How to make machine learning specifically for an individual in a group when we have the data on the group?
There are a number of other options.  The way to select between them is to ask yourself: How are you going to deploy this classifier?  What data will be available to the classifier?

It's possible to build a single model that still has the ability to personalize for a particular person.  You do that by adding another attribute that identifies the person; typically, this would be encoded as a one-hot vector, so you have 3000 data points, and each data point has some features realted to their emotion measurement(s), plus 100 more features for the one-hot-encoded identity vector.

The exact details depend on what data will be available at test time.  Suppose that you will try to predict the number of calories in the evening meal based on the emotion measurements over the past week.  Then you have $100 \times (30-6) = 2400$ data points (the first 6 days from each person aren't usable because you don't have a week of emotion measurements yet).  Each data point has 135 features: 35 features for the emotion measurements over the past 7 days, and 100 features for the one-hot-encoded vector.  Then you train a model on this.

If you use this kind of approach, you need to be careful about how you split the data into training, validation, and test sets.  I suggest you split by person not by data point; so if you want a 60/20/20 split, you pick 60 people to be in the training set (leading to $60 \times 24$ data points in the training set, 20 people to be in the validation set ($20 \times 24$ data points), and 20 people for the test set ($20 \times 24$ data points).  This is based on a deployment scenario where you train a single model once based on some people's data, and then to predict calories for a new person, you don't retrain it based on historical data from the new person.

Hopefully you can see how to adjust this based on the deployment scenario.  The basic principle is to figure out how the classifier will be used in practice, and then choose an evaluation strategy that matches how it will be used.



Another option is to first try to cluster the users into a few clusters, and build a classifier to predict which cluster each person is in.  Then, build one model per cluster.



A third option is to try to identify some additional attribute of the person that will help you predict their calory consumption.  Add this as an additional feature (W).    For instance, this might be gender or demographics or anything else that you think has predictive power.