All projects.
A focused archive of analytics, campaign strategy, and growth experiments built around measurable business outcomes.
Bank Customer Churn Prediction System
Machine Learning
Model & Business Signals
20.4%Overall churn rate
Roughly 1 in 5 customers left the bank.
99.8%Model accuracy
Random Forest performance reported in the project.
GitHubSource files
Notebook, Streamlit app, model artifacts, README, and training script are linked from the source repository.
Challenge
Identify high-risk bank customers early enough for retention teams to intervene before churn happens.
Key Result
99.8%
Reported model accuracy using a Random Forest classifier, with complaint behavior emerging as the dominant risk signal.
Approach
Built an end-to-end Python workflow with EDA, feature engineering, model training, saved preprocessing artifacts, and a Streamlit prediction UI.
Dashboard KOL
Marketing Dashboard
Live Looker Studio Dashboard
Embedded KOL dashboard built in Looker Studio.
Challenge
Centralize KOL campaign monitoring in a dashboard that is easy to review and share.
Output
Live
Looker Studio report embedded directly into the portfolio as an interactive dashboard.
Approach
Use Looker Studio as the reporting layer for campaign performance tracking, with portfolio access through iframe embed.