title: Getting 'LinAlgError: Eigenvalues did not converge' when running factor analysis
body: <p>select the dataset and standardise the dataset and update the standardised dataset in new dataframe</p>
  
  <pre><code>credit_seg_new.columns ## new dataset
  </code></pre>
  
  <pre><code>data_scaled
  from sklearn.preprocessing import StandardScaler
  sc = StandardScaler()
  data_scaled = sc.fit_transform(credit_seg_new)
  </code></pre>
  
  <p>then finding the correlation of that standardised dataset</p>
  
  <pre><code>corrl=pd.DataFrame(data_scaled).corr()
  corrl
  </code></pre>
  
  <p>I got the eigen value and cum_variance of that correlation</p>
  
  <pre><code>eigen_values = np.linalg.eigvals(corrl)
  eigen_values_cumvar = (eigen_values/corrl.shape[0]).cumsum()
  pd.DataFrame({'Eigen_value': eigen_values, 'Cumm_variance':eigen_values_cumvar}, index=range(1,23)).round(4)
  </code></pre>
  
  <p><a href="https://i.stack.imgur.com/H9Wjs.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/H9Wjs.png" alt="Result of the above code"></a></p>
  
  <p>but when using factor analysis</p>
  
  <pre><code>fa = FactorAnalyzer(n_factors=10,rotation='varimax',method='ml').fit(credit_seg_new)
  </code></pre>
  
  <p><a href="https://i.stack.imgur.com/xwiYV.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/xwiYV.png" alt="I&#39;m getting error"></a></p>
  
  <p>please help as i'm stuck!!!</p>