Muhammad Israr

Final Year Project

• Comparative analysis of Brain Tumor Classification and Detection using Machine Learning Algorithms

A comparative study was conducted using five models: Logistic Regression, Random Forest, CNN, AlexNet, and VGG-19 for brain tumor classification. Traditional models like Logistic Regression and Random Forest showed decent accuracy but lacked deep feature extraction capability. CNN and AlexNet significantly outperformed them with better spatial feature learning. VGG-19 achieved the highest accuracy due to its deep architecture, though it required more computational resources.

Self Work

• Titanic Survival Predication

Titanic survival prediction uses machine learning models to identify factors influencing passenger survival based on features like age, gender, class, and fare. Models like Logistic Regression and Random Forest effectively classify survival outcomes with notable accuracy.

• Credit card fraud detection
• Mall Customer Segmentation
• Bird Species Classification

As a graphic designer, I specialize in creating visually compelling designs tailored to client needs. My work includes album covers, postcards, flyers, book designs, and more, each crafted to deliver strong visual impact and clear communication. With a blend of creativity and precision, I aim to produce designs that resonate with the target audience and elevate brand identity.

I worked on Excel sheets and various call center websites, efficiently entering and managing customer data such as names, addresses, emails, phone numbers, and other relevant details

I worked in a call center environment where I was responsible for monitoring employee call activities and verifying the accuracy of client-provided data to ensure its validity and reliability.