You will get End-to-End Customer Churn Prediction System Using Machine Learning
Project details
You will get a reliable customer churn prediction system built with proven machine learning techniques and a strong focus on business impact, not just model accuracy.
I specialize in traditional machine learning for tabular data, where careful feature engineering, proper validation, and decision-aware modeling usually outperform overly complex approaches. My process emphasizes clean data handling, leakage-safe validation, and scenario analysis so predictions can be used confidently in real decisions.
The final deliverable is not just a model, but a clear understanding of why customers churn, how confident the predictions are, and how to act on them. You’ll receive interpretable results, practical recommendations, and reproducible outputs that can be integrated into your existing workflows.
This approach is ideal for SaaS, subscription-based products, and data-driven teams that want actionable churn insights they can trust.
I specialize in traditional machine learning for tabular data, where careful feature engineering, proper validation, and decision-aware modeling usually outperform overly complex approaches. My process emphasizes clean data handling, leakage-safe validation, and scenario analysis so predictions can be used confidently in real decisions.
The final deliverable is not just a model, but a clear understanding of why customers churn, how confident the predictions are, and how to act on them. You’ll receive interpretable results, practical recommendations, and reproducible outputs that can be integrated into your existing workflows.
This approach is ideal for SaaS, subscription-based products, and data-driven teams that want actionable churn insights they can trust.
Machine Learning Tools
NumPy, Python, PyTorch, scikit-learn, SciPy, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$100
|
Standard
$200
|
Advanced
$500
|
|---|---|---|---|
| Delivery Time | 5 days | 7 days | 13 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 2 |
Number of Graphs/Charts | 3 | 3 | 6 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code | - |
Frequently asked questions
About Pablo
Data Scientist
Montevideo, Uruguay - 4:12 pm local time
I can help you with:
• Building and training classification, regression, or clustering models
• Designing full ML pipelines (data cleaning, feature engineering, evaluation)
• Computer Vision tasks such as detection, segmentation, or tracking
• Geospatial analysis using GeoPandas, QGIS, and satellite imagery
• NLP applications: text classification, summarization, and automation
• Data extraction, transformation, and analysis
Core technologies: Python, NumPy, Pandas, Scikit-Learn, PyTorch, TensorFlow, XGBoost, Optuna, GeoPandas, QGIS.
My approach is straightforward: understand the problem, design the most effective solution, and deliver clean, reproducible work.
Steps for completing your project
After purchasing the project, send requirements so Pablo can start the project.
Delivery time starts when Pablo receives requirements from you.
Pablo works on your project following the steps below.
Revisions may occur after the delivery date.
Data review & churn alignment
Review the dataset, validate churn definition, check missing values/outliers, and confirm what features can be used without data leakage.
Feature engineering & baseline model
Build a baseline model and create key features (tenure, usage, recency/frequency, aggregates). Establish a clear benchmark with proper validation metrics.