Microsoft Stock Price Prediction

[ayush-09]

Objectives:

Build a LSTM model for stock price prediction. Train the model on historical stock price data. Evaluate the model's performance using appropriate metrics. Use the trained model to make predictions on unseen data. Visualize the predicted stock prices and compare them with the actual prices. Proposed Methodology:

Data Collection: Obtain historical stock price data from reliable sources. In this example, the MSFT.csv file is used as input data. Data Preprocessing: Clean and preprocess the data by removing any missing values or outliers. Scale the data using standardization to improve model performance. Train-Test Split: Split the preprocessed data into training and testing datasets. Use 80% of the data for training and 20% for testing. LSTM Model Architecture: Design and implement a sequential LSTM model with multiple LSTM layers and dense layers for regression. Model Training: Train the LSTM model using the training dataset and optimize the model's parameters using the Adam optimizer and mean squared error loss function. Model Evaluation: Evaluate the trained model's performance using appropriate metrics such as mean squared error (MSE) and visualize the loss curve during training. Predictions: Use the trained model to make predictions on the testing dataset. Rescale the predictions to their original scale for interpretability. Visualization: Plot the predicted stock prices along with the actual prices to visually assess the model's performance.

[ayush-09]

@khusheekapoor Please assign this task to me

[khusheekapoor]

@ayush-09 - you can go ahead! We are assigning you 21 days for this project, after which it will be assigned to someone else if not completed. All the best! Name the file as: algorithm_dataset.ipynb and link it in the readme of the labeled directory as algorithm - dataset.