Random Forest Stock Predictor

This project focuses on developing a stock price prediction model using Random Forest, a popular machine learning algorithm. The objective is to predict future stock prices based on historical data, enabling investors to make informed decisions.

The primary goal of this project is to build a robust stock price predictor using the Random Forest algorithm. The model will analyze historical stock data and other relevant features to forecast future price movements accurately.

The dataset used for training and testing the model consists of historical stock prices, trading volume, and various financial indicators. Additionally, external factors such as market sentiment, economic indicators, and news sentiment may also be incorporated to enhance prediction accuracy.

Data Preprocessing: The dataset undergoes preprocessing to handle missing values, normalize features, and engineer new features if necessary.

Feature Selection: Relevant features are selected based on their importance and correlation with the target variable (stock price).

Model Training: The Random Forest algorithm is trained on the preprocessed dataset to learn the underlying patterns and relationships between features and stock prices.

Model Evaluation: The trained model is evaluated using appropriate evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), or Root Mean Squared Error (RMSE).

Prediction: Once the model is trained and evaluated, it is used to make predictions on unseen data, i.e., future stock prices.

The performance of the Random Forest stock predictor will be assessed based on its accuracy in predicting future stock prices. Evaluation metrics such as MAE, MSE, RMSE, and prediction accuracy will be reported.

The trained model can be used to make real-time predictions on future stock prices by providing relevant input features. Additionally, the model can be integrated into trading platforms or investment strategies to assist investors in making informed decisions.

The project concludes with insights into the effectiveness of the Random Forest algorithm for stock price prediction. Potential limitations, areas for improvement, and future research directions will also be discussed.