Predicting Stock Prices with LSTM in Python: 30-Day Forecast Using Yahoo Finance Data.

In this tutorial, learn how to predict stock prices with Python using Long Short-Term Memory (LSTM) neural networks. We’ll go through each step—from loading stock data from Yahoo Finance to building and training an LSTM model for a 30-day stock price forecast. Perfect for data science beginners and finance enthusiasts, this video shows how deep learning can be applied to financial predictions. Don’t forget to like, subscribe, and support the channel!

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Time Stamp:
00:00 Introduction
00:20 Disclaimer
00:45 Welcome and what we will go over
02:22 What is LSTM?
03:06 Why use LSTM?
03:53 List of Steps we will go over
04:58 Open Anaconda to open Jupyter Notebook
05:20 Create a new workbook
05:38 How to add more cells in Jupyter Notebook
05:52 Installing Necessary Libraries
07:24 Import libraries
09:27 Step 1: load the stock data from Yahoo Finance
14:10 Explaining Ticker Symbols for Stock Market symbols
15:52 Step 2: Process and scale the data
19:32 Step 3: Prepare the training data by creating features and target sets
24:09 Step 4: Build the LSTM model architecture
29:01 Step 5: Train the model with the training data.
32:18 Step 6: Prepare data for generating future predictions.
33:48 Step 7: Generate the 30-day forecast using the model.
33:08 Step 8: Transform predictions back to the original scale.
40:20 Step 9: Visualize the historical data and 30-day forecast
43:30 Visualize the historical data with predictions
45:01 Thank you message

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