🎯 Aspiring Data Analyst focused on Financial & Business Data Analytics

• 🔍 Passionate about uncovering patterns in complex datasets
• 🤖 Building ML-powered systems with Explainable AI
• 📊 Love creating dashboards that tell compelling data stories
• 🧠 Skilled in SQL, Python, Machine Learning, and Tableau
• 🌱 Currently sharpening skills in Advanced ML and BI Tools
• 📍 Based in India

End-to-end real-time fraud detection pipeline with SHAP explainability & live Streamlit dashboard

• 🔧 Tech: Python, LightGBM, XGBoost, SHAP, SMOTE, Streamlit, Plotly, Optuna
• 💡 Handles severe class imbalance using SMOTE; explains every prediction in plain English via SHAP
• 📊 Multi-page live dashboard deployed on Streamlit Cloud
• 🎯 Models compared: LightGBM vs XGBoost vs Isolation Forest with threshold optimization
• 🔗 Live Dashboard: Click Here

ML pipeline to predict customer churn and segment by risk tier

• 🔧 Tech: Python, Jupyter Notebook, Scikit-learn, Pandas, Matplotlib, Seaborn
• 💡 Segments customers into Critical Risk, Suspicious, and Clear tiers
• 📊 Comprehensive model comparison with ROC-AUC and PR-AUC metrics
• 🎯 Feature engineering + business recommendations for retention strategy

NLP-powered sentiment classification of e-commerce product reviews

• 🔧 Tech: Python, Jupyter Notebook, NLP, Pandas, Matplotlib
• 💡 Classifies customer reviews into Positive, Negative, and Neutral sentiments
• 📊 Visual insights into customer opinion trends across product categories
• 🎯 Business-ready insights for product improvement decisions

End-to-end bank customer churn analysis using SQL + Python + Looker Studio

• 🔧 Tech: SQL (MySQL), Python, Google Colab, Google Looker Studio
• 💡 Identifies churn drivers by geography, gender, age, tenure, balance, and product usage
• 📊 Interactive dashboard with KPI scorecards on Looker Studio
• 🔗 Live Dashboard: Click Here

Complete end-to-end HR attrition analysis using MySQL

• 🔧 Tech: MySQL, MySQL Workbench, GitHub
• 💡 Full pipeline: schema design → data loading → cleaning → basic/intermediate/advanced analysis
• 📊 Advanced SQL: window functions, aggregates, conditional logic, high-risk segment identification
• 🎯 Identifies attrition patterns by department, job role, overtime, income, and tenure

Clinical heart disease data analysis with Tableau dashboards integrated into a Flask web app

• 🔧 Tech: MySQL, Tableau, Flask, Python, HTML, CSS
• 💡 Transforms raw clinical data into interactive visual insights
• 📊 Full-stack integration of analytics into a web application
• 🔗 Live Tableau Dashboard: Click Here
• 🔗 Live Story: Click Here