Network Modeling Workflow

1. Problem Definition

Identify the Task: Determine whether the problem is classification, regression, clustering, or another type of problem.
- Example: Is it a linear classification problem, or does it require more complex modeling?
Determine the Output: Define what the model should predict (e.g., binary output, categorical labels, continuous values).

Collect Data: Gather the dataset(s) needed for the task.
Explore and Understand the Data:
- Visualize data distributions and relationships.
- Identify missing values or outliers.
Data Cleaning:
- Handle missing data (e.g., imputation, removal).
- Remove or handle outliers.
Data Preprocessing:
- Normalization/Standardization: Decide if data needs to be normalized or standardized based on the model type.
- Encoding Categorical Variables: Convert categorical data into numerical values using methods like one-hot encoding.
- Feature Engineering: Create or select features that will improve model performance.

Train-Test Split: Divide the data into training and testing sets (commonly 80/20 or 70/30 splits).
Validation Set: Optionally, further split the training set into training and validation sets to fine-tune model parameters.

Choose a Model Type:
- Linear Models: For simple linear relationships.
- Deep Neural Networks (DNN): For more complex tasks.
- Convolutional Neural Networks (CNN): For image data.
- Recurrent Neural Networks (RNN): For sequential data.
Select Architecture:
- Input Layer: Match the number of neurons to the number of input features.
- Hidden Layers: Determine the number of hidden layers and neurons in each based on problem complexity.
- Activation Functions: Choose appropriate activation functions (e.g., ReLU, Sigmoid, Softmax).
- Output Layer: Define based on the type of task (e.g., single neuron for binary classification, softmax for multi-class classification).

Choose Loss Function: Select an appropriate loss function (e.g., cross-entropy for classification, mean squared error for regression).
**Select