ML + WEB PROJECTFULL-STACKAI / ML
Cricket Player Performance Predictor
Machine-learning cricket performance predictor built around a self-created IPL dataset and a web interface.
ReactFlaskScikit-learnRandom Forest
SYSTEM SIGNAL
dataset creation + model integration + frontend/backend delivery
INTERACTIVE SYSTEM MAP
Inspect the architecture signal.
Compact map of the supplied web + ML stack and self-built data source.
NODE INSPECTOR
EXPERIENCE
React
Frontend layer for the predictor.
CLICK ANOTHER NODE TO TRACE THE SYSTEM
01
ENGINEERING NOTE
Problem
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Build a player-performance predictor using a self-built IPL dataset.
02
ENGINEERING NOTE
Constraints
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Dataset was self-built from IPL data.
03
ENGINEERING NOTE
Decisions / trade-offs
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Use Random Forest with a React frontend and Flask backend.
04
ENGINEERING NOTE
What broke / changed
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No failure, migration, or benchmark detail was supplied in the project history.