My Linkedin Profile

I document my beginner-friendly Flask to-do list project here as a practical exercise in Python web development. The repository is intended for learning and educational purposes; the captured README does not specify its routes, database schema, or task data model.

My TO-DO-LIST-HW-FOR-AMITAVA-SIR repository is on @Mousam098's GitHub.

My TO-DO-LIST-HW-FOR-AMITAVA-SIR repository metadata says: 1 star; started 2026-05; last updated 2026-05.

I describe the application as “a simple and user-friendly TO-DO List web application built using Python and Flask.”

The features I list are: Add new tasks; Update/Edit tasks; Delete tasks; Display all tasks.

My stated learning goals are: Flask framework basics; Routing and templates; Handling user input; Web application structure; CRUD operations for tasks.

My stated learning outcomes are: Flask basics; Python web development; CRUD operations; Virtual environments; Git & GitHub; Using UV package manager.

The README lists the project structure as: static/; templates/; app.py; requirements.txt; README.md.

The README lists Python, Flask, HTML, CSS, Bootstrap, and SQLite as technologies. It describes the interface as simple, and the application as lightweight and easy to run.

My documented setup commands are: git clone https://github.com/Mousam098/TO-DO-LIST-HW-FOR-AMITAVA-SIR.git; cd TO-DO-LIST-HW-FOR-AMITAVA-SIR; pip install uv; uv --version; uv venv; .venv\Scripts\activate; uv pip install flask; uv pip install -r requirements.txt; python app.py; uv run app.py.

For dependency installation, I document uv pip install flask or uv pip install -r requirements.txt; for starting the app, I document python app.py or uv run app.py.

The README says uv venv creates a .venv folder and shows .venv\Scripts\activate for Windows CMD; after activation, the prompt may show (.venv).

The browser URL shown in my captured README is http://<redacted>:5000; the host is redacted, while the displayed port is 5000.

I list the author as Mousam Koley and the GitHub profile as https://github.com/Mousam098.

The README includes a Screenshots section, but the captured content provides no screenshots or screenshot details. It also gives no task counts, test results, runtime measurements, or other application results. The repository README presents UV as the package manager for setup and gives alternative commands for installing dependencies and running the application.

mysql -u root -p < sql/01_schema.sql

mysql -u root -p carbon_credit_platform < sql/02_triggers.sql

mysql -u root -p carbon_credit_platform < sql/03_sample_data.sql

mysql -u root -p carbon_credit_platform < sql/04_views_and_queries.sql

I document CarbonX here as a database-focused final-year and portfolio project. It extends the brief project description already on this page with the repository’s data model, database rules, setup, demonstration flow, and deployment guidance.

My CarbonX project is a MySQL and Streamlit-based carbon credit trading platform with automated SQL triggers, project verification, credit issuance, trading, payments, audits, certificates and analytics.

The CarbonX repository is on @Mousam098's GitHub.

The CarbonX repository is written in Python and has 1 star.

The CarbonX repository was started 2026-07, last updated 2026-07.

The README describes CarbonX as a database-focused final-year/portfolio project built with MySQL, SQL triggers, Python and Streamlit.

CarbonX models verified sustainability projects, issued carbon credits, company holdings, trades, payments, government audits, certificates and downloadable management reports.

CarbonX has 10 main database tables.

• I list the CarbonX tables in this order: companies, carbon_credits, transactions, projects, government_agencies, government_audits, certificates, payments, reports, and system_audit_log.

The CarbonX project structure includes app.py, db.py, requirements.txt, .env.example, .gitignore, and four SQL scripts: 01_schema.sql, 02_triggers.sql, 03_sample_data.sql, 04_views_and_queries.sql.

I use automated database logic to enforce the platform’s project, credit, company, trade, and payment rules.

In CarbonX, credits can be issued only for a verified project.

In CarbonX, Initial credits must belong to the project developer.

In CarbonX, a seller cannot sell more credits than it owns.

In CarbonX, Buyer and seller must be different active companies.

In CarbonX, a transaction total must equal quantity × unit price.

When a CarbonX trade is completed, seller source quantity reduced; buyer credit lot created; seller balance reduced; buyer balance increased by credit-insert trigger; system audit record created.

In CarbonX, Payment must exactly match the transaction value.

For local setup, I install MySQL Server and MySQL Workbench, keep the root password available, and run the schema, triggers, sample data, and views/query scripts in sequence. The README also provides Command Prompt commands for running those SQL scripts.

The CarbonX SQL setup order is sql/01_schema.sql, sql/02_triggers.sql, sql/03_sample_data.sql, sql/04_views_and_queries.sql.

Windows Command Prompt setup:
cd carbon_credit_trading_platform
py -m venv .venv
.venv\Scripts\activate
python -m pip install --upgrade pip
pip install -r requirements.txt
copy .env.example .env

The README’s example database connection uses port 3306, database carbon_credit_platform, host localhost, user root.

The example `.env` settings are:
DB_HOST=localhost
DB_PORT=3306
DB_NAME=carbon_credit_platform
DB_USER=root
DB_PASSWORD=YOUR_REAL_MYSQL_PASSWORD

I run the app with streamlit run app.py; if the command is not recognized, the README recommends python -m streamlit run app.py. The browser normally opens at http://localhost:8501.

• My recommended CarbonX demonstration flow is to open Dashboard and show KPIs; register a buyer or developer company; create a sustainability project; verify it under Audits & Certificates; issue credits for the verified project; complete a trade; show changed company balances; open Reports → Trigger-generated audit log; and run SHOW TRIGGERS FROM carbon_credit_platform; in Workbench.

The CarbonX README recommends deploying the Streamlit app to Streamlit Community Cloud rather than Vercel.

For public deployment, The database cannot remain on your local computer when the app is public.

For a public app, I need a cloud MySQL database and must configure its credentials through Streamlit Community Cloud Secrets. The local example uses .streamlit/secrets.toml with a [mysql] section containing host, port, database, user, and password.

For deployment, Choose a hosted MySQL service that permits CREATE TRIGGER.

For CarbonX deployment, Do not upload .env or commit .streamlit/secrets.toml; add the same values through the app's Secrets settings.

I still need to choose a hosted MySQL provider and verify its current plan supports triggers, connections, and database availability; the README does not name a cloud provider or give a deployment URL. The chosen cloud database host, user, and password also remain to be determined.

I identify Python as my primary language for AI, machine learning, analytics, automation and development.

I list basic Java and object-oriented programming concepts among my skills.

I list basic frontend logic and web application functionality among my skills.

I list webpage structure, styling and responsive interface fundamentals among my skills.

My learning roadmap moves from Machine Learning to Deep Learning.

My learning roadmap connects Generative AI to LLM Applications.

My learning roadmap connects LLM Applications to AI and ML Engineering.

My learning roadmap connects Machine Learning to Data Analytics.

My learning roadmap connects Data Analytics to Power BI and SQL.

I list Excel formulas and functions among my analytics skills.

I list Power BI visuals and filters among my analytics skills.

My machine-learning topics are Machine-learning fundamentals; Supervised learning; Regression and classification; Data preprocessing; Feature preparation; Train-test splitting; Model training and evaluation; Exploratory Data Analysis.

My AI and computer-vision topics are Image classification; Object detection; YOLO fundamentals; Generative AI application development; LLM API integration; Gemini API integration; Prompt engineering; Output validation; Human-in-the-Loop workflows.

My annotation and data-quality topics are Image annotation; Bounding-box annotation; Semantic-segmentation fundamentals; Data validation and quality checking; LiDAR technology and annotation fundamentals.

My Python libraries are Pandas; NumPy; Matplotlib; Seaborn; Scikit-learn; Keras.

My analytics and business-intelligence profile includes Power BI; Microsoft Excel; Power Query; Data Visualization; Exploratory Data Analysis; Data cleaning; KPI creation; Dashboard development; Business reporting; Trend analysis; DAX fundamentals; Business insight generation.

My database topics are MySQL; Oracle SQL; DBMS fundamentals; Relational databases; Database tables; Primary and foreign keys; Database normalization; SQL joins; Subqueries; Aggregate functions; Grouping and filtering; CRUD operations.

