![instructor profile](https://s3.amazonaws.com/files.dezyre.com/faculty/images/Mehmet-Akgun.jpeg)
University of Economics and Technology, Instructor
![instructor profile](https://s3.amazonaws.com/files.dezyre.com/faculty/images/gareth_faculty.jpeg)
Chief Scientific Officer, Machine Medicine Technologies
![instructor profile](https://s3.amazonaws.com/files.dezyre.com/faculty/images/james_faculty.jpeg)
Dev Advocate, Pinecone and Freelance ML
![instructor profile](https://s3.amazonaws.com/files.dezyre.com/faculty/images/Ted.jpeg)
Director of Business Intelligence , CouponFollow
Learn how to predict stock prices using RNN and LSTM models. Understand deep learning concepts and apply them to real-world financial data for accurate forecasting.
Get started today
Request for free demo with us.
Schedule 60-minute live interactive 1-to-1 video sessions with experts.
Unlimited number of sessions with no extra charges. Yes, unlimited!
Give us 72 hours prior notice with a problem statement so we can match you to the right expert.
Schedule recurring sessions, once a week or bi-weekly, or monthly.
If you find a favorite expert, schedule all future sessions with them.
Source:
Source:
Source:
Source:
Source:
Source:
Source:
Source:
Source:
Source:
Source:
Source:
Source:
250+ end-to-end project solutions
Each project solves a real business problem from start to finish. These projects cover the domains of Data Science, Machine Learning, Data Engineering, Big Data and Cloud.
15 new projects added every month
New projects every month to help you stay updated in the latest tools and tactics.
500,000 lines of code
Each project comes with verified and tested solutions including code, queries, configuration files, and scripts. Download and reuse them.
600+ hours of videos
Each project solves a real business problem from start to finish. These projects cover the domains of Data Science, Machine Learning, Data Engineering, Big Data and Cloud.
Cloud Lab Workspace
New projects every month to help you stay updated in the latest tools and tactics.
Unlimited 1:1 sessions
Each project comes with verified and tested solutions including code, queries, configuration files, and scripts. Download and reuse them.
Technical Support
Chat with our technical experts to solve any issues you face while building your projects.
7 Days risk-free trial
We offer an unconditional 7-day money-back guarantee. Use the product for 7 days and if you don't like it we will make a 100% full refund. No terms or conditions.
Payment Options
0% interest monthly payment schemes available for all countries.
Overview
Stock price prediction plays a crucial role in financial markets, and accurate forecasts can have significant implications for businesses, investors, and financial institutions.
Stock price prediction helps investors and fund managers make informed investment decisions. By accurately forecasting future stock prices, investors can identify potential opportunities for maximizing profits or minimizing losses. It enables them to allocate their capital wisely and adjust their investment portfolios based on predicted price movements.
Machine and Deep Learning have demonstrated their potential to enhance stock price prediction accuracy and assist investors, traders, and financial analysts in making informed decisions. By leveraging ML techniques, businesses can gain valuable insights, optimize investment strategies, and improve risk management in stock market's complex and dynamic realm.
The prediction of stock prices is a challenging task due to the inherent complexity and volatility of financial markets. Traditional methods often fail to capture the intricate patterns and dependencies present in stock price data. However, RNN and LSTM models have shown great potential in capturing temporal dependencies and making accurate predictions in various time series forecasting tasks.
In this first project of our four-part series, we will focus on implementing recurrent neural networks (RNNs) and long short-term memory (LSTM) networks specifically for the task of stock price prediction. This project serves as an essential foundation for understanding and utilizing these advanced deep learning techniques in the context of financial forecasting.
Throughout this project, we will delve into the fundamentals of RNNs and LSTMs and explore their suitability for capturing temporal dependencies in stock price data. We will learn how to preprocess and prepare the historical stock price dataset for training and testing our models.
Aim
The aim of this project is to develop a robust stock price prediction system utilizing recurrent neural networks (RNN) and long short-term memory (LSTM) networks. We will also understand the fundamentals of deep learning and its application in financial forecasting.
Data Description
The dataset used for this project consists of historical stock prices for Apple Inc. (AAPL) obtained from the Yahoo Finance API. It includes a comprehensive record of daily stock price data, capturing the opening, closing, highest, and lowest prices, as well as the trading volume for each trading day.
Tech Stack
Language: Python
Libraries: keras, tensorflow, statsmodels, numpy, pandas, yfinance, pandas datareader, pandas_ta
Approach
Neural Networks Basics:
Review the basics of neural networks to understand their structure and functionality.
Building and Training Neural Networks with Keras on an Example Dataset
Loading Time Series Data:
Obtain the time series data for stock prices from yahoo finance.
Data Transformations:
Perform feature scaling or normalization to bring data into a consistent range.
Overlapping window creation for training.
Recurrent Neural Networks:
Model Building and Training
Sequence Generation and Evaluation
LSTMs:
Model Building and Training
Sequence Generation and Evaluation
Multivariate Input and LSTMs
Creating Technical Indicators
Creating Labels
Perform feature scaling or normalization to bring data into a consistent range.
Model Building and Training
Evaluation
Recommended
Projects
30+ NumPy Interview Questions and Answers for Data Analysts
Prepare for success in data analyst interviews with our curated list of 30+ NumPy Interview Questions and Answers. | ProjectPro
Understanding LLM Hallucinations and Preventing Them
A beginner-friendly handbook for understanding LLM hallucinations and exploring various prevention methods.
Get a free demo