You will get a customer churn prediction model in Python
Project details
You will get a production-ready customer churn prediction model built in Python with full explainability using SHAP.
I have 6+ years of experience in telecom analytics where I built churn models used in real business decisions. My models have achieved up to 95% accuracy on telecom datasets.
What makes my service different:
• I use SHAP explainability, so you understand WHY customers churn, not just which ones will churn.
• Clean, well-documented Jupyter notebook you can present to your team or stakeholders.
• Full EDA showing patterns in your data before modeling.
• Model comparison so you get the best algorithm for your data.
I have built churn models for the telecom and banking sectors using Random Forest, XGBoost, and LSTM models. Every delivery includes source code, documentation, and a clear summary of top churn drivers your business can act on.
I have 6+ years of experience in telecom analytics where I built churn models used in real business decisions. My models have achieved up to 95% accuracy on telecom datasets.
What makes my service different:
• I use SHAP explainability, so you understand WHY customers churn, not just which ones will churn.
• Clean, well-documented Jupyter notebook you can present to your team or stakeholders.
• Full EDA showing patterns in your data before modeling.
• Model comparison so you get the best algorithm for your data.
I have built churn models for the telecom and banking sectors using Random Forest, XGBoost, and LSTM models. Every delivery includes source code, documentation, and a clear summary of top churn drivers your business can act on.
AI Development Type
Deep Learning, Model TuningAI Tools
Keras, MLflow, PyTorch, Sonnet, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$50
|
Standard
$150
|
Advanced
$300
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | - | ||
Detailed Code Comments | - | ||
Knowledge Graph | - | - | - |
Model Documentation | - | ||
Ontology | - | - | - |
Source Code | |||
Taxonomy | - | - | - |
Optional add-ons
You can add these on the next page.
Additional Revision
+$15Frequently asked questions
About Malik Jamal
Data Scientist and ML Engineer - Churn, Fraud Detection, RAG
Rawalpindi, Pakistan - 6:00 pm local time
What I build for clients:
✔ Churn & retention prediction models (95%+ accuracy)
✔ Fraud detection & anomaly detection systems
✔ RAG pipelines using LLMs (Claude API, ChromaDB, Streamlit)
✔ Customer segmentation & clustering (K-Means, DBSCAN)
✔ Power BI dashboards & automated data reports
✔ ML model deployment via Flask APIs on AWS
✔ Explainable AI using SHAP & LIME for business stakeholders
My edge: I don't just build models, I make sure business stakeholders understand the results. I use SHAP/LIME to explain every prediction in plain language, which is rare and highly valued by non-technical clients.
Tech stack:
Python · Scikit-learn · TensorFlow · PyTorch · XGBoost · Pandas
SQL · PostgreSQL · MySQL · Hadoop · AWS · Power BI · Streamlit
Flask · ChromaDB · LangChain · Claude API · Git · CI/CD
Currently, I have completed my MS in Data Science at Bahria University (2026).
If you need a reliable data scientist who delivers clean code, clear documentation, and on-time delivery - let's talk.
Steps for completing your project
After purchasing the project, send requirements so Malik Jamal can start the project.
Delivery time starts when Malik Jamal receives requirements from you.
Malik Jamal works on your project following the steps below.
Revisions may occur after the delivery date.
Data Analysis and EDA
I will analyze your dataset, assess data quality, handle missing values, and create visualizations that show churn patterns across all key features.
Model Building and Training
I will build and compare multiple ML models including Random Forest, XGBoost, and Logistic Regression to find the best performing algorithm for your data.