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Fiverr Pro
English
$
USD
I will build machine learning churn prediction model to increase customer retention
Awais Naeem
About this gig
Losing recurring customers? Predict churn before it happens with custom Machine Learning!
I build end-to-end predictive churn models in Python using advanced algorithms (XGBoost, LightGBM, Random Forest) to identify at-risk customers and maximize retention.
What I Deliver:
- Data Preprocessing & EDA: Feature engineering, missing value handling, and class imbalance mitigation (SMOTE).
- Predictive Modeling: High-accuracy ML models optimized for precision, recall, and ROC-AUC.
- SHAP Explainability: Understand the exact drivers behind customer churn risk.
- Production Deployment: Wrapping the trained model into a FastAPI REST API for seamless app integration.
Tech Stack: Python, Scikit-Learn, XGBoost, LightGBM, Pandas, NumPy, SHAP, FastAPI, Docker.
Why Choose Me?
- Clean, reproducible Jupyter notebooks & modular code.
- Focus on business ROI and actionable customer risk scoring.
- 100% data privacy and NDA compliance.
Message me with your dataset structure before placing an order!
Get to know Awais Naeem
Awais Naeem
AI and Machine Learning Engineer
- FromPakistan
- Member sinceOct 2024
- Avg. response time1 hour
- Last delivery8 months
Languages
Urdu, English
I am an AI & Machine Learning Engineer with 7+ years of experience in real-world automation, from AI agents to backend systems
I help when teams are stuck in manual work, tools don’t integrate or AI feels unclear.
I have built:
• AI agents using LLMs (OpenAI, LLaMA, RAG pipelines)
• Automation Agents using n8n, OpenClaw, make.com
• Workflow automations (APIs, Python, backend systems)
• Chatbots that understand context, not just scripts
• Data + AI systems that turn raw data into decisions
I build systems that save time, cut costs and actually get used. Reach out if you're exploring AI.
My Portfolio
FAQ
1. What data format do I need to provide?
CSV, Excel, SQL database dumps, or Parquet files containing historical customer interactions, transaction history, and churn labels.
2. How do you handle imbalanced churn datasets?
I use advanced resampling techniques like SMOTE/ADASYN along with stratified splitting and cost-sensitive learning to optimize evaluation metrics (ROC-AUC, Precision-Recall curve).
3. Will I understand why a specific customer is predicted to churn?
Yes! I include SHAP (SHapley Additive exPlanations) values to highlight the top feature importances driving churn risk scores.
4. Can you deploy this model to my cloud infrastructure?
Absolutely. The Premium package covers containerizing the model via Docker and deploying it as a REST endpoint on AWS, GCP, or Azure.
5. How is data privacy handled?
Your data remains strictly confidential and is used solely for training your custom model before secure deletion upon order delivery.
