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Predicting abalone age (ring count) using a Random Forest Regressor. The model is deployed via a FastAPI backend for efficient and scalable predictions.

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Name		Name	Last commit message	Last commit date
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Abalone_MD_FastApi.ipynb		Abalone_MD_FastApi.ipynb
README.md		README.md
abalone_model.py		abalone_model.py
api_abalone.py		api_abalone.py
hasil output fastapi.docx		hasil output fastapi.docx
model.pkl		model.pkl
train.csv		train.csv

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🐚 Abalone Age Prediction API

This project predicts the age of an abalone (measured by ring count) based on physical attributes using a Random Forest Regressor.

🔧 Tools & Technologies

🧠 Random Forest Regressor for regression modeling
⚡ FastAPI for building a fast, lightweight API
📊 Abalone Dataset for model training and evaluation

🚀 Features

Predict abalone age using attributes like length, diameter, weight, etc.
Deployed via FastAPI for quick and scalable access
Built for integration with frontend or other applications

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Predicting abalone age (ring count) using a Random Forest Regressor. The model is deployed via a FastAPI backend for efficient and scalable predictions.

random-forest-regressor fastapi abalone-dataset

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