Handle missing data.

[SverreNystad]

Strategies to Handle Missing Values:

1. Removal:
   • Remove rows with missing values, especially when the number of such rows is minimal.
2. Imputation:
   • Replace missing values with statistical measures like mean, median, or mode.
3. Use more advanced techniques like model-based imputation or k-NN imputation. Interpolation:
   • For time-series data, interpolate missing values based on neighboring non-missing values.

Proposed Plan:

1. Quantify Missing Data:

   • Calculate the proportion of missing values in each column to decide whether to remove or impute them.
   2. Impute or Remove:
      • For columns with a minimal amount of missing values, we can consider removing the rows with missing values or imputing them with statistical measures.
      • For columns with a substantial amount of missing values, we can consider advanced imputation methods or, if necessary, removal of the feature.

[SverreNystad]

We removed bad values, and dropped columns that had high percentage of missing data