Data Preprocessing Enhancements for Improved Model Performance

[FreeSpirit11]

Description

To improve the performance and accuracy of the Bitcoin price prediction model, the following enhancements are proposed for the data preprocessing step:

1. Use More Features:

   • Include additional relevant features such as trading volume, moving averages, or technical indicators (e.g., RSI, MACD).

2. Feature Engineering:

   • Create new features such as percentage changes, rolling averages, or volatility measures to capture more information relevant to the prediction task.

3. Data Normalization:

   • Ensure all features are properly scaled before training to improve model convergence and performance.

Implementation Details

1. Include More Features:

   • Add features like Volume, MA7, MA14, MA21, RSI, and MACD.

2. Feature Engineering:

   • Calculate and add percentage changes for the Close price.
   • Compute rolling averages and volatility measures over different windows (e.g., 7-day, 14-day).

3. Data Normalization:

   • Use MinMaxScaler or StandardScaler from sklearn to normalize all features.

[FreeSpirit11]

Hi @Niketkumardheeryan , I have raised this issue. Please assign it to me under GSSOC.

[Niketkumardheeryan]

@FreeSpirit11 go for it

[FreeSpirit11]

HI @Niketkumardheeryan , I have started working on this issue. Please assign it level 2 or level 3 .