Aspiring AI Engineer
Motivated AI & ML undergraduate with hands-on experience in building, optimizing, and deploying machine learning models. Skilled in predictive analytics, feature engineering, model building and model optimization, seeking impactful internship/full time opportunities.
I am a passionate AI & Machine Learning enthusiast, currently pursuing my BTech in AIML. My journey started with a deep curiosity for data-driven decision-making, which evolved into mastering machine learning, deep learning, NLP, and real-world AI applications.
Through academic and personal projects, I’ve worked with Machine learning and Deep learning algorithms like Linear Regression, Logistic Regression, Ensemble Methods, SVMs, CNNs, RNNs, LSTMs etc., applying them to solve real-world problems effectively.
A key achievement is completing a "30 Days, 30 Projects" challenge, where I developed and deployed diverse ML projects daily, gaining full-stack ML experience from data preprocessing to deployment.
Next Word Prediction is an NLP project that predicts the next word in a text sequence using LSTM models trained on Shakespeare's Hamlet dataset. Built with Python, TensorFlow, and Streamlit, it allows real-time predictions and is deployed on Streamlit Cloud.
Diabetes Prediction is a machine learning app that predicts diabetes risk based on patient health metrics. Built using an SVM classifier with Python and flask, it enables real-time predictions through a user-friendly web interface and deployed on Render.
Wine Quality Prediction is a machine learning web app that predicts wine quality based on chemical properties. After comparing multiple models, AdaBoost gave the best performance. The app is built with Python, Streamlit for the UI, and deployed for real-time predictions.
Twitter Sentiment Analysis is an NLP project that classifies tweets into positive, negative, or neutral sentiments using machine learning models. Built with Python, NLP libraries (NLTK, Scikit-learn), and deployed via Streamlit for real-time sentiment prediction.
My professional journey and the experiences that have shaped my career
Edunet Foundation (APSSDC)
Built an employee burnout prediction model and Experimented with Logistic Regression, Random Forest, and XGBoost algorithms, achieving 92% accuracy with Logistic Regression. Deployed on Render, gaining experience in Python, data preprocessing, model evaluation, and deployment.
Edu Skills
Gained practical exposure to AI and Machine Learning concepts through extensive hands-on training sessions. Worked with real-world datasets to apply various machine learning algorithms, optimization techniques, and performance tuning methods.
Strengthened expertise in data preprocessing, model evaluation, hyperparameter tuning, and deployment strategies, building a strong foundation for real-world AI/ML problem-solving.
Professional certifications that validate my expertise and commitment to continuous learning
Milestones that define my journey in technology and innovation
Secured 1st place in the prestigious Hackathon organized by ISTE at Mohan Babu University, competing against top talent from the Data Science and AI/ML departments.
Ranked in the top 2.37% among nearly 2.5 lakh candidates in the Engineering Entrance Exam of Andhra Pradesh, demonstrating exceptional academic excellence.
Successfully completed the 30 Days, 30 Projects Challenge, developing and deploying 30 machine learning models in a month, showcasing expertise in ML, AI, and real-world applications.
Let's discuss your next project or just say hello. I'm always open to new opportunities and interesting conversations.