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Trading Analytics Platform is a Python-based web application for backtesting trading strategies, analyzing historical market data, and visualizing performance metrics. The platform enables traders to compare strategies, generate reports, and optimize trading performance through interactive dashboards.
The network for creativity
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Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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Project: Velocity SaaS – Revenue Turnaround Analysis
Type: Independent portfolio project
Dataset: Synthetic B2B SaaS, 2,000 accounts / 2.2M rows, benchmarked against ProfitWell/OpenView
What I did:
MRR waterfall analysis, churn segmentation by plan/industry/channel
Usage decay / silent churn risk modeling
Logistic regression churn predictor: 98% recall, 70% precision, 0.82 F1
Built executive Looker Studio dashboard + Streamlit churn predictor
Outcome: $411K silent MRR at risk identified, CS-ready prioritized account list
Stack: SQL, DuckDB, Python, Pandas, Scikit-learn, Looker Studio, Streamlit
Live app: velocity-saas-churn-predictor.streamlit.app
Repo: github.com/SURAJRAJPUT2006
amazing
Halo CME Detection & Space Weather Prediction System
Title: ML-Based Early Warning System for Solar Storms (Smart India Hackathon Winner)
Problem: Geomagnetic storms and Coronal Mass Ejections (CMEs) threaten satellite infrastructure, but predicting their arrival and severity is a hard time-series forecasting problem with multi-scale temporal patterns.
My approach: I developed supervised time-series forecasting models (LSTM, GRU) trained on space-weather data, applying lag and rolling-window feature engineering to capture temporal dynamics across multiple scales. I also built a real-time ML-based alerting pipeline for risk assessment and forecasted key geomagnetic indices (Kp, Ap, Dst, Sunspot Number) 7 days ahead.
Tools used: Python, PyTorch/TensorFlow, LSTM, GRU, time-series feature engineering, MLflow
Result: Achieved a 7% improvement in CME/storm prediction accuracy and a 6% improvement in arrival-time prediction. Won Smart India Hackathon 2025 (Ministry of Education's Innovation Cell, Government of India), and the work was published as corresponding author in the Journal of Science, Computing and Engineering Research (JSCER), 2026.
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