Supervised • Unsupervised learning • Deep Learning • Neural Networks • Regression Analysis • Generative AI & NLP
Predictive Modeling • Model Evaluation • Probability & Statistics • Feature Engineering • Statistical Inference & Methods
Data Cleaning • Preparation > Visualization > Reporting • Exploratory Data Analysis (EDA) & dashboard dev (Power BI, Tableau)
Statistical Analysis & Hypothesis Testing • Business Intelligence (BI) • data storytelling • SAS
I design and implement AI-powered systems that enhance decision-making and automate complex processes.
My focus is on applying AI in practical business contexts — from intelligent chatbots to cognitive analytics.
I collect, clean, and analyze data to uncover trends, patterns, and actionable insights that support business strategy.
I also design interactive dashboards that bring data to life and empower data-driven decisions.
I build, train, and evaluate machine learning models that predict outcomes, detect anomalies, and optimize performance.
My work combines data science techniques with strong mathematical foundations to develop reliable and scalable ML solutions.
I focus on protecting data, applications, and cloud environments by implementing proactive security controls and identity management solutions.
My approach blends technical defense with analytical insight to detect, respond to, and mitigate threats.
A modern, interactive AI chatbot built with Streamlit that supports multiple AI models via OpenRouter API.
Technologies Used: Python, OpenRouterAPI, Streamlit
Highlights :Real-time typing effect for AI responses, Message history persistence, Responsive design.
Link: GitHub Repository
A machine learning-powered web app for predicting diseases from symptoms, with explainable results and prescription suggestions. Built for rapid prototyping, research, and real-world deployment using Streamlit.
Technologies Used: Python, Pandas, Scikit-learn, Streamlit, Pickle
Highlights:
Predicts diseases based on user-input symptoms
Uses Random Forest, SVM, and Naive Bayes models (ensemble)
Link: GitHub Repository
Built an interactive dashboard to identify churn patterns and support customer retention strategies.
Technologies Used: Power BI, Excel, DAX, Power Query
Highlights:
Data visualization for the data analysis (DAX) was done in Microsoft Power BI Desktop
Link: GitHub Repository
Developed a model to identify phishing websites by analyzing features and applying machine learning algorithms.
Focused on feature selection, data preprocessing, and model accuracy.
Technologies Used: Python, Pandas, NumPy, Gradient Boosting Algorithm
Highlights :Achieved high accuracy in detecting phishing URLs.
Data-driven approach with feature engineering.
Analyzed app market trends and predicted app success metrics using data analytics and machine learning.
Analytical and results-driven AI & Machine Learning Engineer with a strong background in Data Analytics, Security and cloud-based model deployment. Certified by Microsoft, IBM and Google in AI, Machine Learning and Data Analytics. Skilled in building predictive models, developing neural networks, and deploying AI solutions using Python, Azure and Streamlit to drive intelligent automation and data-driven decision-making. My goal is to bridge the gap between analytics, artificial intelligence, and cybersecurity to help organizations make smarter, safer, and scalable decisions.
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