Smart Fish Farming Recommendation System using K-Means Algorithm

A pressing problem in fish farming is the shortage of high-quality stock. These fish now can be raised in ponds using hormonal stimulation as a treatment for this problem. The supply of pure fish stock in the required numbers has been guaranteed as a result. The purpose of our project is to help people predict the best type of fish to be cultivated in the habitat depending on the environmental variables and to help people do efficient fish farming or fish culture. Using Data Visualization and Data Processing, Unsupervised Algorithms and analyze the clustering of unlabeled datasets by K Means Clustering Algorithm and Hierarchical Algorithm. Finally, applying Logistic Regression to real-world problems to check the type of fish that will be suitable to cultivate in the habitat according to the input details about the cultivable area. The project aims to improve the ability of fish farmers to monitor, control, and document key factors affecting fish production.