You will get a churn prediction model + retention insights (Telco/SaaS)
Project details
You will get a production‑ready churn prediction solution for subscription or telco data. The delivery includes a clean EDA overview, a trained classification model (LogReg/RF/XGBoost), clear evaluation (ROC‑AUC, F1, Confusion Matrix), and an easy inference script or sheet to score new customers. The work focuses on actionable retention insights, reproducible code, and concise executive reporting.
Machine Learning Tools
ChatGPT, MLflow, NumPy, pandas, Python, scikit-learn, XGBoostWhat's included
| Service Tiers |
Starter
$5
|
Standard
$15
|
Advanced
$20
|
|---|---|---|---|
| Delivery Time | 2 days | 5 days | 5 days |
Number of Revisions | 0 | 3 | 2 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | |||
Source Code | - | - | - |
About Salma
Data Scientist | Python, SQL, ML
Munuf, Egypt - 6:34 pm local time
I am professionally trained through the IBM Data Science program, specializing in Python (Pandas, NumPy, Scikit-learn), SQL, and advanced Machine Learning (classification, regression, clustering).
My expertise covers the full data science workflow:
Data Analysis & EDA: Uncovering hidden patterns through thorough exploratory data analysis.
Data Visualization: Creating clear and impactful visualizations (Matplotlib, Seaborn) to communicate complex findings.
Machine Learning: Building and deploying predictive models. I successfully delivered a capstone project to predict customer churn, developing a high-accuracy classification model using a telecom dataset.
Modern Tools: I am proficient in MLOps practices (MLflow, Hugging Face) and skilled in Prompt Engineering for LLM-assisted analytics.
I am passionate about helping businesses translate raw data into actionable insights and strategic decisions. Let's connect to discuss how I can help you achieve your goals.
Steps for completing your project
After purchasing the project, send requirements so Salma can start the project.
Delivery time starts when Salma receives requirements from you.
Salma works on your project following the steps below.
Revisions may occur after the delivery date.
Inputs review
validate dataset schema, target definition, and goals; confirm scope and timeline inside the workroom
EDA and data prep
handle missing values, encode categoricals, scale numerics, split train/test, and document what was done in a concise Notebook following a clear delivery style.