title: Shuffling a classification timeseries data
body: <p>I am working on a time series binary classification problem. Where each of my rows represent a single person (i got N consumers), and the columns are daily measurements from a single variable that i got from him (K measurements for each consumer). Then, i need to detect if someone has commited fraud or not (FLAG 1 or 0). A small example here:</p>
  <pre><code>data = {'CONS_NO': [1,2,3,'N'], 'Day_1': [1, 2, 3, 4], 'Day_2': [200, 321, 0, 128], 'Day_K': [123, 0, 3, 1], 'FLAG':[1,1,0,0]}
    
  # Create DataFrame  
  df = pd.DataFrame(data)
  df
  
    CONS_NO  Day_1  Day_2  Day_K  FLAG
  0       1      1    200    123     1
  1       2      2    321      0     1
  2       3      3      0      3     0
  3       N      4    128      1     0
  </code></pre>
  <p>The way my dataset is now, the first 3000 rows are made up of consumers who committed fraud, while the rest of the rows are made up of honest consumers.</p>
  <p>I have seen that i shouldn't shuffle my columns and that i need to use something like <code>TimeSeriesSplit()</code> to split my train/test sets. But, is it ok to shuffle the rows in my dataframe? Or to be more precise, do i really need to do this? Will it help with training my model?</p>