lakshay-arora

from pandas.io.parsers import ParserError

# create a list to store the dataframes 
dataframe_list = []

# iterate through the folders 1 to 34
for folder in range(1, 35):
    
    # create two boolean variables for both kind of exceptions.
    parse_error = False

lakshay-arora

pd.read_csv('dataset/32/region_32.csv')

lakshay-arora

# create a list to store the dataframes
dataframe_list = []

# iterate through folder 1 to 34
for folder in range(1, 35):
    # try we are able to read the file
    try :
        ### notice that for folder i, we have file name "region_i"
        ### create the file name
        file_name = 'region_' + str(folder) + '.csv'

lakshay-arora

for folder in range(1, 35):
    
    file_name = 'region_' + str(folder) + '.csv'
    data = pd.read_csv('dataset/'+ str(folder) +'/' +file_name)

lakshay-arora

for directory in glob.glob('dataset/*'):
    for files in glob.glob(directory + '/*'):
        print(files)

lakshay-arora

# list all the files in the folder
for directory in glob.glob('dataset/*'):
    print(directory)

lakshay-arora

# import the required libraries
import glob
import pandas as pd

lakshay-arora

train_gender_encoded, validate_gender_encoded, test_gender_encoded = transform_data(train_gender,
                                                                                    validate_gender,
                                                                                    impute_gender,
                                                                                    'gender',
{
    'type' : { 'BinaryEncoding' : ['city','branch_code','age_bucket'],
               'OneHotEncoding' : ['occupation','dependents']
             }
})

lakshay-arora

def transform_data(_data,_validate,_test,_target,encoding) :
    
    if 'BinaryEncoding' in encoding['type'].keys():
        ce_OHE = ce.BinaryEncoder(cols=encoding['type']['BinaryEncoding'])
        ce_OHE.fit(_data)
        _data = ce_OHE.transform(_data)
        _test = ce_OHE.transform(_test)
        _validate = ce_OHE.transform(_validate)
    
    if 'TargetEncoding' in encoding['type'].keys():

lakshay-arora

# get the prediction array
predictions = predictions['predictions']

# print the actual image and the predicted result
for i, prediction in enumerate(predictions):
    print("Prediction: ",np.argmax(prediction))
    show(i,test_images[i])