BALA VAMSI PALAKONDA

Computer Science Engineer
523111, Kanigiri, IN.

About

Highly motivated Computer Science Engineer specializing in Machine Learning, with a proven ability to design and implement innovative AI solutions. Adept at leveraging advanced algorithms and deep learning frameworks to solve complex problems, as demonstrated by impactful projects in URL phishing detection and plant disease classification. Eager to apply strong problem-solving, analytical, and communication skills to contribute to cutting-edge technological advancements in a dynamic environment.

Education

QIS College Of Engineering and Technology
Ongole, Andhra Pradesh, India

→

Bachelor of Technology

Computer Science Engineering

Grade: 8.56/10.0

Sri Sai Junior College
Kanigiri, Andhra Pradesh, India

→

Intermediate

Science (Mathematics, Physics, Chemistry)

Grade: 961/1000

Courses

MPC

Z P High School
Tallur, Andhra Pradesh, India

→

SSC (Secondary School Certificate)

General Studies

Grade: 9.8/10.0

Awards

National Means-cum-Merit Scholarship (NMMS)

Awarded By

Government of India

Awarded for exceptional academic performance and merit, recognizing excellence in studies and supporting higher education.

Languages

Telugu
English

Skills

Programming Languages

Python, Java, C#.

Machine Learning & AI

Machine Learning, Deep Learning, Convolutional Neural Networks (CNN), Scikit-learn, TensorFlow, Keras, OpenCV, Random Forest, SVM.

Data Analysis & Tools

Pandas, NumPy, Jupyter Notebook, SQL, Data Analysis, Feature Engineering.

Web Development

HTML, CSS, JavaScript.

Soft Skills

Communication, Problem Solving, Quick Learner, Time Management, System Design.

Projects

URL Phishing Detection using ML

→

Summary

Designed and implemented a machine learning system to classify URLs as phishing or legitimate based on extracted features such as domain structure, presence of suspicious keywords, and length metrics.

Plant Leaf Disease Detection using ML

Machine learning

→

Summary

Developed an advanced machine learning system to detect and classify plant leaf diseases using image data, employing Convolutional Neural Networks (CNNs) for feature extraction and pattern recognition.