01
Telecom Churn Prediction
Built classification models to identify at-risk customers and reduce attrition for a telecom operator.
85%
Accuracy
87%
F1-score
Python
Scikit-learn
SMOTE
Tableau
Logistic Regression
Random Forest
Selected work
Models that moved the needle.
Three featured projects where machine learning translated directly into measurable business value.
02
Customer Transaction Prediction
Regression model forecasting transaction volumes — improving demand planning and reducing forecast error.
0.82
R²
15%
Error reduction
Python
Pandas
Scikit-learn
Feature Engineering
03
Customer Segmentation (RFM)
K-Means clustering on RFM features to power targeted campaigns and lift click-through rates.
5
Segments
+15%
CTR uplift
Python
K-Means
RFM Analysis
Power BI
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