themes, settings, export, and Local Storage.
A polished energy-management landing experience focused on smart consumption monitoring, AI recommendations, forecasting, anomaly detection, and sustainable usage.
A bilingual MSME chat agent that supports product catalogs, FAQs, order capture, and confirmation across multiple business sectors.
A Java desktop Academic Management System using SQL Server and a local Llama 3.1 assistant for context-aware natural-language analysis of student data.
A simple timeline of the areas I have been building across my AI engineering path.
Started building the programming foundation with Python and core software development concepts (Harvard university.CS50).
Moved into data analysis, visualization, preprocessing, and practical work with data-focused Python libraries (iti).
Started applying supervised learning concepts through model building, evaluation, and practical projects (Stanford online.coursera)
Started exploring LLM-based agents, tool use, prompt engineering, and practical intelligent workflows (iti).
Building automation workflows with n8n and connecting AI systems to real services and business processes (DEPI).
I'm always open to conversations about AI, collaboration on projects, or opportunities to learn and contribute.