Labelled data is necessary for supervised learning because it allows the model to learn the relationship between the input and output variables. In supervised learning, we train the model on labeled data, where each data point is associated with a label or target variable. This means that we know the expected output or response variable for each input or feature set.

The model uses the labeled data to learn the underlying patterns or relationships between the input and output variables. It then uses this knowledge to make predictions on new, unseen data. Without labeled data, the model cannot learn the relationship between the input and output variables, and hence cannot make accurate predictions on new data.

The goal of supervised learning is to learn the underlying patterns or relationships between the input and output variables so that we can make accurate predictions on new, unseen data.

Figures 2 and 3 below give us an idea about the RAW data available to us for analysis, and its transformation to clean data.

This dataset is not clean and formatted so that a Neural Network can be implemented onto it. We dropped features, addressed data type mis-match, and modified column values so as to make the dataset suitable for the model to be implemented.

The final dataset (figure 3) is a combination of 23 of these cleaned datasets for each league. As can be seen, we have a total dataset with dimensions 945x6

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Now, since our dataset is ready, next, we need to split it into training and testing datasets.

Splitting data into training and testing sets is necessary in supervised learning to evaluate the performance of the model. The purpose of training a model is to make it learn from the given data so that it can make accurate predictions on unseen data. However, if the model is overfitted, it will perform well on the training data but poorly on the testing data, which defeats the purpose of creating a model in the first place.

To avoid overfitting, the data is split into two sets: the training set and the testing set. The model is trained on the training set and evaluated on the testing set. This way, the model can be tested on data it has not seen before, and the performance on the testing set can be used to estimate the performance of the model on new, unseen data.

Splitting data into training and testing sets is also useful for hyperparameter tuning. Hyperparameters are settings that are chosen before training a model, and they can significantly impact the performance of the model. By testing the model on the testing set, different hyperparameters can be evaluated, and the ones that lead to the best performance can be selected.

Creating a disjoint split when creating test and train splits is important for several reasons:

Preventing overfitting: When a model is trained on a dataset, it may learn to memorize the specific data points and relationships within that dataset, rather than learning more generalizable patterns. This can lead to overfitting, where the model performs well on the training data but poorly on new data. By creating a disjoint split where the test set contains data that the model has not seen during training, we can evaluate the model's ability to generalize to new data and prevent overfitting.

Evaluating model performance: When testing a model's performance, we want to know how well it will perform on new, unseen data. By creating a disjoint split, we can evaluate the model's performance on a set of data that it has not seen during training, which gives us a more accurate estimate of how the model will perform in the real world.

Improving model selection: When comparing the performance of different models, we want to ensure that they are being evaluated on the same set of data. By creating a disjoint split, we can ensure that all models are being evaluated on the same set of test data, which allows for a fair comparison of their performance.

Overall, creating a disjoint split when creating test and train splits is essential for evaluating and comparing machine learning models, as it allows us to test their ability to generalize to new data and prevent overfitting.

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Neural networks (NNs) require specific labels depending on the type of neural network you are using. In general, neural networks require labeled data, which means that the data used for training the network must have a corresponding label or output value associated with each input. For example, in a binary classification problem, each data point is labeled as either a 0 or a 1, and the neural network is trained to correctly classify each data point as one of these two labels. Similarly, in a multi-class classification problem, each data point is labeled with a specific class label, and the neural network is trained to correctly classify each data point into one of the multiple classes.

In addition, the type of neural network being used can also affect the labeling requirements. For instance, in supervised learning tasks, such as classification and regression problems, the labeled data is used to train the neural network to produce accurate outputs given input data. In contrast, in unsupervised learning tasks, such as clustering and anomaly detection, labeled data may not be required, and the neural network learns patterns in the input data without a specific output to compare against.

The Figure above displays, what is known as the "learning curve." It represents the relationship between the number of training iterations (epochs) and the model's loss function.

As can be seen, in the initial epochs, the model is not trained enough and its predictions are not accurate, so the loss function is high. As the number of epochs increases, the model starts to learn more patterns in the data, which leads to a decrease in the loss function. The curve reaches a point where it flattens out, indicating that the model is no longer learning and has reached its best performance.

Therefore, we can conclude that the model is improving and learning from the data. It is essential to monitor the learning curve to ensure that the model is not over-fitting or under-fitting the data.