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I will maximum SEO optimization targeting high traffic business searches
India
AI Engineer Agentic RAG Chatbots Full Stack
About this Gig
TRANSFORM YOUR BUSINESS RETENTION WITH PREDICTIVE REVENUE INTEL
Customer churn kills growth. If you wait for cancellations, you are too late. You need an automated Machine Learning pipeline that predicts exactly which users are at riskweeks before they leave.
As a seasoned Software Developer, I engineer end-to-end predictive analytics tailored to your data structure (Telecom, SaaS, FinTech, E-commerce). I deliver enterprise-ready pipelines that turn raw user telemetry into actionable retention strategies.
WHAT I BUILD FOR YOU:
* Advanced Data Engineering: Pipelines handling missing fields and high-cardinality variables.
* Imbalance Correction (SMOTE): Synthetic oversampling to handle sparse churn datasets without false-positive bias.
* Multi-Algorithmic Evaluation: Training across XGBoost, LightGBM, and Random Forests alongside Deep Learning networks.
* Hyperparameter Tuning: Bayesian/Grid optimization to maximize Precision, Recall, and F1-Scores.
* Dashboard Deployment: Interactive web apps displaying dynamic risk scorecards and feature maps.
Expertise:
Classification
•
Churn
Programming language:
Python
Frameworks:
Scikit-learn
•
Keras
•
PyTorch
•
Panda
APIs:
Microsoft Computer Vision AI
Tools:
Jupyter Notebook
•
TensorFlow
•
Excel
FAQ
What kind of data do I need to provide for you to get started?
For optimal model precision, a historical dataset (CSV, Excel, or SQL dump) containing customer behavior attributes is required. This typically includes features like subscription length, usage frequency, contract type and a historical flag indicating whether the customer stayed or not.
Why do you emphasize SMOTE oversampling in your pipeline development?
In almost all real industries, the number of users who churn is much smaller than the number of users who stay (class imbalance)