My web-application development topics are Flask application development; Streamlit application development; REST API fundamentals; Third-party API integration; API key management; JSON data handling; Form handling; Routing; Template rendering; Frontend and backend integration; Session and application-state fundamentals; Error handling; Modular Python development; Basic software testing; Debugging.

My cloud and deployment profile includes AWS fundamentals; Amazon EC2; Amazon S3; Virtual machine fundamentals; Cloud storage fundamentals; Microsoft Azure fundamentals; Cloud-computing concepts; Basic web application deployment; Environment-variable fundamentals; Render deployment; Vercel deployment fundamentals.

My development tools and practices include Git; GitHub; Visual Studio Code; Anaconda; Jupyter Notebook; Google Colab; Zed Editor; Windows Terminal; Command Prompt; Python virtual environments; Pip; Git branching and commits; Debugging tools; Version control.

My software-engineering fundamentals include Data Structures and Algorithms; Object-Oriented Programming; DBMS; Operating-system fundamentals; Computer-network fundamentals; Software Development Life Cycle; Requirement understanding; Problem-solving; Debugging; Software testing fundamentals; Git-based version control; Technical documentation; System-design fundamentals.

My listed leadership roles and activities are Team Lead — Smart India Hackathon; Team Lead — ISRO Hackathon; Data Annotation and Machine Learning Trainee — Anudip Foundation; Core Member — DevNexus Technical Community; Campus Ambassador — Kshitij, IIT Kharagpur.

The topics I list as currently learning are Advanced Python; Data Structures and Algorithms; Machine Learning; Deep Learning Fundamentals; Generative AI; Large Language Model Applications; Computer Vision; System Design; REST APIs; Cloud Computing; Power BI and Advanced SQL.

My GitHub profile presents my interests in AI/ML, Python, data analytics, GenAI, computer vision, and cloud, alongside projects spanning analytics, prediction, dashboards, AI applications, databases, and management systems. I use GitHub to maintain source code and demonstrate my technical projects.

I am open to open-source contributions, technical collaborations, and internship opportunities.

I am interested in Computer Vision, LiDAR, object detection, and data annotation.

My GitHub README also presents my current focus, learning goals, and the kinds of roles and collaborations I am seeking; these are profile statements, not a detailed account of project methods or results.

I describe myself on the profile as an aspiring AI/ML engineer, Python developer, and data analytics enthusiast.

I use Python for data analysis, exploratory data analysis, machine-learning workflows, API integration, automation, and application development.

I am learning Machine Learning, DSA, DBMS, System Design, APIs, and Cloud Computing.

I am seeking opportunities in AI/ML, Python development, data analytics, and software engineering.

The captured GitHub repository reports 1 star.

The captured repository metadata says it started 2025-09 and was last updated 2026-07.

My GitHub profile identifies me as MOUSOM KOLEY (@Mousam098), Kolkata, West Bengal, India.

I have been on GitHub since 2023.

My profile lists 20 repositories.

Nine of my named repositories have 1 star: Insurance-EDA-and-Cost-Prediction, HR-Attrition-Analytics-Dashboard, mousom-os-portfolio, Mousam098, CarbonX-Carbon-Credit-Trading-Platform, Employee-Performance-And-Payroll-Management-System, Waste-Classification-AI--SIH, ISRO-Craters-and-Boulders-Detection-Project, and TO-DO-LIST-HW-FOR-AMITAVA-SIR.

The languages listed across my repositories are Python (5), Jupyter Notebook (3), HTML (2), TypeScript (1).

My Insurance-EDA-and-Cost-Prediction repository is an end-to-end exploratory data analysis and insurance cost prediction using Python, Pandas, Matplotlib, Seaborn and Linear Regression.

My CarbonX-Carbon-Credit-Trading-Platform repository is a MySQL and Streamlit-based carbon credit trading platform with automated SQL triggers, project verification, credit issuance, trading, payments, audits, certificates and analytics.

My Waste-Classification-AI--SIH repository is a professional and lightweight Waste Classification Web App that classifies waste images into two categories: Biodegradable and Non-Biodegradable.

My TO-DO-LIST-HW-FOR-AMITAVA-SIR repository is for understanding the concept of Flask.

My Product_Manegment_System- repository is described as a scalable project management system that uses api.

