kiku1705

sftp = Net::SFTP.start(@sftp_details['server_ip'], @sftp_details['server_username'], :password => decoded_pswd)
  if sftp
    begin
      sftp.dir.foreach(@sftp_details['server_folder_path']) do |entry|
        print_memory_usage do
          print_time_spent do
            if entry.file? && entry.name.end_with?("csv")
              batch_size_cnt = 0
              sftp.file.open("#{@sftp_details['server_folder_path']}/#{entry.name}") do |file|
                header = file.gets

kiku1705

import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from sklearn import metrics

df = pd.read_csv('mammographic_masses.data.txt',feature_names=['BI_RADS','age','shape','margin','density','severity'])
df = df.replace('?', np.nan)
#check data is not baised and equally distributed 
df.describe(include='all')
df.dropna(inplace=True)

kiku1705

def passing_grade(maths,physics,chemistry):
  if maths >= 35 and physics >= 35 and chemistry >= 35:
    avg_marks = (maths + physics + chemistry) / 3
    if avg_marks <= 59:
      print("C Grade")
    elif avg_marks <= 69:
      print("B Grade")
    else:
      print("A Grade")
  else: