Project Story: Stock Price Prediction with LSTM

About the Project

Inspiration

As an enthusiast of both finance and machine learning, I was inspired to explore how LSTM models could be used for stock price prediction. The challenge of predicting the unpredictable intrigued me, and I sought to develop a robust solution that could provide valuable insights for investors.

Learning Journey

Throughout the project, I delved deep into the world of time series forecasting, experimenting with different architectures of LSTM models and optimizing their hyperparameters. I learned about the significance of feature engineering in improving model performance and gained insights into the complexities of financial data analysis.

Building the Project

The project was built using Python, leveraging popular libraries such as pandas, scikit-learn, and Keras. I scraped historical stock data from Yahoo Finance, preprocessed it for modeling, and engineered additional features like MACD histograms and seasonal decomposition to enhance prediction accuracy. Hyperparameter optimization using Grid Search Cross Validation was employed to fine-tune the LSTM models.

Challenges Faced

One of the main challenges I encountered was dealing with the inherent volatility and non-linearity of stock prices. Finding the right balance between model complexity and interpretability was also a recurring challenge. Additionally, optimizing hyperparameters efficiently while avoiding overfitting required careful experimentation and validation.

Challenges Faced One of the main challenges I encountered was dealing with the inherent volatility and non-linearity of stock prices. Finding the right balance between model complexity and interpretability was also a recurring challenge. Additionally, optimizing hyperparameters efficiently while avoiding overfitting required careful experimentation and validation.

Built With

• keras
• machine-learning
• python
• seaborn