Approach

To address the challenging requirements, we adopted a structured approach:

Data Collection: We utilized Python to collect and preprocess historical Bitcoin price data, ensuring data consistency and quality.

Model Selection:
We employed two primary approaches for time series forecasting:

Hyperparameter Tuning: We performed extensive hyperparameter tuning to optimize the performance of the LSTM and Prophet models.

Model Evaluation: Models were rigorously evaluated using metrics such as RMSE to ensure they met the client's specified accuracy threshold.

Google Cloud Platform (GCP): GCP was used for scalable computing and storage

Technologies

Outcome

The project yielded impressive results:

High Prediction Accuracy: The LSTM models achieved RMSE values below 0.05, demonstrating their accuracy in forecasting Bitcoin prices.

Time Interval Flexibility: The models could predict Bitcoin prices over various time intervals, from short-term fluctuations to long-term trends.

Actionable Insights: The accurate price predictions provided valuable insights for investors and traders, enabling informed decision-making.