StephenFordham

class DeletionException(Exception):
    def __init__(self, original_value):
        self._original_value = original_value

    def __str__(self):
        return 'The value for the name attribute, {} can not be deleted'.format(self._original_value)


class Employees(object):
    def __init__(self, name, age, id_number):

StephenFordham

class Employees(object):
    def __init__(self, name, age, id_number):
        self._name = name
        self._age = age
        self._id_number = id_number

    def __setattr__(self, key, value):
        if key == '_name' and hasattr(self, '_name'):
            raise AttributeError('The value for the name attribute has already been set, and can not be re-set')
        if key in ['_age', '_id_number']:

StephenFordham

class Employees(object):
    def __init__(self, name, age, id_number):
        self._name = name
        self._age = age
        self._id_number = id_number

    def __setattr__(self, key, value):
        if key == '_name' and hasattr(self, '_name'):
            raise AttributeError('The value for the name attribute has already been set, and can not be re-set')
        if key in ['_age', '_id_number']:

StephenFordham

results =[]

strategies = ['mean', 'median', 'most_frequent','constant']

for s in strategies:
    pipeline = Pipeline([('impute', SimpleImputer(strategy=s)),('model', model)])
    cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)
    scores = cross_val_score(pipeline, X, y, scoring='accuracy', cv=cv, n_jobs=-1)
    
    results.append(scores)

StephenFordham

dataframe.columns

dataframe = pd.read_csv('framingham.csv')

for i in range(len(dataframe.columns)):
    missing_data = dataframe[dataframe.columns[i]].isna().sum()
    perc = missing_data / len(dataframe) * 100
    print('>%d,  missing entries: %d, percentage %.2f' % (i, missing_data, perc))

StephenFordham

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

dataframe = pd.read_csv('framingham.csv')

dataframe.head()

StephenFordham

features = pd.DataFrame(data=data[0], columns=['feature_' + str(i) for i in range(1, 6)])
lables = pd.DataFrame(data[1], columns=['labels'])
dataset = pd.concat([features, lables], axis=1)
data_point_1 = scaler.transform(np.array(dataset.iloc[0][:-1]).reshape(-1, 5))
knn.predict(data_point_1)[0]

# Output
# 0

StephenFordham

from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
X = scaler.fit_transform(df_feat)
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)

StephenFordham

error_rate = []

for i in range(1, 40):
    knn = KNeighborsClassifier(n_neighbors=i)
    knn.fit(X_train, y_train)
    pred_i = knn.predict(X_test)
    
    error_rate.append(np.mean(pred_i != y_test))
    
   

StephenFordham

from selenium import webdriver
from selenium.webdriver.common.action_chains import ActionChains
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.support.wait import WebDriverWait
from selenium.webdriver.common.by import By
from selenium.webdriver.support import expected_conditions as EC


headless_options = Options()