- Programming Languages: Python, Java, R, SQL
- ML/AI Frameworks & Libraries: TensorFlow, PyTorch, scikit-learn, Keras, NumPy, Pandas, Matplotlib
- Data Processing & Tools: Google Colab, Jupyter, Anaconda, MySQL, SQLite, Excel (Pivot Tables, VLOOKUPs)
- Core Competencies: Machine Learning, Deep Learning, Computer Vision, Natural Language Processing, Data Visualization, Statistical Analysis
- Cloud & Deployment: AWS, Google Cloud, Azure (basic knowledge)
- Optimized AI model performance by streamlining data collection, cleaning, and preprocessing, improving data quality by 30%.
- Enhanced customer engagement by managing and automating workflows in Odoo CRM, reducing manual intervention by 40%.
- Developed and deployed an AI-powered chatbot, improving customer response time by 50% and increasing user interactions.
- Automated data extraction using Python web scraping scripts, reducing research time by 60% and enhancing lead generation.
- Enhanced model performance by performing data cleaning and optimization, leading to a 20% improvement in efficiency.
- Designed and implemented regression models, leveraging statistical analysis and feature engineering to improve predictive accuracy.
- Trained and evaluated machine learning models using scikit-learn, optimizing hyperparameters to increase model accuracy and robustness.
- Developed insightful data visualizations, enabling data-driven decision-making and improving interpretability of model outputs.
- Achieved 98% accuracy in detecting DeepFake videos using CNNs, Vision Transformers (ViT), and BlazeFace.
- Improved processing speed by 94% with CUDA-based acceleration for real-time analysis.
- Developed an automated Twitter bot using Selenium to detect, analyze, and respond to DeepFake verification requests.
- Trained on Facebook’s DFDC dataset with over 120,000 videos for robust detection.
- Designed for scalability and cloud deployment on AWS and Google Cloud for efficient large-scale processing.
- Increased image classification accuracy to 95% using Support Vector Machines (SVM), Deep Learning, and Neural Networks.
- Optimized image processing speed by 40%, reducing classification time from 5 seconds to 3 seconds per image.
- Automated dataset collection, expanding training data by 30%, using Bing Image Downloader for large-scale image acquisition.
- Enhanced feature extraction efficiency by 50% with advanced preprocessing techniques in NumPy, Matplotlib, and scikit-learn.
- Reduced misclassification errors by 35% through iterative model training and fine-tuning of hyperparameters.
- Scalable deployment-ready model, designed for real-time image recognition on cloud-based platforms.
- The Digital Looking Glass: Predicting DeepFake Evolution through Social Media Bot Analysis, ZKG International vol. IX, 2024
- Issue I.DOI: https://bit.ly/DigitalLookingGlass
Osmania University
- Bachelor Of Engineering - Computer Science (Specialized in AI & ML) Hyderabad, India | July 2024 | CGPA: 7.90
- Relevant Courses: AI, Advanced ML, Data Mining, DBMS, Big Data, Deep Learning
- IEEE Member (2021-2024): Assisted in event organization and inventory management.