This project focuses on developing a machine learning model to predict stock prices using Long Short-Term Memory (LSTM) networks, a type of recurrent neural network ideal for time-series forecasting. In financial markets, accurately forecasting stock prices is a challenging task due to market volatility and the sequential nature of stock data. This project aims to address this challenge by leveraging LSTM’s ability to learn long-term dependencies, making it an excellent choice for financial forecasting.
We will begin by covering the basics of Python and essential libraries for data science and machine learning, including Pandas, NumPy, Matplotlib, yfinance, and Scikit-learn. This project will also introduce the basics of the stock market and technical analysis, which will help to gain a foundational understanding of stock market trends and indicators that will be essential for data preprocessing and interpretation.
We will also work with historical stock price data, transforming it through various preprocessing steps to create a time-series dataset. The project will include defining a lookback period, where we use a sliding window of past prices to predict the next price. After data preparation, we will build and train the LSTM model, optimizing it through experimentation with hyperparameters like batch size, number of LSTM units, and dropout rates. They will also evaluate the model’s predictive performance using metrics such as Mean Square Error (MSE) to understand its accuracy.
This will be our week wise plan for the project: Week-wise Plan
Note: There may be slight changes in the plan if there is time constraint.
By the end, you will have hands-on experience with deep learning in finance!