AI Soccer Match Prediction Website
Budget: $250 – $750 USD
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.
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.