The AAPL Stock Price Forecast project utilizes the PyCaret library, which simplifies machine learning workflows for time series forecasting. The goal is to predict future Apple (AAPL) stock prices based on historical data using PyCaret's time series forecasting module. This project is designed to explore different machine learning models to forecast stock prices, allowing for comparison and selection of the best-performing model. #Project Workflow: Data Loading: Historical stock price data for Apple (AAPL) is loaded into a Pandas DataFrame, typically containing columns like Date (or Index) and Price (closing price). The data is preprocessed to ensure correct formatting for time series analysis.
PyCaret Setup: The PyCaret setup function initializes the time series forecasting environment by specifying: The target variable: typically, this is the stock Price column. The fold_strategy: this defines how cross-validation will be performed. Time series strategies such as 'expanding' or 'rolling' windows are used for time series data. Other parameters such as session ID for reproducibility.
Model Comparison: PyCaret provides an easy way to compare several machine learning models for time series forecasting. By using the compare_models() function, different models are evaluated on performance metrics like RMSE, MAPE, or MAE. This step helps identify the best model for forecasting future stock prices.
Forecasting: Once the best model is selected, it is used to generate predictions for future stock prices. The model is trained on historical data, and then a forecast for the upcoming time period is made.
Visualization: The actual historical prices are plotted along with the forecasted stock prices to visually compare the model's performance. This allows for an intuitive understanding of how well the model captures the trend and patterns in stock price movements.