Stock Price Prediction with Machine Learning (Beginner-Friendly)
Download the source code from here:
https://onepagecode.substack.com/
Want to learn how to predict stock prices using Machine Learning? This video is your perfect guide! We'll break down the process into easy-to-understand steps, making it accessible even for beginners with no prior coding experience.
In this video, you'll discover:
The fundamentals of using Machine Learning for stock price prediction
How to collect and prepare historical stock data
Building a powerful neural network model with Keras, a popular deep learning library
Step-by-step walkthrough of training and evaluating your model
Tips and tricks for interpreting your predictions
By the end of this video, you'll have the knowledge and tools to build your own basic stock price prediction model using Machine Learning!
This video is perfect for:
Investors looking to gain an edge in the market
Students curious about Machine Learning applications
Anyone interested in learning data analysis and prediction techniques
Ready to unlock the power of Machine Learning for stock price prediction? Watch now and get started on your journey!
00:00:00 Extract open prices for training dataset
00:01:02 Imports libraries, sets plotting style
00:02:23 Defines a variable called "ticker"
00:02:59 Assigns the value 'TSLA' to ticker
00:03:28 Assigns a value to a variable
00:03:52 Assigns value 'TSLA' to variable 'ticker'
00:04:39 Retrieves Yahoo finance data for a stock
00:05:31 Counts rows and columns in dataframe
00:06:08 Changes plot style to dark background
00:06:43 Plots closing price as a graph
00:07:45 Splits dataset for training
00:09:03 Normalize dataset between 0 and 1
00:10:09 Splits data into training samples
00:11:42 Convert data to NumPy arrays
00:12:14 Reshape x_train data for LSTM model
00:12:57 Sequential neural network with LSTM layers
00:14:36 Compiling a neural network model
00:15:48 Fits model with training data in 3 epochs
00:17:10 Prepare test data for model evaluation
00:18:21 Converts variable `x_test` to NumPy array
00:18:58 Reshapes input data for CNN model
00:19:48 Make predictions and scale back
00:20:36 Calculate and print root mean square error
00:21:21 Applies dark style to plots
00:21:59 Plotting stock price predictions for Amazon
00:23:45 Extracts the last 15 elements
00:24:23 Retrieve and predict stock price
00:25:34 Retrieve and predict stock opening prices
