gabriel19913

from sklearn.decomposition import PCA

pca = PCA(15, random_state = seed)
pca.fit(scaled_X)
variance = pca.explained_variance_ratio_
var=np.cumsum(np.round(pca.explained_variance_ratio_, decimals=7)*100)

gabriel19913

import matplotlib.pyplot as plt
import seaborn as sns

plt.figure(figsize=(10,10))
sns.set(font_scale=1.5)
ax=sns.heatmap(df.corr(), cmap = "RdBu_r")
bottom, top = ax.get_ylim()
ax.set_ylim(bottom + 0.5, top - 0.5)
plt.tight_layout()
plt.savefig('pearson.png')

gabriel19913

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaled_X = scaler.fit_transform(X)

gabriel19913

Collecting bottleneck
  Using cached https://files.pythonhosted.org/packages/05/ae/cedf5323f398ab4e4ff92d6c431a3e1c6a186f9b41ab3e8258dff786a290/Bottleneck-1.2.1.tar.gz
Requirement already satisfied: numpy in c:\users\gabri\anaconda3\lib\site-packages (from bottleneck) (1.16.0)
Building wheels for collected packages: bottleneck
  Building wheel for bottleneck (setup.py) ... error
  ERROR: Complete output from command 'C:\Users\gabri\Anaconda3\python.exe' -u -c 'import setuptools, tokenize;__file__='"'"'C:\\Users\\gabri\\AppData\\Local\\Temp\\pip-install-6t52qrhz\\bottleneck\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' bdist_wheel -d 'C:\Users\gabri\AppData\Local\Temp\pip-wheel-jglc7sxb' --python-tag cp37:
  ERROR: running bdist_wheel
  running build
  running build_py
  creating build

gabriel19913

# For Windows users# Note: <> denotes changes to be made

#Create a conda environment
conda create --name <environment-name> python=<version:2.7/3.5>

#To create a requirements.txt file:
conda list #Gives you list of packages used for the environment

conda list -e > requirements.txt #Save all the info about packages to your folder

gabriel19913

from sklearn.datasets import load_wine
from sklearn.utils import shuffle
import numpy as np
from sklearn.model_selection import KFold
from sklearn.preprocessing import scale
from sklearn import tree
from sklearn.metrics import accuracy_score
from sklearn.model_selection import cross_val_score
from sklearn.metrics import accuracy_score

gabriel19913

clear all
clc
x1 = -0.25 + 0.08 * randn(1, 10);
x2 = 0.5 + 0.08 * randn(1, 10);
C1 = [x1;x2];

x1 = 0.7 + 0.08 * randn(1, 10);
x2 = 0.25 + 0.08 * randn(1, 10);
C2 = [x1;x2];

gabriel19913

C1 = np.random.multivariate_normal(np.array([5, 5]), np.array([[1, 0],[0, 1]]), 500)
C2 = np.random.multivariate_normal(np.array([0, 0]), np.array([[1, 0],[0, 1]]), 500)

data1 = pd.DataFrame({'X1':C1[:,0],'X2':C1[:,1]})
data1['Class'] = np.full((500, 1), 'C1')
data2 = pd.DataFrame({'X1':C2[:,0],'X2':C2[:,1]})
data1['Class'] = np.full((500, 1), 'C2')

data = pd.concat([data1, data2], sort=True)

gabriel19913

%%time
#Importing libraries
import pandas as pd
import json as JSON
from json import load
import numpy as np
import requests
import matplotlib.pyplot as plt
import seaborn as sns
import time

gabriel19913

rom selenium import webdriver
from bs4 import BeautifulSoup
import csv

browser = webdriver.Chrome('C:\\Users\\100pau\\workspace\\chromedriver.exe')

browser.get('https://www.centauro.com.br/capacete-peels-mirage-storm-preto-e-verde-fosco-m00w4e-mktp.html?cor=34')

html = browser.execute_script("return document.getElementsByTagName('html')[0].innerHTML")
soup = BeautifulSoup(html, 'html.parser')