Video available at - https://www.youtube.com/watch?v=usjB5TsOo9I
Building a stock price prediction application in Python involves several key steps, which we covered comprehensively in this video. The process begins with data collection, where historical stock price data is obtained from sources like Yahoo Finance using libraries such as yfinance. Next, data preprocessing is crucial for cleaning the data, handling missing values, and engineering features that could improve prediction accuracy. Techniques like moving averages and technical indicators are commonly used for feature engineering.
Once the data is prepared, the next step is model selection. Various algorithms can be considered, ranging from simple linear regression to more complex techniques like random forests or LSTM networks. In our example, we opted for a Linear Regression model for its simplicity and interpretability. The model is trained on the preprocessed data, and its performance is evaluated using metrics like Mean Squared Error.
Deployment is facilitated through a Flask web application, allowing users to input stock features and receive predicted prices. The Flask application serves as the interface for interacting with the trained model. Users can send POST requests containing stock features, and the application responds with predicted prices calculated by the model.
Continuous improvement is essential for maintaining the application's accuracy and relevance over time. This involves monitoring the model's performance, collecting feedback from users, and updating the model with new data or improved algorithms. Version control and documentation play crucial roles in managing the codebase and ensuring collaboration among team members.
Throughout the video, we provided detailed explanations and code snippets for each step of the process. From data preprocessing to model training and deployment, we aimed to provide a comprehensive guide to building a stock price prediction application in Python. Additionally, we discussed the importance of optimization, user interface enhancement, security measures, and scalability considerations for further improving the application.
By following these steps and best practices, developers can create robust and effective stock price prediction applications that provide valuable insights to users. From data collection to deployment and beyond, attention to detail and continuous improvement are key to success in this domain.