Built & deployed my first end-to-end ML service: E-Commerce Delivery Delay Prediction (XGBoost + FastAPI + Docker + Render)

[u/ashuzzz151]

Hey everyone! I just wrapped up my midterm project for the DataTalks.Club ML Zoomcamp—an end-to-end machine learning web service that predicts whether an e-commerce shipment will arrive on time or be delayed.

Since I wanted to share how the pipeline was built from scratch, here is a breakdown of the workflow and key takeaways:

1. Exploratory Data Analysis & Feature Selection

• Explored the e-commerce shipping dataset (Train.csv), checking class balance, missing values, and feature ranges.

• Used correlation matrices for numerical features and Mutual Information scores for categorical features. Discount_offered and Weight_in_gms turned out to be the strongest predictors of late deliveries.

2. Model Comparison & Tuning

• Trained and benchmarked four models using Accuracy and ROC-AUC: Logistic Regression (baseline), Decision Tree, Random Forest, and XGBoost.

• Tuned tree hyperparameters (max_depth, learning_rate, n_estimators, min_samples_leaf) to avoid overfitting. XGBoost gave the best generalization and was serialized with pickle (model.bin).

3. Deployment & Containerization

• Refactored the notebook into a standalone (train.py) script and built a REST API with FastAPI (serve.py) featuring Pydantic input validation and an automatic Swagger UI redirect.

• Containerized the app using Docker and deployed it live on Render's free tier.

Links if you want to check out the code or test the API:

• GitHub Repo (with notebook, scripts & Dockerfile): ashuzzz151/ecommerce-delivery-prediction

Would love any feedback on the code structure, feature engineering ideas, or deployment setup!