Yes it is possible and sklearn library already has a function for it. Here is the link for more detail. scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier.predict_proba

Give me some time. I will give you a detailed answer by end of this week.

For rounds in cluster head selection, I have made a notebook. Check it and tell me if it answers the question or if you require more detail. www.kaggle.com/code/gauranggupta123/cluster-head-selection-using-cso For the process of finding the number of alive nodes, there are certain assumptions you have to make. Let's take an example so that it is easy to understand. Assumptions: Energy stored in each node:- 50 units Energy consumed in normal operations per day:- 1 unit Energy consumed when communicating per day:- 4 units ( I assume that at particular times of t day, sensors send readings to base stations for further processing and analysis. This is a variable depending on what type of application you are using your nodes for) Recharge by other sources (sun, wind, water, geothermal, etc) per day:- 1 unit So in one day, the sensor consumes 1 + 4 - 1 = 4 units of energy. Total number of days the nodes will live = 50 / 4 = 12.5 days. The above example is a simulation of how a node's lifetime is calculated. Even in this example, I haven't considered the environmental conditions the nodes are in. Some environments are extremely hostile and can damage the nodes' circuits. These nodes are generally preferred in hostile environments or the environments we want to analyze without going there. Also, you can add several variables that only one node is added per day so this example will become the lifetime of one node. To know the sensor node network lifetime is difficult because there is no clearly defined method for it. Some researchers agree on the fact that if more than half of the nodes in the system are dead that means the network is considered dead, others argue that even if one node is alive the network is alive. Then there are some problems with each topology. Here topology means in which manner the nodes communicate with one another and base station. The most commonly used topology is tree topology. In this, there is a vulnerability that if an upper node collapses, the node that only communicates to it to send data to the base station will not be able to do that and they will be considered practically dead as we are not getting any data out of them.

You can start on kaggle. They have courses on Machine Learning along with projects. After that explore datasets and try to apply machine learning models on data to get predictions

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