BHANUPRAKASH PONNALA

MCA Student | Aspiring ML & Cloud Engineer

Andhra Pradesh, IN.

About

Highly motivated and detail-oriented MCA student with a robust foundation in computer science, specializing in Machine Learning and Cloud technologies. Proven ability to develop and deploy advanced models, as demonstrated by impactful projects in CNN-based disease detection and predictive analytics for diabetes, leveraging TensorFlow, Keras, and AWS. Eager to apply strong technical skills in model development, data preprocessing, and cloud architecture to drive innovation and contribute fresh perspectives within a dynamic organization.

Education

Swarnandhra College of Engineering and Technology (Autonomous)

Andhra Pradesh, Andhra Pradesh, India
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Master of Computer Applications (MCA) Computer Applications Grade: CGPA: 7.4

Adikavi Nannaya University, Sri YN College

Andhra Pradesh, Andhra Pradesh, India
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Bachelor of Science in Computer Science Computer Science Grade: CGPA: 7.24

Certificates

AWS Cloud Architecting

EDUSKILLS

Cloud Computing Internship

TRIARIGHT & GlobalOneServices

Skills

Machine Learning Frameworks & Libraries

TensorFlow, Keras, OpenCV, NumPy, Matplotlib.

Programming Languages

C, Python, SQL.

Web Technologies

HTML, CSS.

Databases

MongoDB, SQLite.

Tools & Platforms

MS Excel, MS PowerPoint.

Cloud & DevOps

AWS EC2, IAM, CI/CD.

Machine Learning Techniques

Convolutional Neural Networks (CNN), Logistic Regression, Decision Trees, Support Vector Machines (SVM).

Data Analysis & Preprocessing

Data Preprocessing, Missing Value Handling, Normalization, Feature Selection, Hyperparameter Tuning, Evaluation Metrics (Accuracy, Precision, Recall, AUC-ROC).

Interests

Sports

Cricket.

Entertainment

Watching Anime.

Projects

Cotton Leaf Disease Detection Using CNN

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Summary

Developed an advanced Convolutional Neural Network (CNN) model leveraging TensorFlow and Keras to accurately detect and classify cotton leaf diseases, enhancing agricultural diagnostics.

Pima Diabetes Prediction Model

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Summary

Built and rigorously evaluated multiple machine learning models to predict diabetes likelihood using the Pima Indians dataset, focusing on model accuracy and reliability.