Faisal Al-Tameemi FaisalAl-Tameemi

## interview.md

      
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    Coding Questions


Given an API response which returns a list of transactions, aggregate (sum) the transactions' amounts of a specific type

The aggregation function will run either daily or weekly, must return the correct response either way
How would you optimize this function?


API Endpoint: https://530a-5-107-84-27.ngrok.io/transactions


Given the same API method above, write a function which returns the counts of each transaction type


## benwillcabinets.service
[Unit]
Description=Ben Will Cabinets

[Service]
ExecStart=/var/www/construction-website/server.js
Restart=always
User=nobody
Group=nogroup
Environment=PATH=/usr/bin:/usr/local/bin
Environment=NODE_ENV=production

## build_directory.sh
set -e

# latest commit
LATEST_COMMIT=$(git rev-parse HEAD)

# latest commit where path/to/folder1 was changed
FOLDER1_COMMIT=$(git log -1 --format=format:%H --full-diff path/to/folder1)

# latest commit where path/to/folder2 was changed
FOLDER2_COMMIT=$(git log -1 --format=format:%H --full-diff path/to/folder2)

## results-xgboost-linear-reg.csv

          
            Model Name
             Training Score
            	Validation Score
            	Test Score

            
              NaiveModel (always predicts mean price)
              	0.000000
              	-0.000172
              	-0.000609

            
              RandomForestRegressor
              	0.711069
              	0.487592
              	0.489714

            
              PLSRegression
              	0.459397
              	0.445175
              	0.432827

            
              LinearRegression
              	0.534462
              	0.488490
              	0.487435

## train-test.py
def train_test(df, response_col='price', dummy_na=False, test_size=.3, rand_state=42, plot=False):
    #Split into explanatory and response variables
    X = df.drop(response_col, axis=1)
    y = df[response_col]

    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=rand_state)

    lm_model = LinearRegression(normalize=True)
    lm_model.fit(X_train, y_train)


## coeffecients-linear-model.csv

          
            Feature
            Coeffecient
             Absolute Coeffecient

            
              neighbourhood_Waterfront Communities-The Island
              82.96
              82.96

            
              neighbourhood_Beechborough-Greenbrook
              82.35
              82.35

            
              neighbourhood_Bay Street Corridor
              66.44
              66.44

            
              neighbourhood_Church-Yonge Corridor
              64.54
              64.54

            
              room_type_Shared room
              -64.34
              64.34

## most-expensive-price-per-bed-neighbourhoods-airbnb-toronto.csv

          
            Neighbourhood
            Average Listing Price ($)
            Average Price per Bed ($)

            
              Waterfront Communities-The Island
              191.83
              124.92

            
              Rosedale-Moore Park
              174.61
              117.98

            
              Niagara
              158.23
              114.36

            
              Bridle Path-Sunnybrook-York Mills
              166.33
              114.02

            
              Bay Street Corridor
              147.45
              107.69

## top-listings-count-airbnb-toronto.csv

          
            Neighbourhood
             Listings Count on AirBnB

            
              Waterfront Communities-The Island
              3093

            
              Niagara
              799

            
              Annex
              691

            
              Church-Yonge Corridor
              589

            
              Dovercourt-Wallace Emerson-Junction
              512

            
              Little Portugal
              493

            
              Bay Street Corridor
              491

            
              Trinity-Bellwoods
              449

            
              Kensington-Chinatown
              442

## most-expensive-neighbourhoods-airbnb-toronto.csv

          
            Neighbourhood
            Average Listing Price ($)
            Average Price per Bed ($)

            
              Waterfront Communities-The Island
              191.83
              124.92

            
              Rosedale-Moore Park
              174.61
              117.98

            
              Lawrence Park South
              168.72
              79.51

            
              Bridle Path-Sunnybrook-York Mills
              166.33
              114.02

            
              Lawrence Park North
              164.93
              68.55

## erc721-example.sol
pragma solidity ^0.4.19;
contract ERC721 {
   string constant private tokenName = "My ERC721 Token";
   string constant private tokenSymbol = "MET";
   uint256 constant private totalTokens = 1000000;
   mapping(address => uint) private balances;
   mapping(uint256 => address) private tokenOwners;
   mapping(uint256 => bool) private tokenExists;
   mapping(address => mapping (address => uint256)) private allowed;
   mapping(address => mapping(uint256 => uint256)) private ownerTokens;
	[Unit]
	Description=Ben Will Cabinets

	[Service]
	ExecStart=/var/www/construction-website/server.js
	Restart=always
	User=nobody
	Group=nogroup
	Environment=PATH=/usr/bin:/usr/local/bin
	Environment=NODE_ENV=production
	set -e

	# latest commit
	LATEST_COMMIT=$(git rev-parse HEAD)

	# latest commit where path/to/folder1 was changed
	FOLDER1_COMMIT=$(git log -1 --format=format:%H --full-diff path/to/folder1)

	# latest commit where path/to/folder2 was changed
	FOLDER2_COMMIT=$(git log -1 --format=format:%H --full-diff path/to/folder2)
Model Name	Training Score	Validation Score	Test Score
NaiveModel (always predicts mean price)	0.000000	-0.000172	-0.000609
RandomForestRegressor	0.711069	0.487592	0.489714
PLSRegression	0.459397	0.445175	0.432827
LinearRegression	0.534462	0.488490	0.487435
	def train_test(df, response_col='price', dummy_na=False, test_size=.3, rand_state=42, plot=False):
	#Split into explanatory and response variables
	X = df.drop(response_col, axis=1)
	y = df[response_col]

	X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=rand_state)

	lm_model = LinearRegression(normalize=True)
	lm_model.fit(X_train, y_train)
Feature	Coeffecient	Absolute Coeffecient
neighbourhood_Waterfront Communities-The Island	82.96	82.96
neighbourhood_Beechborough-Greenbrook	82.35	82.35
neighbourhood_Bay Street Corridor	66.44	66.44
neighbourhood_Church-Yonge Corridor	64.54	64.54
room_type_Shared room	-64.34	64.34
Neighbourhood	Average Listing Price ($)	Average Price per Bed ($)
Waterfront Communities-The Island	191.83	124.92
Rosedale-Moore Park	174.61	117.98
Niagara	158.23	114.36
Bridle Path-Sunnybrook-York Mills	166.33	114.02
Bay Street Corridor	147.45	107.69
	Neighbourhood	Listings Count on AirBnB
	Waterfront Communities-The Island	3093
	Niagara	799
	Annex	691
	Church-Yonge Corridor	589
	Dovercourt-Wallace Emerson-Junction	512
	Little Portugal	493
	Bay Street Corridor	491
	Trinity-Bellwoods	449
	Kensington-Chinatown	442
	pragma solidity ^0.4.19;
	contract ERC721 {
	string constant private tokenName = "My ERC721 Token";
	string constant private tokenSymbol = "MET";
	uint256 constant private totalTokens = 1000000;
	mapping(address => uint) private balances;
	mapping(uint256 => address) private tokenOwners;
	mapping(uint256 => bool) private tokenExists;
	mapping(address => mapping (address => uint256)) private allowed;
	mapping(address => mapping(uint256 => uint256)) private ownerTokens;