DARJYO/SardinePredictor

This application predicts the presence of sardines off the coast of Durban, KZN, South Africa during July. # SardinePredictor This application predicts the presence of sardines off the coast of Durban, KZN, South Africa during July. Lets predict the presence of sardines off the coast of Durban, KwaZulu-Natal, South Africa during July. To do this, I'll need to consider environmental factors such as sea temperature, salinity, and historical sardine run data. Then, I'll create a Tkinter front end to make it user-friendly. ### Step 1: Setting Up the Environment First, make sure you have Python installed along with the necessary libraries: ``` pip install pandas numpy scikit-learn tkinter ``` ### Step 2: Creating the Sardine Prediction Model I'll start by creating a simple model. This is a mock-up, assuming DARJYO has historical data that includes features like sea temperature, salinity, and sardine presence. ### Step 3: Creating the Tkinter Front End Next, I will create a Tkinter interface to allow users to input sea temperature and salinity, and then use the model to predict sardine presence. ### Step 4: Running the Application Run the Python script to start the Tkinter application. You will see a window where you can input the sea temperature and salinity. When you click the "Predict" button, it will display a message box with the prediction result. These steps covers the basic structure and workflow. In a real-world scenario, you'll need a robust dataset and potentially more sophisticated preprocessing and feature engineering to achieve high accuracy. For now, this gives a starting point to build and expand upon. ##### Darshani Persadh