I’m someone who is deeply curious about how data and technology can solve real-world problems. Over time, I’ve developed a strong interest in understanding patterns, drawing insights, and building smart solutions using data. Whether it’s analyzing trends, exploring new tools, or experimenting with AI models, I genuinely enjoy the process of learning and creating. I'm always excited to take on new challenges and grow in the field of data and AI.
View ResumeMinibeast is a Python-based intelligent scanning tool that automates vulnerability detection in web applications by orchestrating system-level tools and analyzing security threats through rule-based logic and smart execution flows.
Sizylle is a voice-activated virtual assistant built using core Python. It can follow spoken commands to perform basic tasks like playing music, opening the camera, checking your location, or answering simple questions like “What is corona?”. While it's currently based on straightforward Python scripting, Sizylle lays the foundation for a smarter, more interactive assistant—future-ready for machine learning integration.
I-BEAM is a smart IoT-based environmental monitoring system designed to observe and report on air quality, temperature, humidity, and gas concentrations. Built with the vision of integrating intelligent decision-making in future updates, I-Beam serves as the foundation for AI-powered environmental safety and awareness. It provides real-time insights that can be used in homes, schools, offices, or industrial settings to promote a healthier and safer living environment.
This COVID-19 India Tracker Dashboard provides a real-time overview of the pandemic across the country, displaying key metrics like confirmed cases, recoveries, deaths, and active cases. It features interactive time-series visualizations and statewise breakdowns, helping users track trends, compare regional data, and understand the overall impact. Built using Python and visualization libraries, it offers a clean and informative interface for public awareness and analysis.
This project aimed to develop a classification model to detect Parkinson’s disease based on visual patterns in spirals, waveforms, and handwriting obtained during clinical assessments. Although pen pressure is a key factor in diagnosing the disease, this study explored whether visual characteristics alone could effectively indicate the presence and severity of Parkinson’s.
Development and tools: Python, R, SQL, Version Control, Shell Scripting, Linux, Power BI, Tableau
Feature engineering: Outlier Detection (IQR, Z-score, Percentile), Encoding (One-Hot, Label, Ordinal), Handling Imbalanced Data (Under/Oversampling, SMOTE), Feature Scaling (Standardization, Normalization), Imputation, EDA
Libraries and frameworks: Numpy, Pandas, Seaborn, Matplotlib, scikit-learn, TensorFlow, Keras, OpenCV
Computer vision: Image Segmentation, Image Classification, Object Detection, Feature Extraction, Biomedical Image Analysis, Deep Learning for Image Processing
Statistical techniques: regression analysis, time series analysis, optimization, simulation, Markov chain Monte Carlo, stochastic models, Bayesian inference, hypothesis testing, cluster analysis, experimental design, multivariate analysis, random forests, decision trees, neural networks, reinforcement learning
Other skills: Database Management, Data Visualization, LLMs, Gen AI, Data pipelines, MLops, model deployment, Interdisciplinary Research
theshrikantgade@gmail.com
+91-7987976050