drigols

import pandas as pd
pd.set_option('display.max_columns', 18)

data = pd.read_csv('../datasets/athlete_events.csv')
dt = data.dropna()

print("Full sample: {0}".format(data.shape))
print("Sample without NaN: {0}".format(dt.shape))

drigols

import pandas as pd
pd.set_option('display.max_columns', 18)

data = pd.read_csv('../datasets/athlete_events.csv')
dt = data.dropna()

print(dt.head())

drigols

import pandas as pd
pd.set_option('display.max_columns', 18)

data = pd.read_csv('../datasets/athlete_events.csv')

print(data.head())
print(data.dtypes)

drigols

import pandas as pd
pd.set_option('display.max_columns', 42)

data = pd.read_csv('../datasets/2015-building-energy-benchmarking.csv')

data['DataYear'] = data['DataYear'].astype(object)
print(data.dtypes)

drigols

import pandas as pd
pd.set_option('display.max_columns', 42)

data = pd.read_csv('../datasets/2015-building-energy-benchmarking.csv')
print(data.dtypes)

drigols

import pandas as pd
pd.set_option('display.max_columns', 42)

data = pd.read_csv('../datasets/2015-building-energy-benchmarking.csv')
print(data.head())

drigols

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from matplotlib import pyplot as plt

diameterPassed = float(input("What's the diameter(cm) of the pizza you want? "))

diameters = [[7], [10], [15], [30], [45], [13], [60], [100], [5], [30], [90], [18], [70], [110], [25]]
prices   = [[8], [11], [16], [38.5], [52], [14], [70], [90], [6], [38.5], [102], [20], [85], [100], [34]]

model = LinearRegression()

drigols

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from matplotlib import pyplot as plt


diameter = [[7], [10], [15], [30], [45], [13], [60], [100], [5], [30], [90], [18], [70], [110], [25]]
prices   = [[8], [11], [16], [38.5], [52], [14], [70], [90], [6], [38.5], [102], [20], [85], [100], [34]]

model = LinearRegression()

drigols

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from matplotlib import pyplot as plt
import pandas as pd

pd.set_option('display.max_columns', 21)
df = pd.read_csv('../datasets/kc_house_data.csv')
df = df.drop(['id', 'date', 'zipcode', 'lat', 'long'], axis=1)

y = df['price']

drigols

"""
R-Squared or Coefficient of Determination
"""

def createRegression(samples,variavel_numbers, n_noise):
  from sklearn.datasets import make_regression
  x, y = make_regression(n_samples=samples, n_features=variavel_numbers, noise=n_noise)
  return x, y

if __name__ =='__main__':