Stock Market Prediction using LSTM

• Tech Stack: Python (version 3.7+), Pandas, NumPy, Scikit-learn, SVM, KNN, DT, XGBoost, Random Forest, Logistic Regression
• Github URL: Project Link

In this project, we leverage historical stock market data to train an LSTM model, which learns from past price patterns to predict future stock prices. The dataset includes features like opening price, closing price, and volume, and is preprocessed, split into training and testing sets, and transformed as necessary. Using frameworks like TensorFlow or PyTorch, we train the LSTM model, adjusting hyperparameters and employing regularization and dropout to prevent overfitting. After training, we evaluate the model's performance on the testing set using metrics such as mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE), and visualize the predicted stock prices alongside actual prices to assess accuracy.