Great! I'd be happy to guide you through the process of creating a Gaussian Mixture Model (GMM) by hand. Please upload the data, and we can proceed with the step-by-step derivation and calculation of the model parameters.

In the meantime, here's an overview of the key steps involved in creating a Gaussian Mixture Model:

Steps to build a Gaussian Mixture Model (GMM):

1. Initialization:

   • Assign initial values for the parameters (weights, means, and variances of the Gaussian components).

2. Expectation Step (E-step):

   • Compute the responsibility for each data point, which is the probability that the data point belongs to each Gaussian component. This uses the current values of the model parameters.

3. Maximization Step (M-step):

   • Update the parameters (weights, means, variances) based on the responsibilities calculated in the E-step.

4. Log-likelihood:

   • Compute the log-likelihood of the data under the current model and check for convergence. If the model has not converged, return to step 2.

Once you upload the data, we can apply these steps with specific calculations.

Would you like me to proceed with the detailed explanation of each step while waiting for the data?