• 🔭 Currently building end-to-end analytics projects in GST fraud detection and railway operations data
• 🤝 Looking to collaborate on data analysis, business intelligence, SQL, or dashboard projects
• 🌱 Currently learning statistical modeling, data science, and business analytics
• 💬 Ask me about SQL window functions, anomaly detection, Power BI, Tableau, Python for data analysis
• ⚡ Fun fact: I built a 3-layer GST fraud detection system that flags 15% of 50,000 invoices — using pure SQL and statistics, no ML

Languages

Data & Analytics Libraries

Databases

BI & Visualization

Tools

End-to-end analytics system detecting fraudulent GST invoice patterns across 50,000+ records

• 3-layer detection pipeline — Rule-based validation → Statistical anomaly detection → Weighted risk scoring
• SQL window functions for Z-score analysis, rolling average spike detection, and IQR outlier detection
• Flags 7,512 invoices (15%) as suspicious, identifies 35 HIGH-risk vendors out of 210
• Interactive Tableau dashboard — 🔗 View Live
• Stack: Python · PostgreSQL · SQL · Pandas · Tableau

ML-powered personal finance analytics tool with category prediction, budget monitoring, and spending forecasting

• Naive Bayes + TF-IDF classifier predicts expense categories from transaction descriptions
• Month-over-month spending forecasting based on historical patterns to support budget planning
• Visual analytics dashboard — category breakdown (pie chart) and spending trend (line chart)
• Budget threshold monitoring with automated alerts when limits are exceeded
• Stack: Python · Scikit-learn · SQLite · Pandas

Automated data pipeline simulating real-time railway delays with risk classification

• APScheduler runs the pipeline every 5 minutes automatically
• Classifies delays into HIGH / MEDIUM / LOW risk tiers per train
• Dual storage — rolling CSV (last 10 records/train) + optional PostgreSQL
• Interactive Power BI dashboard with delay trends and risk distribution
• Stack: Python · Pandas · APScheduler · PostgreSQL · SQLAlchemy · Power BI

End-to-end business analytics on 4 years of retail sales data — from raw CSV to decision-ready Power BI dashboard

• Analyzed $2.30M in sales (2014–2017) with ~52% YoY growth across regions and categories
• Identified loss-making products (Tables & Bookcases) despite high sales volume using SQL queries
• Built KPI dashboard tracking Sales, Profit, Margin, and Quantity with region & category filters
• West region drives 31.58% of revenue — Technology leads in both sales and profitability
• Stack: Python · Pandas · SQLite · Power BI

⭐️ From Saksham3124