Hy! đź‘‹,
I'm
CSE Graduate from North South University | Developer
Resume
As a dedicated Computer Science and Engineering student, I
prioritize high-quality Projects and
continuous personal growth. I have a proven ability to quickly acquire technical skills, adapt
seamlessly to new roles, and approach tasks with diligence and integrity. My strengths lie in
problem-solving, teamProjects, leadership, and effective communication. With a focus on
innovative solutions, I am committed to adding significant value in every role through critical
thinking and a results-oriented approach....
email : mdbakerfarhad@gmail.com
Education is not the learning of facts, but the training of the mind to think
B.Sc in Computer Science & Engineering | CSE
GPA: 3.04
Higher Secondary School Certificate
GPA: 4.83 | Science
Secondary School Certificate
GPA: 5.00 | Science
Develop and execute comprehensive marketing strategies and campaigns that align with the company's goals and objectives.
Lead, mentor, and manage a high-performing marketing team, fostering a collaborative and resultsdriven Projects environment
March 2023 - March 2024
Facilitated effective team management by coordinating tasks, schedules, and resources to ensure seamless Projectsflow.
Successfully communicate with clients to understand their needs, provide exceptional customer service, and foster strong relationships, resulting in increased client satisfaction.
November 2021 - March 2023
Worked as Sub-Executive team member and organized three Intra NSU events and one Hackathon contest.
In-Charge of arranging in campus events like Hour of code, Programming Contest etc.
Being an active member of NSU ACM SC, I executed an event named Technovation 2.0, an annual event comprised of gaming contest, robosoccer, megabot clash, mini hackathon etc.
September 2019 - February 2023
Analysis of Machine Learning Models for Wearable Devices Centric Human Activity Recognition
This Projects presents a comprehensive study on applying machine learning models for human activity recognition. The increasing number of wearable devices and the growing interest in health monitoring and intelligent environments have fueled the demand for robust and accurate systems that automatically recognize human activities. This study explores and compares the performance of different machine learning (ML) models in human activity recognition. This study intends to identify the most effective ML model to classify and predict human activities accurately. In this study, various models are evaluated, including ensemble-based classifiers such as Decision Tree, Random Forest, K-Nearest Neighbor, Logistic Regression, AdaBoost algorithm, and XGBoost algorithm. All models have been evaluated using the publicly available dataset “Human Activity Recognition with Smartphones,” which captures six daily activities: Lying, Standing, Sitting, Walking, Walking Upstairs, and Walking Downstairs. Among all the applied models, XGBoost accomplished the highest accuracy of 99.52% with 100% precision, 100% recall (100%), and 1.0 F1 score. Hyperparameter tuning on the ML models is implemented to attain the best accuracy.
