text: import numpy as np
  import pandas as pd
  import pickle
  from sklearn.preprocessing import MinMaxScaler
  from keras.models import load_model
  
  
  def get_sensitivity_scores(model, features, top_n):
      """
      Finds the sensitivity of each feature in features for model. Returns the top_n
      feature names, features_top, alongside the sensitivity values, scores_top.
      """
      # Get just the values of features
      x_train = features.values
      # Apply min max normalization
      scaler = MinMaxScaler().fit(x_train)
      x_train = scaler.transform(x_train)
      # Find mean and standard deviation of each feature
      x_train_avg = np.mean(x_train, axis=0).reshape(1, -1)
      x_train_std = np.std(x_train, axis=0).reshape(1, -1)
      prediction_mean = model.predict(x_train_avg)
  
      scores_max = []
      # Iterate over each feature
      for i in range(x_train_avg.shape[1]):
          # Copy x_train_avg
          x_train_i = x_train_avg.copy()
          # Add the standard deviation of i to that column
          x_train_i[:, i] = x_train_i[:, i] + x_train_std[:, i]
          result_i = model.predict(x_train_i)
          # Take the difference and divide by standard deviation
          diff = (result_i - prediction_mean) / x_train_std[:, i]
          scores_max.append(diff.flatten()[0])
      scores_max = np.absolute(scores_max)
      indices_top = np.argsort(scores_max)[-top_n:]
      features_top = features.iloc[:, indices_top].columns
      scores_top = scores_max[indices_top]
      return features_top, scores_top
  
  
  # Initialize result sheet
  df_sensitivity = pd.DataFrame()
  n_top = 10
  
  # Load dataset
  df = pd.read_csv('../data/dataset_processed.csv', index_col=0)
  features = df[df.columns[2:]]
  
  # Load SVR model
  filename = '../models/final_SVR_model.sav'
  model = pickle.load(open(filename, 'rb'))
  
  features_top, scores_top = get_sensitivity_scores(model, features, n_top)
  df_sensitivity['SVR features'] = features_top
  df_sensitivity['SVR sensitivity values (abs)'] = scores_top
  
  df_sensitivity.to_csv('../results/sensitivity_analysis.csv')
  