I will customer churn analysis and predictive modeling in python
I turn data into decisions, churn, forecasting, customer analytics
About this Gig
Struggling to understand why customers are leaving or which ones are worth saving?
I help businesses turn raw customer data into clear, actionable retention decisions using Python and statistical modeling.
What I offer:
- Exploratory data analysis & data cleaning
- Customer churn analysis identify who's at risk and why
- Predictive modeling (XGBoost, Logistic Regression) with accuracy metrics
- Survival analysis estimate when customers are likely to churn
- Customer segmentation (RFM) for targeted retention strategy
- Clear, business-ready reports and recommendations
Why work with me:
I'm a Statistics student with hands-on, portfolio-proven experience including a churn prediction + retention budget model that identified 593 priority customers with an expected ROI of 1.02x7.08x. I focus on reproducible analysis and honest reporting, not just a number that looks good.
Tools: Python (pandas, scikit-learn, XGBoost, lifelines), SQL, SPSS, Streamlit.
Send me a message with a short description of your data or business question I usually respond within a few hours and I'm happy to clarify scope before you order.
My Portfolio
FAQ
What data format do you need to get started?
CSV or Excel works best. Just make sure customer records include relevant fields (e.g. tenure, contract type, usage, churn status). If you're not sure, send me a sample and I'll confirm before you order.
Will I get the Python code along with the results?
Yes — for Standard and Premium packages, you'll receive the full Python notebook (data cleaning, churn analysis, and predictive model) so you can reproduce or extend the analysis yourself.
Can you explain the churn analysis results in simple terms?
bsolutely. Every report includes a plain-language summary of key findings and actionable recommendations — not just technical metrics — so you can use it directly for business decisions.

