tfolkman

from sklearn.datasets import load_breast_cancer
from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.metrics import f1_score, classification_report, accuracy_score, mean_squared_error

data = load_breast_cancer()
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.20, random_state=42)
clf = KNeighborsClassifier()
gridsearch = GridSearchCV(clf, {"n_neighbors": [1, 3, 5, 7, 9, 11], "weights": ['uniform', 'distance'], 
                                'p': [1, 2, 3]}, scoring='f1')

tfolkman

from sklearn.datasets import fetch_mldata
from sklearn.manifold import TSNE
from sklearn.decomposition import PCA
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt

# get mnist data
mnist = fetch_mldata("MNIST original")
X = mnist.data / 255.0

tfolkman

from sklearn.linear_model import SGDRegressor
linear_regression_model = SGDRegressor(tol=.0001, eta0=.01)
coeffs = []
for i, data in enumerate(bootstrap_X):
    linear_regression_model.fit(data, bootstrap_y[i])
    coeffs.append(linear_regression_model.coef_)

tfolkman

from sklearn.utils import resample
n_bootstraps = 1000
bootstrap_X = []
bootstrap_y = []
for _ in range(n_bootstraps):
    sample_X, sample_y = resample(scaled_df, target)
    bootstrap_X.append(sample_X)
    bootstrap_y.append(sample_y)

tfolkman

import pandas as pd
import swifter

df.swifter.apply(lambda x: x.sum() - x.min())

tfolkman

from collections import defaultdict
my_default_dict = defaultdict(int)
for letter in 'the red fox ran as fast as it could':
	my_default_dict[letter] += 1
print(my_default_dict)

tfolkman

from collections import Counter
ages = [22, 22, 25, 25, 30, 24, 26, 24, 35, 45, 52, 22, 22, 22, 25, 16, 11, 15, 40, 30]
value_counts = Counter(ages)
print(value_counts.most_common())

tfolkman

from collections import namedtuple
Features = namedtuple('Features', ['age', 'gender', 'name'])
row = Features(age=22, gender='male', name='Alex')
print(row.age)