The listed repository update dates are Most listed repositories 2026-07; TO-DO-LIST-HW-FOR-AMITAVA-SIR 2026-05; attendence-api-assignment and bulk-certificate-generator 2026-10.

I build software, analytics, machine-learning, and Generative AI projects to learn through practical implementation and demonstrate what I can do. My project work ranges from data analysis and dashboards to AI-powered applications and relational databases.

I developed SnapSolver as my final-year capstone project with a five-person team.

I developed SnapSolver, an educational application using Python, Streamlit, and the Google Gemini API, that accepts questions and generates structured, step-by-step solutions.

My SnapSolver work included conversation history, voice-based question input, prompt engineering, application architecture, data-flow diagrams, and project documentation.

I developed CarbonX, a carbon-credit trading application using Python, Streamlit, and MySQL.

CarbonX's database structures cover companies, projects, carbon credits, transactions, audits, certificates, payments, and reports.

I implemented SQL joins, aggregate queries, views, and database triggers in CarbonX.

I created a CarbonX trigger that automatically updates company carbon balances after completed transactions.

I built an interactive HR analytics dashboard using Power BI, Power Query, DAX, and Excel.

My HR dashboard analyzes employee attrition across departments, job roles, age groups, salary, education, gender, and tenure.

The HR dashboard KPIs include employee count, attrition count, attrition rate, average income, average age, and years at the company.

I used interactive charts, slicers, KPI cards, and drill-down analysis in the HR dashboard.

My credit-card fraud-detection work used Python, pandas, and scikit-learn to analyze transaction data, investigate class imbalance, preprocess data, and explore classification models.

I evaluated fraud-detection models using precision, recall, F1-score, accuracy, and confusion matrices.

I analyzed an insurance dataset containing 1,338 records using Python, pandas, and Seaborn.

For insurance medical-cost prediction, I explored age, BMI, smoking status, region, children, and medical charges, and built a pipeline using ColumnTransformer, OneHotEncoder, and Linear Regression.

I evaluated insurance predictions using MAE, MSE, RMSE, and R-squared.

I developed a waste-classification application using Python, TensorFlow, Keras, and Streamlit to classify images as biodegradable or non-biodegradable.

My waste-classification work covered dataset preparation, image classification, training and validation workflows, and application integration.

I secured third place in a Smart India Hackathon-related hackathon through my contribution to an AI-based waste-classification solution.

I built a product-management application using Python, Gradio, and the Groq API, with CRUD operations for product creation, retrieval, updating, deletion, searching, and reporting.

I integrated a Generative AI assistant into the product-management application for product descriptions, summaries, recommendations, and user queries.

I developed an admission-chance prediction application using Python, Flask, HTML, and CSS, with preprocessing, model serialization, prediction logic, and a browser-based interface.

I worked on PayPerform 360, an Excel-based payroll analytics workbook covering 100 employees across five departments and three organizational levels.

I participated in an ISRO Hackathon involving technology-driven problem solving.

I qualified for Round 2 of TCS CodeVita.

I completed a technical workshop conducted under Kshitij, IIT Kharagpur.

I served as a senior mentor in my college's Google Developer Group.

I hosted Devnexus and a Devnexus hackathon.

I use GitHub to maintain source code and demonstrate my technical projects; my projects include documentation, requirements, and implementation details.

My portfolio is at https://mousom-os-portfolio.vercel.app, and it has an animated, operating-system-inspired interface.

My GitHub profile is https://github.com/Mousam098.

My LinkedIn profile is https://linkedin.com/in/mousom-koley-5b845a262.

I use Python, SQL, C, HTML, CSS, and JavaScript fundamentals, and I understand fundamentals of Object-Oriented Programming, Data Structures, Algorithms, and the Software Development Life Cycle.

My data-analytics skills include pandas, NumPy, exploratory data analysis, data cleaning, feature engineering, statistical analysis, data validation, and KPI analysis.

I have worked with scikit-learn, regression, classification, preprocessing, feature evaluation, model training, and model evaluation, and have exposure to clustering fundamentals, computer vision, image classification, TensorFlow, and Keras.

