Group Assignment 1

Portfolio Exercise 1

Note: M3 - Group Assignment 1 Deadline: Tuesday 19th of November at 12.00 PM

Introduction

In this assignment, you’ll deepen your understanding of time series data analysis, feature engineering, and neural network modeling. Using the Tesla stock closing prices, you’ll preprocess the data to create useful features and normalize it for training. The main goal is to build, train, and evaluate a Recurrent Neural Network (RNN) in PyTorch, exploring how various architectures and hyperparameters impact model performance.

Task

  • Build, train, and evaluate a RNN using Pytorch.
  • The RNN should have a minimum of 2 hidden layers.
  • Experiment with at least 3 different variations of hyperparameters (e.g., number of layers/neurons, activation functions, epochs learning rates, etc.).

The assignment should include the following steps:

  1. Feature Selection
  2. Feature Engineering (if necessary)
  3. Standard ML Preprocessing (if necessary)
  4. Train-Test Split
  5. Defining a Neural Network Architecture in Pytorch
  6. Defining a Training Loop
  7. Training the Model
  8. Experimenting with Different Hyperparameters
  9. Evaluating the Final Model on the Test Data

Data

  • Dataset: Tesla (TSLA) Stock Market Data
  • Source: Use Yahoo Finance or similar to download the dataset.

Delivery

  • Create a GitHub repository.
  • Save the Colab notebook in the repository.
  • Provide a README.md with a brief description of the assignment.
  • Submissions can be made in groups of up to 3 members.
  • Submit the assignment by sending an email with the link to the repository to Hamid (hamidb@business.aau.dk).