M.Sc. Artificial Intelligence & Data Analytics student at Amity University, Noida. Focused on developing end-to-end Machine Learning pipelines, deep sequence classifiers, and predictive time-series models.
> epoch 15/50: loss=0.104 acc=91.2%
> epoch 30/50: loss=0.038 acc=96.8%
> epoch 45/50: loss=0.015 acc=98.4%
> process finished with code 0 (success)
I am an M.Sc. student specializing in Artificial Intelligence and Data Analytics at Amity University. With a robust undergraduate foundation in Computer Applications from Bharati Vidyapeeth University, Pune, I specialize in constructing predictive machine learning frameworks, tuning sequence classification networks, and analyzing structured business data.
My engineering approach centers on modular programming design in Python, utilizing mathematical concepts for regression/classification, feature extraction, and performance evaluation. I am highly motivated to implement interpretable AI models that automate operational intelligence and support predictive decision systems.
Designed an end-to-end ML pipeline using 200+ biomedical voice samples to predict Parkinson's disease presence and severity. Engineered key features and compared multiple models (Logistic Regression, Random Forest, SVM) via cross-validation, successfully optimizing prediction accuracy from 78% to 86%.
Built an interpretable churn prediction model on a dataset of 7,000+ customers. Addressed class imbalance using SMOTE and achieved an ROC-AUC score of 0.89. Utilized SHAP values to identify key churn drivers and optimized decision thresholds to balance precision and recall.
Developed a time-series predictive model using simulated sensor data (10,000+ records) to forecast smart manufacturing equipment failures. Extracted rolling window features and trend lines, achieving 84% anomaly prediction accuracy and reducing downtime by 25%.
Constructed an LSTM-based deep classifier trained on 20,000+ text samples to capture long-term sequential dependencies. Implemented custom tokenization, padding, and embedding layer architectures, achieving an 88% classification accuracy (a 10% improvement over baselines).
Conducted a systematic literature review analyzing 50+ papers spanning traditional ML, deep learning, and transformer architectures. Developed a detailed taxonomy based on contextual feature engineering and identified critical research gaps in multilingual and cross-domain sarcasm detection.
Advanced studies in neural optimization, machine learning modeling, structured data mining, predictive statistics, and big data configurations. Focusing on practical applications of deep learning pipelines.
Graduated with a CGPA of 6.9. Constructed strong core foundations in computer applications, programming paradigms (Java, Python), database management systems (SQL), and algorithm designs.
- **President**, Music Society BVP Pune: Led performer divisions and directed major events.
- **Secretary General / Co-Secretary General**, TAFS Model United Nations (2019-2020): Led event coordination, directed logistics, and handled delegate operations.
- **NCC Air Wing 'C' Certificate** Holder.
To demonstrate some AI design principles in a fun way, I have built a simulated neural assistant. Recruiters can click any of the programmed terminal queries on the right to receive immediate answers regarding my training, projects, or goals.
Responses are streamed directly in standard monospace console formatting.
I am open to discussions regarding summer internship positions, junior data analyst opportunities, or AI/ML collaborations. Reach out via email, social links, or submit a message through the dashboard.
Noida, Uttar Pradesh, India