I explore LLMs, prompt engineering, Generative AI APIs, RAG fundamentals, embeddings, and vector database concepts, and have worked with Google Gemini API, Google GenAI SDK, Groq API, LangChain fundamentals, and Ollama.

I use Streamlit, Flask, FastAPI, and Gradio to develop applications and have experience integrating REST APIs and external AI services.

I work with MySQL and Oracle SQL, including relational database design, joins, subqueries, views, triggers, aggregations, and CRUD operations.

I use Power BI, DAX, Power Query, Excel 365, PivotTables, Pivot Charts, XLOOKUP, VLOOKUP, and What-If Analysis, and have exposure to Tableau.

I use Git, GitHub, VS Code, Jupyter Notebook, Google Colab, and Postman, and have fundamentals-level exposure to Docker, AWS EC2, AWS S3, Render, and Vercel.

I achieved 95% in Higher Secondary Science and secured the highest Science marks at my school.

I completed an EF SET English certificate.

I believe a working project demonstrates technical understanding better than simply listing technologies on a résumé.

I prefer understanding why something works instead of memorizing commands or solutions.

I evaluate career opportunities based on technical responsibilities, learning potential, compensation, work environment, and long-term career growth.

I prefer honest assessments over exaggerated claims.

I do not believe that adding an AI component to an application automatically makes it useful; technology should solve an actual problem.

I believe developers should understand the code and systems they build instead of blindly depending on AI-generated solutions.

I prefer a hands-on workflow of understanding the problem, exploring possible solutions, implementing an approach, and testing the result.

I value readable code, structured project architecture, proper documentation, and reproducible workflows.

I prefer building smaller working solutions before introducing unnecessary complexity.

I am strengthening my Python programming and problem-solving fundamentals and improving my Data Structures and Algorithms knowledge.

I am exploring Machine Learning, NLP, Generative AI, LLM applications, RAG pipelines, embeddings, and AI agent workflows.

My primary career goal is to become a Software Engineer specializing in AI/ML and Generative AI applications.

I am also interested in Data Analyst, Business Intelligence Analyst, Python Developer, Generative AI Developer, and Data Science Associate roles.

My longer-term aim is to understand complex problems independently, design solutions, and build reliable systems. I want my professional identity to reflect actual skills, meaningful projects, continuous improvement, and the quality of my delivered work.

I work across Python development, Data Analytics, Machine Learning, Generative AI, and application development.

I enjoy building practical applications, experimenting with AI APIs, solving technical problems, working with datasets, and turning ideas into functional projects.

I completed Higher Secondary education in Science in 2022 with 95%, studying Physics, Chemistry, Mathematics, and Biology.

I achieved the highest marks in Science at my school.

My academic subjects included Data Structures, Database Management Systems, Artificial Intelligence, Machine Learning, Statistics, Data Analytics, Computer Networks, Operating Systems, Software Engineering, and Cloud Computing.

In 2026, I completed practical training in Data Analytics with Generative AI through Anudip Foundation's Centre of Excellence in Kolkata.

My Anudip training covered Python, SQL, Excel, Power BI, statistical analysis, Machine Learning, dashboard development, Generative AI, and prompt engineering.

During practical work, I worked with datasets, performed exploratory data analysis, developed dashboards, explored predictive modeling, and integrated AI APIs into applications.

I am pursuing entry-level opportunities in AI/ML Engineering, Python Development, Data Analytics, Business Intelligence, and Generative AI development.

I am interested in opportunities where I can work on real technical problems, develop practical engineering skills, and contribute to useful software products.

People can ask me about Python, SQL, Machine Learning fundamentals, Generative AI, LLMs, RAG, data analytics, Power BI, application development, project building, and early-career technology opportunities.

I also shared a brief update about joining ASX and my hopes for connecting with people there.

I said, “Yay, I joined ASX today.”

I was looking forward to meeting folks on this platform to make meaningful connections.

My headline is: Software Engineer | Applied AI & Generative AI | Python • SQL • RAG • LLMs • FastAPI | B.Tech IT 2026

I'm a B.Tech Information Technology graduate (2026) focused on building practical solutions at the intersection of Software Engineering, Machine Learning, Data Analytics, and Generative AI.

