code: def insight_1_feature_imp(self, df):
      #
      top_important_variables = []
      average_shap_values = []
  
      df_numpy = df.to_numpy()
  
      for i in range(3):
          top_important_variables.append(df_numpy[-1 * (i + 1)][0])
          average_shap_values.append(df_numpy[-1 * (i + 1)][1])
  
      sentences = []
  
      sentences.append(
          "This graph helps you identify which features in your dataset have the greatest effect on the outcomes of your machine learning model."
      )
      sentences.append(
          "In this graph, you can see: "
          + str(top_important_variables[0])
          + " "
          + str(top_important_variables[1])
          + " "
          + str(top_important_variables[2])
          + " "
          + "are the top three most important variables according to your machine learning model."
      )
  
      sentences.append(
          "Remember, each variable might affect the outcome differently. Let’s scroll down and explore how each variable impacts the outcome."
      )
  
      sentences.append(
          "On average, the variable "
          + str(top_important_variables[0])
          + " "
          + "will change the model outcome by "
          + str(round(average_shap_values[0], 2))
      )
  
      return sentences
  
file_path: explainx/lib/modules/local_explanation/apps/insight_regression.py
imports: from imports import *
label_desc: Use image node_id 2 for calling the insight_regression obj's underlying member method code with example usage: obj.insight_1_feature_imp(df) and returns: sentences