Recurrent Neural Networks (RNNs) Model

The aim behind RNN is sequencing data, as well as determining the probability of an occurrence. Some common examples are chat boxes, generating image descriptions, and predicting or forecasting.

Refer to this resource when completing the “RNN Model”

Intro to Recurrent Neural Networks LSTM | GRU | Kaggle

Complete the steps below, then write a comprehensive technical report as a Python Jupyter notebook (to include all code, code comments, all outputs, plots, and analysis).

1. Build a long short-term memory (LSTM) model to predict the price of stock for Apple for the year 2021 (Apple Stock Price from 1980-2021 (kaggle.com))
2. Use 75 epochs.
3. Visualize the results for the LSTM model.
4. Evaluate your model by determining the root mean squared error and explain your results (how accurate is your model, what could you do to make it better, etc.).
5. See if the model can predict 2022 prices.

Summarize the overall functioning of the RNN and its accuracy.

Make sure the project documentation contains:

• Problem statement
• Algorithm of the solution
• Analysis of the findings
• References