SaraM92

#import needed libraries
import statsmodels.api as sm
import numpy as np
import matplotlib.pyplot as plt
#set hypothesis parameters 
n = 1018
pnull = .52
phat = .56
#apply z test
sm.stats.proportions_ztest(phat * n, n, pnull, alternative='larger')

SaraM92

#Import needed libraries
import numpy as np
import scipy.stats
#build a function that calculates the confidence interval
def mean_confidence_interval(data, confidence=0.95):
    a = 1.0 * np.array(data)
    n = len(a)
    m, se = np.mean(a), scipy.stats.sem(a)
    h = se * scipy.stats.t.ppf((1 + confidence) / 2., n-1)
    return m, m-h, m+h

SaraM92

# for inline plots in jupyter
%matplotlib inline
# import matplotlib
import matplotlib.pyplot as plt
# import seaborn
import seaborn as sns
# settings for seaborn plotting style
sns.set(color_codes=True)
# settings for seaborn plot sizes
sns.set(rc={'figure.figsize':(5,5)})

SaraM92

#Import Pnadas to deal with datasets
import pandas as pd
#Dataset source
#https://www.kaggle.com/dipam7/student-grade-prediction
df = pd.read_csv('student-mat.csv')
df.head(3)
#student achieved 80% or higher as a final score
df['grade_A'] = np.where(df['G3']*5 >= 80, 1, 0)
#value of 1 if a student missed 10 or more classes
df['high_absenses'] = np.where(df['absences'] >= 10, 1, 0)

SaraM92

#Import needed libraries
import random
N = int(input("How many times do you want to play?"))               # no of times you will play the game
start_money= 10
money = start_money
for i in range(N):
    money -= 1                     # pay for the game
    black = random.randint(1, 6)   # throw black
    green = random.randint(1, 6)   # throw green
    if black > green:              # success?

SaraM92

from itertools import permutations, combination
#Getting all permutations of a particular length.
seq = permutations(['b','i','r', 't', 'h'], 5)
#print list of permutations
for p in list(seq):
   print(p)
   
#Getting all combination of a particular length.
combi = combinations(['b','i','r', 't', 'h'], 5)
#Print the list of combinations

SaraM92

#Import libraries to help simulate experimental probabilities
import random
from collections import Counter
#A function to genrate randome experiments
def gen(x):
    expResults = []
    for i in range(x):
        expResults.append(random.randint(1,6))
    return expResults
#An experiment with 100 events

SaraM92

#Import autoscraper
from autoscraper import AutoScraper
#A stackoverflow question we want to find questions related to
url = 'https://stackoverflow.com/questions/24512112/how-to-print-struct-variables-in-console'
#A list of a related thread to use as a guide to find similar questions
wanted_list = ["Assign one struct to another in C"]
#Collect similiar threads
scraper = AutoScraper()
result = scraper.build(url, wanted_list)
print(*result, sep="\n")

SaraM92

#Import needed libraries
import theano 
from theano import tensor 
#Define sigmoid function
def sigmoid(x):
  return 1 / (1 + tensor.exp(-x))
#Creating a symbolic variable
a = tensor.dmatrix('a') 
#Get sigmoid of a 
sig_a = sigmoid(a) 

SaraM92

#Example from LightGBM documentation
#Import needed libraries
import lightgbm as lgb
import pandas as pd
from sklearn.metrics import mean_squared_error

print('Loading data...')
# load or create your dataset
df_train = pd.read_csv('../regression/regression.train', header=None, sep='\t')
df_test = pd.read_csv('../regression/regression.test', header=None, sep='\t')