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I will build a customer churn prediction machine learning model
Python Developer AI Workflow Specialist
I develop data-driven solutions and efficient Python scripts to assist businesses with automation.
With my solid computer science and algorithms background, my area of expertise lies in Machine Learn...
Informazioni su questo servizio
Development of a machine learning model in Python designed to calculate and predict customer churn rates. The project involves exploratory data analysis (EDA), feature engineering, and training classification algorithms to identify behavioral patterns. It is designed to flag users at risk of canceling a service and enable companies to make proactive retention decisions.
Linguaggio di programmazione:
Python
•
SQL
•
Colab
Framework:
Scikit-learn
•
keras
•
Panda
API:
Altro
Strumenti:
Quaderno jupyter
•
tensorflow
•
Excel
•
Colab
FAQ
How can a Churn Prediction model help my business?
It allows you to identify "at-risk" customers before they leave. By knowing who is likely to cancel, you can launch targeted retention campaigns, save on customer acquisition costs, and stabilize your monthly recurring revenue (MRR).
Is my company data safe with you?
Absolutely. Data privacy is my top priority. I am happy to sign an NDA if required. Once the project is completed and delivered, I delete your dataset from my local environment unless you request otherwise for future maintenance.
How accurate will the churn predictions be?
Accuracy depends on the quality and volume of your historical data. During the process, I provide detailed evaluation metrics (Accuracy, Precision, Recall, and F1-Score) so you can understand exactly how reliable the model's "risk alerts" are before you deploy them.
What kind of data do I need to provide?
Typically, I need historical customer behavior data (subscription dates, usage frequency, last login, support tickets, payment history). I can work with CSV, Excel, or SQL exports. If your data is unorganized, I include a data cleaning phase using Pandas to get it ready.
How do I get the predictions once the model is ready?
Depending on your package, I can deliver a Jupyter Notebook with the final report, or a fully functional Flask API. With the API, your existing software can "ask" the model for a risk score for any specific customer in real-time.
