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Overview:

This project aims to predict the age of abalones based on physical measurements using an XGBoost model. The system analyzes features such as shell length, weight, and diameter to predict the age of abalone, a common task in marine biology. The model’s performance is evaluated using RMSE (Root Mean Squared Error) to ensure prediction accuracy.

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Key Features:

1. XGBoost Model:

XGBoost is employed for regression tasks due to its efficiency in handling large datasets and its ability to model complex relationships between features for accurate age prediction.

2. Feature Extraction:

The model utilizes key physical measurements of abalones, including length, diameter, weight, and shell thickness, to predict their age, which is determined by counting the growth rings.

3. RMSE Validation: The model’s accuracy is evaluated using Root Mean Squared Error (RMSE), which measures the difference between predicted and actual abalone ages, ensuring precise predictions.

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Links:

• GitHub