Due on Feb 25 (30 points)

1. (10 points) Try to build a linear regression model to predict the stock price of MSFT from the prices of several other stocks. You can download the Jupyter notebook of the question from here.

2. Let's repeat the prediction in the last question using a neural networks

   1. (2 points) Split the MSFT stock price data into training (9/1/2021-11/30/2021), validation (12/1/2021-12/31/2021), and test (1/1/2022-1/31/2022) datasets.

   2. (10 points) Estimate the MSFT stock price again with a fully connected neural network with 5 hidden layers. Each hidden layers have 20 neurons. And use ReLU as activation function.

   3. (4 point) Try different optimization algorithms, SGD, Momentum, Adam. Plot the training loss/validation loss VS epoch.

   4. (4 points) Set learning rate schedulers using OneCycleLR, CyclicLR, and ReduceLROnPlateau. PyTorch offers learning rate schedulers to change the learning rate over time. Check this tutorial for more instruction. Observe any difference in your training