Sure, here's a rough TO DO list for implementing a Support Vector Machine (SVM) from scratch:

Data Preprocessing: Collect the data, normalize or scale the features, and split the dataset into training and testing sets.

Define the Kernel Function: Select a kernel function that best fits the data. Common kernel functions include linear, polynomial, radial basis function (RBF), and sigmoid.

Initialize the Parameters: Set the values of hyperparameters like regularization parameter (C), learning rate (alpha), and number of iterations (epochs).

Train the Model: Use the training set to find the optimal decision boundary that maximizes the margin between the two classes. You can use optimization techniques like gradient descent or quadratic programming to solve the optimization problem.

Test the Model: Evaluate the performance of the trained SVM model on the testing set. Use metrics like accuracy, precision, recall, and F1-score to measure the performance.

Tune the Parameters: Try different values of hyperparameters to improve the performance of the SVM model. You can use techniques like grid search or random search to find the optimal values.

Validate the Model: Finally, validate the SVM model on a new dataset to ensure that it generalizes well to unseen data.

Remember that implementing an SVM from scratch can be a challenging task, especially if you're new to machine learning. It's a good idea to consult with your instructor or refer to online resources for guidance.
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