In this assignment, students will build an ANN, then write a comprehensive technical report as a Python Jupyter notebook $($to include all code, code comments, all outputs, plots, and analysis$).$ Make sure the project documentation contains a$)$ problem statement, b$)$ algorithm of the solution, c$)$ analysis of the findings, and d$)$ references.

Download the $"$NFL Players Dataset," located in the topic Resources. The dataset contains football players' characteristics.

Select from a pool of $200$ "Active Players" and $200$ "Retired Players."

Define "optimal team" based on your decision of the player characteristics necessary to build a team:

$-$Starting offense $(11$ players$)$: passing, rushing, and receiving.

$-$Starting special teams $(11$ players$)$: punting, punt returns, and kick returns.

$-$Starting defense $(11$ players$)$: tackles, sacks, safeties, and fumbles.

Your task is to identify the optimal team from each pool comprised of $33$ "Active Player" and "Retired Player" members.

Examine the multilayer neural network MLP architecture depicted in the $"$DSC$-550$ An Artificial Neural Network Model Image."

Build a deep artificial neural network MLP to include the following: a$)1$ input layer, b$)$ as many hidden layers as you deem necessary, and c$)$ an output layer fully connected to the hidden layers.

Explain your architecture and how the NFL player characteristics are used as inputs.

Activate the MLP by performing the following steps:

Starting at the input layer, forward propagate the patterns of the training data through the network to generate an output.

Based on the network's output, calculate the error that we want to minimize using a cost function that we will describe later.

Back propagate the error, find its derivative with respect to each weight in the network, and update the model.

Repeat steps $1$ through $3$ for multiple epochs and learn the weights of the MLP$.$

Use forward propagation to calculate the network output and apply a threshold function to obtain the predicted class labels in the one$-$hot representation.

Interpret the output of your MLP in the context of selecting an optimal football team.

Please use the $"$NFL Play Statistics dataset $($primary$)"$ dataset on kaggle, I cant put a url in$.$ I$\text{'}$m having trouble with the size of the dataset and overfitting issues when I train the model as just offense, but cant do offense, defense and special teams with oyut size issues. I do think some $.$csv$\text{'}$s are not needed to build the model which should help. But need help.