UMU18

import pandas as pd
import numpy as np

class StockPrices:
    # param prices dict of string to list. A dictionary containing the tickers of the stocks, and each tickers daily prices.
    # returns list of strings. A list containing the tickers of the two most correlated stocks.
    @staticmethod
    def most_corr(prices):
        data=pd.DataFrame.from_dict(prices)
        au_corr=data.corr()

UMU18

import numpy as np
from sklearn import linear_model

class MarketingCosts:

    # param marketing_expenditure list. Expenditure for each previous campaign.
    # param units_sold list. The number of units sold for each previous campaign.
    # param desired_units_sold int. Target number of units to sell in the new campaign.
    # returns float. Required amount of money to be invested.
    @staticmethod

UMU18

SELECT name
FROM employees
where id NOT IN (
    SELECT managerId FROM employees where managerId is not null
)

UMU18

SELECT COUNT(id) FROM students where firstName='John'

UMU18

test case for deposit-interest (https://www.testdome.com/questions/quality-assurance/deposit-interest/19735?visibility=17&skillId=83)

#Test Case       |      DEPOSIT      |      AGE      |   INTEREST RATE
_________________|___________________|_______________|__________________
    1            |        99         |      18       |    Unavailable
    2            |        10001      |      18       |    Unavailable
    3            |        100        |      18       |    1.0%
    4            |        999        |      18       |    1.0%
    5            |        1000       |      18       |    1.3%
    6            |        4999       |      18       |    1.3%

UMU18

<?php
//answer for https://www.testdome.com/questions/php/file-owners/11840?visibility=17&skillId=5
class FileOwners
{
    public static function groupByOwners($files)
    {
        $result=array();
        foreach($files as $key=>$value)
        {
            $result[$value][]=$key;