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# imports
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
# generate random data-set
np.random.seed(0)
x = np.random.rand(100, 1)
y = 2 + 3 * x + np.random.rand(100, 1)
{
"variables": [],
"info": {
"name": "Elastic Search Requests",
"description": "",
"schema": "https://schema.getpostman.com/json/collection/v2.0.0/collection.json"
},
"item": [
{
"name": "Adding documents to index",