text: # --------------
  import numpy as np
  import pandas as pd
  import matplotlib.pyplot as plt
  
  # Load the dataframe
  df = pd.read_csv(path)
  
  #Code starts here
  p_a = df[df['fico'].astype(float) >700].shape[0]/df.shape[0]
  print(p_a)
  p_b = df[df['purpose'] == 'debt_consolidation'].shape[0]/df.shape[0]
  print(p_b)
  df1 = df[df['purpose'] == 'debt_consolidation']
  p_a_b = df1[df1['fico'].astype(float) >700].shape[0]/df1.shape[0]
  print(p_a_b)
  result = (p_a_b == p_a)
  print(result)
  new_df = df[df['paid.back.loan'] == 'Yes']
  prob_lp = new_df.shape[0]/df.shape[0]
  print(prob_lp)
  prob_cs = df[df['credit.policy'] == 'Yes'].shape[0]/df.shape[0]
  print(prob_cs)
  prob_pd_cs = new_df[new_df['credit.policy'] == 'Yes'].shape[0] / new_df.shape[0]
  bayes = (prob_pd_cs * prob_lp)/ prob_cs
  print(bayes)
  df1 = df[df['paid.back.loan'] == 'No']
  print(df1.shape)
  df1['purpose'].value_counts().plot(kind="bar")
  inst_median = df['installment'].median()
  print(inst_median)
  inst_mean = df['installment'].mean()
  print(inst_mean)
  df['installment'].hist(normed = True, bins=50)
  
  
  
  