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from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures

x = [[1],[4],[7],[13],[10]]
# Y1 = 10 + 6*x
y1 = [16, 34, 52, 88, 70]
# Y2 = x*x = x^2
y2 = [1, 16, 49, 169, 100]

# This will convert X into 

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from sklearn import svm

x = [[1],[4],[7],[13],[10]]
y1 = [16, 34, 52, 88, 70]
y2 = [1, 16, 49, 169, 100]

svm_regression_model = svm.SVR(kernel='poly')
svm_regression_model.fit(x,y1)
print svm_regression_model.predict([5])

gr33ndata

# Bayesian Analysis for A/B Experiment with binart goals

import matplotlib
import matplotlib.pyplot as plt

import numpy as np
import scipy as sp
import pandas as pd

def bayesian_analysis(events_a, events_b, successes_a, successes_b, 

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# Better run this in a Jupyer notebook
import numpy as np

p = 0.75
passes = np.random.binomial(n=1, p=p, size=1000)

# Check Mean and Std for the generated data
passes.mean().round(3), passes.std().round(3)

# Take random 1000 x 10 passes (with replacement)

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# Better run this in a Jupyter notebook

import numpy as np
import pandas as pd

green = [32, 34, 38, 28, 32, 34, 38, 28, 33, 50, 32, 39, 29]
red = [33, 32, 39, 29, 33, 32, 39, 29, 33, 8, 32, 39, 29]

green = pd.Series(green)
red = pd.Series(red)