I’m creating an online destination where soccer enthusiasts can check reliable, data-driven match predictions before every kick-off. The engine behind the site will combine large sets of historical match data with up-to-the-minute season statistics, feeding them into a machine-learning model that outputs win-draw-loss probabilities and scoreline estimates. What I need from you is the full build: model selection or custom architecture, data-pipeline automation that fetches and refreshes both the historical archive and the current-season feeds, and a clean web interface that presents predictions in an easy, fan-friendly format. Odds-style percentages, form graphs, and key player influences should surface prominently so visitors can grasp the rationale behind each forecast without reading a research paper. Front-end can be React, Vue, or another modern framework you’re comfortable with, as long as it’s lightweight and mobile responsive. Python (TensorFlow, PyTorch, or scikit-learn) is fine for the back-end model; Node or Django for the API that serves predictions—your choice so long as deployment to a standard cloud stack (AWS, GCP, or similar) is straightforward. Deliverables • End-to-end trained model with reproducible training script • Automated data-ingestion jobs connected to the two specified data sources • REST or GraphQL API returning prediction JSON • Responsive UI styled for casual fans, deployed to a live URL • Brief readme and walkthrough video so I can maintain and iterate Acceptance criteria: model retrains successfully from scratch, API returns predictions within one second, and the live site renders correctly on desktop and mobile. If this sounds like your field, let’s kick off.