Amarnath Reddy Dhanireddy
Professional Summary
Machine Learning Engineer with 0 years of experience applying AI and ML techniques to build predictive models and deep learning solutions. Hands-on experience in data preprocessing, exploratory data analysis, model development, and visualization from academic projects and an internship. Skilled in Python, TensorFlow, PyTorch, scikit-learn, Pandas, and Power BI/Tableau.
Technical Skills
Work Experience
- Analyzed and preprocessed structured and unstructured datasets using Python, Pandas and NumPy to improve data consistency and readiness for modeling.
- Designed and executed exploratory data analysis (EDA) workflows and created visualizations with Matplotlib and Tableau/Power BI to reveal trends, correlations, and key business signals.
- Performed feature engineering and data transformation to enhance predictive signal used by classification and regression models developed in scikit-learn.
- Developed and evaluated supervised machine learning models, applying model selection, cross-validation, and metric-based comparison to choose robust algorithms.
- Implemented automated data-cleaning scripts and reusable preprocessing pipelines in Python to streamline experiment setup and reduce repetitive manual work.
- Documented modeling decisions, data issues, and analysis results; prepared interpretive visual reports to communicate findings to non-technical stakeholders.
Projects
- Built a ViT-based pipeline to detect manipulated media, applying contrastive learning to improve feature separability between authentic and fake samples.
- Conducted data preprocessing, augmentation and training experiments to increase model robustness to common tampering techniques and domain shifts.
- Compared model performance against baseline CNNs and performed error analysis to identify failure modes and guide iterative improvements.
- Implemented a Core Java-based online banking system using OOP principles to model accounts, transactions and customer operations.
- Developed secure transaction handling modules with input validation and exception management to simulate real-world banking workflows.
- Used Java Collections to manage account data and implemented unit-level checks to ensure data integrity across operations.
- Developed a churn-prediction model using historical customer data to support proactive retention strategies.
- Performed data cleaning, EDA, feature engineering and visualization to identify key factors influencing churn and inform model features.
- Trained and validated classification models in scikit-learn and compared performance using precision, recall and ROC analysis.
Education
Certifications
Achievements
- College hackathons - recognized for problem-solving and teamwork
- Active contributor to college workshops and coding competitions
- Top performance in Java development and practical ML projects
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