RENU VIJAY VARAPRASAD VISWANADULA

[email protected] • +919640579799 • Bhimavaram
LinkedIn: https://linkedin.com./in/vijayvaraprasad • GitHub: https://github.com/Vijayvaraprasad

PROFESSIONAL SUMMARY

Motivated AI professional with 1 year of practical experience developing innovative machine learning models using Python, TensorFlow, and PyTorch. Currently honing advanced AI skills through the Infosys Springboard internship, driving impactful projects that improve data-driven decision-making. Demonstrated ability to quickly learn and apply cutting-edge AI techniques, with a strong commitment to delivering scalable and efficient AI solutions that support business growth and innovation.

EDUCATION

bachelor of technology
08/2023 - 04/2027
SRKR Engineering college , Bhimavaram GPA: 8.6

SKILLS

Technical Skills: Python, Java, TensorFlow, PyTorch, Pandas, NumPy, Scikit-learn, Matplotlib, Data Structures, Algorithms, Linear Algebra, Probability, Statistics, Problem Solving
Soft Skills: Communication, Teamwork, Adaptability, Critical Thinking, Collaboration
Tools: Jupyter Notebook, Visual Studio Code, Git, Docker, AWS, Azure
Other: English (Fluent), Agile Methodology, AWS Certified, Machine Learning Domain Knowledge

PROJECTS

Image Classification using CNN (Deep Learning Project)
Technologies: TensorFlow, Python, Jupyter Notebook, NumPy, Pandas, Convolutional Neural Networks (CNNs), Data Preprocessing, Data Augmentation, Supervised Learning, Model Visualization, Hyperparameter Tuning, Architecture Optimization, Collaborative Development
• Led the end-to-end development of a convolutional neural network (CNN) using TensorFlow to classify a dataset of over 10,000 images, improving accuracy to 88% through systematic hyperparameter tuning and architecture optimization
• Developed robust data preprocessing and augmentation pipelines with Pandas and NumPy, reducing overfitting by 15% and enhancing model generalization across diverse image inputs
• Collaborated closely with cross-functional team members by visualizing training progress and performance metrics in Jupyter Notebook, accelerating model refinement cycles and improving communication of technical insights
Stock Price Prediction using LSTM (AI + Data Analysis Project)
Technologies: Pandas, NumPy, TensorFlow, LSTM (Recurrent Neural Network), Hyperparameter Tuning, Sequence Padding, Data Normalization, Matplotlib, Flask, Docker, Jupyter Notebook, Git
• Collected and processed over 1.2 million records spanning 5 years of stock market data using Pandas and NumPy to ensure robust training datasets
• Developed and optimized an LSTM-based Recurrent Neural Network in TensorFlow, improving predictive accuracy by 15% through hyperparameter tuning, sequence padding, and data normalization
• Achieved a Mean Squared Error (MSE) below 0.002, demonstrating high model precision, and deployed the model locally with Flask and Docker for interactive, real-time stock trend predictions

CERTIFICATIONS

ML by google
11/2025
Google
completion of AI modules
05/2025
Infosys