Sure, here's a rough TO DO list for implementing a SVM from scratch:

1.  Data preparation: Gather and preprocess the data, including cleaning, normalization, feature selection, and splitting into training and testing sets.
    
2.  Feature representation: Represent each instance in the dataset as a vector of features, either by hand-crafting or using automatic feature extraction techniques.
    
3.  Model training: Implement the SVM learning algorithm, such as the Sequential Minimal Optimization (SMO) or the stochastic gradient descent (SGD) algorithm, to learn the decision boundary that separates the two classes with the maximum margin.
    
4.  Model testing: Use the trained SVM model to classify new instances and evaluate its performance using appropriate metrics, such as accuracy, precision, recall, F1 score, or area under the ROC curve.
    
5.  Hyperparameter tuning: Tune the hyperparameters of the SVM model, such as the regularization parameter C, the kernel function type, and its parameters, using cross-validation or grid search techniques.
    
6.  Optimization: Optimize the implementation of the SVM algorithm for efficiency and scalability, such as using sparse data structures, parallel processing, or GPU acceleration.
    
7.  Visualization: Visualize the decision boundary and the support vectors in 2D or 3D space to gain insights into the learned model.
    
8.  Documentation and presentation: Write a report that describes the SVM implementation, its performance, and its limitations, as well as the design decisions and the challenges faced during the project. Present your work to the class and be prepared to answer questions and receive feedback.
    

Keep in mind that implementing an SVM from scratch can be a challenging task, especially if you are new to machine learning or programming. You may want to start by studying existing implementations and tutorials, such as those available in popular machine learning libraries like scikit-learn or TensorFlow, before attempting to create your own.
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