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$
USD
I will churn prediction model, predictive analytics, customer segmentation using python
About this Gig
Are you losing customers without knowing why?
I build machine learning models for churn prediction, customer behavior analysis, demand forecasting, and customer segmentation using Python, scikit-learn, and XGBoost.
Works for any business with recurring customers SaaS, e-commerce, subscriptions, retail, fintech, telecom.
- Churn prediction model know who's leaving before they leave
- Customer risk scoring ranked from highest to lowest churn risk
- Demand forecasting predict sales, orders, or usage trends
- Customer segmentation group by behavior and lifetime value
- Visual report charts, feature importance, risk breakdown
- Clean Python code documented and reusable by your team
What I need: a CSV or Excel file with your customer data. Transactions, usage logs, dates anything you have.
Industries: SaaS, e-commerce, telecom, retail, fintech, healthcare, subscriptions.
BSc in Data Science. I deliver decisions, not just models.
Message me before ordering I'll confirm your data is a good fit.
Programming language:
Python
•
SQL
Frameworks:
Scikit-learn
•
Panda
Tools:
Jupyter Notebook
•
Excel
•
MLflow
•
Colab
•
Azure ML Studio
FAQ
What data do you need to build the model?
A CSV or Excel file with customer records works perfectly. Useful columns include signup date, last activity date, purchase history, subscription status, usage logs, or support tickets. Even a basic dataset with 500+ rows is enough to build a meaningful churn prediction or demand forecasting model.
Which industries does this work for?
Any business with recurring customers or repeat transactions. Most common: SaaS platforms, e-commerce stores, subscription services, telecom companies, fintech apps, retail businesses, and healthcare platforms. The machine learning model is trained on your specific data, so predictions are specific
What if my dataset is small or messy?
Small datasets (300–1000 rows) are fine; I use techniques like SMOTE for class balancing and cross-validation to ensure the model is reliable. For messy data I handle missing values, outlier treatment, and feature engineering as part of the project. Send me your data first and I'll give you a review
Can you also do sales forecasting or revenue prediction?
Yes,the Premium package includes a demand forecasting or revenue prediction model alongside the churn model. I use time-series models (ARIMA, Prophet) and regression-based approaches (XGBoost, LightGBM) depending on your data structure. This is ideal for businesses that want retention insight & pred
