I am a Computer and Communication Engineering graduate with hands-on experience in data science internships, machine learning development, and end-to-end predictive modeling. I work primarily with Python, SQL, and modern ML libraries to turn complex datasets into actionable insights.
I have performed exploratory data analysis, preprocessing, feature engineering, model evaluation, hyperparameter tuning, and real-time pipeline development. My internship work includes improving model accuracy, reducing processing time, and integrating external data into classification workflows.
I have built projects in fraud detection, anomaly detection, digital-twin simulation, retrieval-augmented generation, and financial compliance automation. I also developed a full-stack invoice management application backed by PostgreSQL for efficient storage and retrieval of thousands of invoices.
My technical toolkit includes TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy, OpenCV, MySQL, MongoDB, PostgreSQL, Git, and Jupyter Notebook. I am comfortable working across data preparation, model development, database design, API integration, and deployment-oriented ML workflows.
I am a published co-author on predictive models for heart disease and have been recognized through academic excellence awards, the Dean's List, and finalist positions in Smart India Hackathon, Deloitte USI Capstone Program, and Flipkart Grid. I am seeking an entry-level data science or machine learning opportunity where I can contribute analytical and engineering skills.
Conducted exploratory data analysis and data preprocessing on complex datasets to identify actionable insights.
Designed and optimized predictive machine learning pipelines to improve model evaluation metrics and overall performance. Collaborated on data-modeling workflows and integrated data science solutions into project deliverables.
Applied data preprocessing techniques including scaling, encoding, and PCA, improving model accuracy by 12%.
Built a real-time machine learning pipeline with feature engineering and hyperparameter tuning, reducing processing time by 40%. Integrated real-time data into classification pipelines and reduced classification latency by 25%.
Deployed a multimodal emotion-classification system using text, speech, and image inputs, achieving 92% prediction accuracy.
Executed API integrations for data retrieval, increasing access to external data sources by 40% and reducing manual data-entry time by 25%.
Developed and optimized machine learning pipelines for predictive modeling using Python, TensorFlow, and Scikit-learn.
Performed exploratory data analysis and feature engineering on client datasets to improve model accuracy and reduce overfitting.
Bachelor of Technology, Computer & Communication Engineering
B.Tech in Computer & Communication Engineering with a CGPA of 7.62. Achieved a perfect 10 GPA in the final semester and received the Dean's List of Excellence for the highest GPA in the seventh semester.
Senior Secondary
CBSE Senior Secondary education with 70.0%.
Secondary
CBSE Secondary education with 76.8%.
This professional hasn’t added portfolio projects yet.
This professional hasn’t listed any services yet.
