The Fish Weight Prediction Model employs machine learning algorithms to predict the weight of a fish based on several features. The primary features used in the model include:
Species: The type of fish, which provides critical information on the likely weight range and growth patterns.
Weight: Although this is the target variable, it is also used in initial analysis to understand correlations.
Length1: The vertical length of the fish.
Length2: The diagonal length of the fish.
Length3: The cross-sectional length of the fish.
Height: The height of the fish, typically measured at the tallest point.
Width: The width of the fish, usually measured at the widest point.
Machine learning algorithms such as Linear Regression, Decision Trees, Random Forest, and Support Vector Machines (SVM) are trained on a dataset containing these features. The model learns the relationships and patterns within the data to make accurate predictions about a fish's weight based on the given inputs.
Aquaculture Management: Accurate weight prediction helps in the effective management of fish farms, ensuring optimal feeding and growth conditions.
Market Pricing: Knowing the weight of fish in advance can assist in determining market prices and ensuring fair trade practices.
Conservation Efforts: Helps in monitoring fish populations and their health, contributing to conservation efforts by tracking growth rates and detecting potential issues.
Research: Facilitates biological and ecological research by providing reliable data on fish growth patterns and species characteristics.
Efficiency: Reduces the need for manual weighing, saving time and reducing stress on the fish, leading to better overall health and welfare.