Suraj Rajput
SaaS Revenue Analyst | Churn & MRR for B2B
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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
(http://velocity-saas-churn-predictor.streamlit.app)Repo: github.com/SURAJRAJPUT2006
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