BTech Computer Science (AI & Data Science)
MIT World Peace University, Pune
LinkedIn: https://www.linkedin.com/in/muffadal-b-4b660a258/
GitHub: https://github.com/muffi03
I am a Computer Science student specializing in Artificial Intelligence and Data Science with a strong interest in building real-world technology products.
My interests span across machine learning systems, computer vision, and edge AI, along with building digital products and startups. I enjoy working at the intersection of engineering, product thinking, and business strategy.
Alongside academic projects, I actively explore building practical systems such as AI dashboards, IoT-based monitoring systems, and predictive machine learning models.
I am also working on personal ventures including web-based products and startup concepts, combining technical development with product design and business thinking.
• Machine Learning and Explainable AI
• Computer Vision and Edge AI systems
• Building practical AI-driven dashboards and tools
• Developing product-focused technical projects
• Exploring startup ideas in technology and digital products
March 2025 – January 2026
Worked in a cross-functional role combining sales operations, marketing strategy, and data analysis.
Key Contributions:
• Managed sales operations and supported business growth through strategic planning and execution
• Handled social media presence and digital outreach initiatives
• Performed data analysis on sales and engagement metrics to identify trends and improve decision-making
• Collaborated with marketing and operations teams to align digital strategy with business goals
This experience helped develop strong analytical thinking, business understanding, and data-driven decision making.
A machine learning system that predicts credit card default risk and explains predictions using SHAP and LIME.
Features:
• Credit risk prediction using ML models
• Model interpretability using Explainable AI frameworks
• Interactive Streamlit dashboard for analysts
• Counterfactual analysis and prediction insights
Tech Stack:
Python
Scikit-learn
SHAP
LIME
Streamlit
Pandas
NumPy
A supervised machine learning model designed to predict loan approval outcomes based on applicant financial data.
Features:
• Data preprocessing and feature engineering
• Training ML classification models
• Predicting loan eligibility using financial attributes
Tech Stack:
Python
Pandas
NumPy
Scikit-learn
A hybrid IoT-based monitoring system combining infrared sensors and camera modules for accurate occupancy detection.
Features:
• Multi-modal sensor fusion using IR sensors and cameras
• Edge computing architecture using ESP32 and Raspberry Pi
• Computer vision validation using OpenCV
Tech Stack:
ESP32
Raspberry Pi
OpenCV
IoT Sensors
A web-based system designed to manage cattle records and track dairy production data.
Features:
• Cattle record management system
• Milk production tracking dashboard
• Structured data handling and management
Tech Stack:
HTML
CSS
JavaScript
Artificial Intelligence
Machine Learning
Computer Vision
Explainable AI
Edge Computing
IoT Systems
Startup Product Development
Programming Languages:
Python
C
C++
Java
Libraries and Frameworks:
NumPy
Pandas
Scikit-learn
OpenCV
SHAP
LIME
Tools:
Git
GitHub
VS Code
Jupyter Notebook
Linux
• Building a strong portfolio of machine learning and AI projects
• Exploring real-world AI applications and deployment
• Contributing to open-source AI and ML projects
• Preparing for internships in AI, machine learning, and applied engineering