vicotrbb

import matplotlib.pyplot as plt
import pandas as pd
import mplleaflet as mll

covid_info = pd.read_csv('covid19_df.csv')
covid_info.drop(['date', 'estimated_population_2019', 'confirmed_per_100k_inhabitants', 'death_rate'],
                axis=1, inplace=True)
covid_info = covid_info[covid_info.is_last == True]
covid_info.rename(columns={'city_ibge_code': 'codigo_ibge'}, inplace=True)

vicotrbb

covid_info = pd.read_csv('covid19_df.csv')
covid_info.drop(['date', 'estimated_population_2019', 'confirmed_per_100k_inhabitants', 'death_rate'],
                axis=1, inplace=True)
covid_info = covid_info[covid_info.is_last == True]
covid_info.rename(columns={'city_ibge_code': 'codigo_ibge'}, inplace=True)

cities_info = pd.read_csv('municipios.csv')
cities_info.drop(['nome', 'codigo_uf', 'capital'],
                 axis=1, inplace=True)

vicotrbb

import matplotlib.pyplot as plt
import pandas as pd
import mplleaflet as mll

vicotrbb

merged_df = pd.merge(left=covid_info, right=cities_info, left_on='codigo_ibge', right_on='codigo_ibge')
merged_df = merged_df[merged_df.place_type == 'city']

vicotrbb

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

# sklearn imports
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

vicotrbb

iris_dataset = load_iris()
iris = pd.DataFrame(iris_dataset.data, columns=iris_dataset.feature_names)

iris['target'] = iris_dataset.target

vicotrbb

x = pd.DataFrame(iris_dataset.data, columns=iris_dataset.feature_names)
y = pd.DataFrame(iris_dataset.target, columns=['target'])

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=5)

model = LinearRegression()

model.fit(x_train, y_train)

vicotrbb

train_predict = model.predict(x_train)
rmse = np.sqrt(mean_squared_error(y_train, train_predict))
r2 = r2_score(y_train, train_predict)

print('Conjunto de treino')
print(rmse)
print(r2)
np.round(train_predict, 0, train_predict)
scatter = plt.scatter(x_train['sepal length (cm)'], x_train['sepal width (cm)'], 
            c=train_predict, cmap=plt.cm.Set1,

vicotrbb

# Imports
from flask import Flask, request
from . import image

# Flask app
app = Flask(__name__)

@app.route('/image-handler', methods=['POST'])
def handler():
  return image.image_handler(image, methods)

vicotrbb

import cv2 # openCV
import numpy as np

def image_resize(image, width = None, height = None):
  img = cv2.imread(image)
  dim = None
  (h, w) = image.shape[:2]
  if width is None:
    r = height / float(h)
    dim = (int(w * r), height)