Build a Customer Churn Prediction Model with API Endpoint

We're a small SaaS company and we have a CSV dataset (~10,000 rows) of customer activity data including features like subscription length, login frequency, support tickets filed, monthly spend, and whether the customer churned (binary label). We need someone to build a simple but effective churn prediction model and expose it via a lightweight API so our internal tools can call it. Scope of work: - Perform basic exploratory data analysis (EDA) and generate a few key visualizations (churn distribution, feature correlations, top predictive features) - Clean and preprocess the data (handle missing values, encode categoricals, scale features) - Train and evaluate at least 2-3 classification models (e.g., Logistic Regression, Random Forest, XGBoost) with appropriate metrics (accuracy, precision, recall, F1, AUC-ROC) - Select the best model and save it as a serialized file (pickle or joblib) - Build a simple FastAPI endpoint that accepts customer features as JSON input and returns a churn probability score - Provide a Jupyter notebook with the full EDA + modeling workflow, plus the FastAPI app code - Include a brief README with setup instructions Deliverables: GitHub repo or zip with notebook, model file, API code, requirements.txt, and README. We're looking for someone who can do this quickly and cleanly — no over-engineering needed, just solid ML fundamentals and a working API. Ideally completed within a couple of days.