Monte-carlo-simulation

• https://towardsdatascience.com/how-to-use-monte-carlo-simulation-to-help-decision-making-a0a164bc8619

import random
import seaborn as sns
import matplotlib.pyplot as plt

iterations = 10000
variables = ['work', 'salary', 'conditions', 'career_advancement', 'social_impact', 'job_security', 'life_balance',
             'location', 'atmosphere', 'travel']


def Monte_Carlo(grade):
    final_results = []
    weight = [0.15, 0.09, 0.12, 0.11, 0.12, 0.02, 0.11, 0.07, 0.1, 0.11]
    for n in range(iterations):
        results = []
        for i in range(len(variables)):
            value = weight[i] * (random.uniform(grade[i][0], grade[i][1]))
            results.append(value)

        final_results.append(sum(results))
    return final_results


a = Monte_Carlo([[4, 9], [8.5, 10], [5, 9], [8.5, 9.5], [3, 7], [4, 9], [3, 8], [7.5, 8], [5, 9], [0, 6]])
b = Monte_Carlo([[5, 10], [4, 4], [7, 9], [2, 8], [6, 9.5], [8.5, 10], [8, 10], [0, 7], [3, 9], [0, 3]])
c = Monte_Carlo([[4, 7], [6, 8], [6, 9], [6.5, 9], [2, 6], [6.5, 9], [5.5, 9], [9.5, 9.5], [5, 9], [4, 9]])

# Plot the results
fig = plt.figure(figsize=(10, 6))
sns.distplot(a)
sns.distplot(b)
sns.distplot(c)
fig.legend(labels=['Job A', 'Job B', 'Job C'])
plt.title('Monte-Carlo Distributions')
plt.show()