I am an AI/ML & Generative AI Trainee at Accenture CSR – Center of Excellence Training Program (Mar 2026 to Aug 2026), Kolkata, West Bengal, India.

My education includes Government College of Engineering and Leather Technology: Bachelor's Degree, Information Technology (2022 to 2026).

I also list Government College of Engineering and Leather Technology: Bachelor of Technology, Information Technology.

I hold the EF SET English Certificate (C2 Proficient), Jul 2026, EF.

One of my projects is SnapSolver — AI-powered problem-solving application using Streamlit and Gemini API.

One of my projects is Insurance Cost Prediction — end-to-end ML workflow covering EDA, preprocessing, feature transformation, regression, pipelines, and model evaluation.

In my Accenture CSR training, I completed hands-on work in AI, machine learning, data analytics, and generative AI. I developed and evaluated regression, classification, and clustering models, gained practical exposure to LLMs, prompt engineering, and API integration, and created project-based solutions combining data preprocessing, predictive modeling, and analytics.

My degree coursework includes data structures and algorithms, object-oriented programming, database management systems, operating systems, computer networks, software engineering, machine learning, and data analytics. I have also developed academic and independent projects in AI, machine learning, Python application development, computer vision, data analytics, and database systems.

My technical stack includes Python, SQL, machine learning, scikit-learn, Pandas, NumPy, Flask, FastAPI, Streamlit, Power BI, Git/GitHub, REST APIs, and generative AI technologies. I have also worked with LLMs, prompt engineering, RAG, LangChain, vector databases, the Gemini API, and AI-powered application development.

• Skills I list include Microsoft Power Query, Keras, DAX, FastAPI, TensorFlow, Scikit-Learn, Streams, Git, Docker Products, Tableau, EDA, Jupyter, GitHub, Teamwork, Microsoft Power BI, Microsoft Excel, MySQL, Problem Solving, Data Annotation, REST APIs, API Development, Oracle SQL Developer, Cloud Computing, Amazon Web Services (AWS), NumPy, Pandas (Software), Regression Analysis, Flask, Streamlit, Matplotlib, Seaborn, Database Development, Data Engineering, Object-oriented Languages, Generative AI, Data Analytics, Machine Learning, mechine learnning, Software Development, Data Structures, Computer Network Operations, Object-Oriented Programming (OOP), Database Management System (DBMS), Python (Programming Language), SQL, Software Infrastructure, Engineering, Developer Tools, Internet Software, Program Creation.

I emphasize taking a problem from data through analysis, modeling, and an API or application to a usable product, rather than working only at the theoretical level. I focus on clean engineering, continuous learning, analytical problem-solving, and technology that creates measurable impact. I continue strengthening my foundations in data structures and algorithms, machine learning, system design, APIs, databases, cloud technologies, and production-oriented AI development.

People ask me why I prioritize learning over money.

• Other projects on my profile include CarbonX, a carbon credit trading platform with company registration, carbon credit management, SQL triggers, transaction processing, dashboards, and reporting; an HR Attrition Analytics Dashboard built with Power BI; Waste Classification AI, an image-classification application for biodegradable and non-biodegradable waste; and generative AI applications using LLM APIs, RAG concepts, prompt engineering, Streamlit, Gradio, and Python.

For SnapSolver, I describe an academic assistant built with Python, Streamlit, and the Google Gemini API, supporting text and voice input, voice-to-text, session history, and an interactive interface. My role included design and development, AI API integration, frontend functionality, and application workflow testing. My GitHub is https://github.com/Mousam098, and my portfolio is https://mousom-os-portfolio.vercel.app.

For Insurance Cost Prediction, I describe exploratory data analysis, feature engineering, data preprocessing, regression models, model evaluation, and visualization, using Python, Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn. My GitHub profile is https://github.com/Mousam098.

My profile lists Waste Classification AI as using image preprocessing, model inference, and a Streamlit interface, with Python, TensorFlow, Keras, computer vision, and Streamlit.

I am interested in software engineering, AI/ML engineering, generative AI, applied AI, data and analytics, and backend development. I state that I am in India and open to relocation, and open to software engineering, AI/ML, generative AI, and graduate technology opportunities. My GitHub and projects are available in the Featured section.