This project aims to predict Google's stock prices using advanced machine learning techniques, specifically Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN). The project is divided into two main Jupyter notebooks, one focusing on LSTM and the other combining RNN with LSTM methodologies.
The datasets used in this project include historical stock price data for Google. Key features in the dataset include:
- Date
- Opening price
- Closing price
- High
- Low
- Volume of stocks traded
These features are used to understand stock price trends and make predictions.
- Data Cleaning: Removing unnecessary columns, handling missing values.
- Data Transformation: Converting data into a suitable format for analysis (e.g., formatting dates).
- Visualization: Plotting various aspects of the stock data for insights.
- Time Series Plot: Displaying the stock's closing price over time, which helps in identifying the overall trend and any seasonal patterns.
- Volume Traded Plot: Showcasing the volume of stocks traded over time, indicating the market activity levels.
- Moving Averages: Calculating and plotting short-term and long-term moving averages to smooth out price trends and identify momentum.
- Price Distribution: Using histograms and box plots to understand the distribution of stock prices and identify any outliers.
- Correlation Heatmap: Analyzing the correlation between different stock attributes (like open, close, high, low) to understand their relationships.
- Candlestick Charts: For more detailed analysis, showing the opening, closing, high, and low prices within specific time frames.
- The LSTM notebook focuses on building and training an LSTM model, known for its effectiveness in handling time-series data.
- Key steps include data preprocessing, model building, training, and evaluation.
- The RNN notebook introduces a combined approach using both RNN and LSTM.
- This approach aims to leverage the strengths of both models to improve prediction accuracy.
- The results section should include the outcomes of the model predictions.
- Include any metrics used to evaluate the model performance (e.g., RMSE, MAE).
- Visualizations of predicted vs. actual stock prices.
Instructions on how to set up the project environment:
# clone the repository
git clone https://github.com/benjamin-githubprofile/StockMarketPrediction
# navigate to the project directory
cd [project-directory]
# install dependencies
pip install -r requirements.txt