EDA
2024-09-24
hundred_sales <- read.csv('https://raw.githubusercontent.com/Kingtilon1/MachineLearning-BigData/refs/heads/main/100%20Sales%20Records.csv')
thousand_sales <- read.csv('https://raw.githubusercontent.com/Kingtilon1/MachineLearning-BigData/refs/heads/main/1000%20Sales%20Records.csv')EDA
Lets explore the data to gain some insights This snippet provides an overview of the dataset by showing the first few rows, including column names and sample data. Let’s examine the dataset’s structure to understand the data types and column names:
head(hundred_sales)## Region Country Item.Type
## 1 Australia and Oceania Tuvalu Baby Food
## 2 Central America and the Caribbean Grenada Cereal
## 3 Europe Russia Office Supplies
## 4 Sub-Saharan Africa Sao Tome and Principe Fruits
## 5 Sub-Saharan Africa Rwanda Office Supplies
## 6 Australia and Oceania Solomon Islands Baby Food
## Sales.Channel Order.Priority Order.Date Order.ID Ship.Date Units.Sold
## 1 Offline H 5/28/2010 669165933 6/27/2010 9925
## 2 Online C 8/22/2012 963881480 9/15/2012 2804
## 3 Offline L 5/2/2014 341417157 5/8/2014 1779
## 4 Online C 6/20/2014 514321792 7/5/2014 8102
## 5 Offline L 2/1/2013 115456712 2/6/2013 5062
## 6 Online C 2/4/2015 547995746 2/21/2015 2974
## Unit.Price Unit.Cost Total.Revenue Total.Cost Total.Profit
## 1 255.28 159.42 2533654.00 1582243.50 951410.50
## 2 205.70 117.11 576782.80 328376.44 248406.36
## 3 651.21 524.96 1158502.59 933903.84 224598.75
## 4 9.33 6.92 75591.66 56065.84 19525.82
## 5 651.21 524.96 3296425.02 2657347.52 639077.50
## 6 255.28 159.42 759202.72 474115.08 285087.64head(thousand_sales)## Region Country Item.Type Sales.Channel Order.Priority
## 1 Middle East and North Africa Libya Cosmetics Offline M
## 2 North America Canada Vegetables Online M
## 3 Middle East and North Africa Libya Baby Food Offline C
## 4 Asia Japan Cereal Offline C
## 5 Sub-Saharan Africa Chad Fruits Offline H
## 6 Europe Armenia Cereal Online H
## Order.Date Order.ID Ship.Date Units.Sold Unit.Price Unit.Cost Total.Revenue
## 1 10/18/2014 686800706 10/31/2014 8446 437.20 263.33 3692591.20
## 2 11/7/2011 185941302 12/8/2011 3018 154.06 90.93 464953.08
## 3 10/31/2016 246222341 12/9/2016 1517 255.28 159.42 387259.76
## 4 4/10/2010 161442649 5/12/2010 3322 205.70 117.11 683335.40
## 5 8/16/2011 645713555 8/31/2011 9845 9.33 6.92 91853.85
## 6 11/24/2014 683458888 12/28/2014 9528 205.70 117.11 1959909.60
## Total.Cost Total.Profit
## 1 2224085.2 1468506.02
## 2 274426.7 190526.34
## 3 241840.1 145419.62
## 4 389039.4 294295.98
## 5 68127.4 23726.45
## 6 1115824.1 844085.52There are no missing values within both data sets
The average amount of units sold within the smaller dataset are 5128.71 units while the average for the larger dataset is 5053.9888. The larger dataset may include more polorizing numbers within the ’Unites sold column, causing the the average to be lower than the smaller dataset”
str(hundred_sales)## 'data.frame': 100 obs. of 14 variables:
## $ Region : chr "Australia and Oceania" "Central America and the Caribbean" "Europe" "Sub-Saharan Africa" ...
## $ Country : chr "Tuvalu" "Grenada" "Russia" "Sao Tome and Principe" ...
## $ Item.Type : chr "Baby Food" "Cereal" "Office Supplies" "Fruits" ...
## $ Sales.Channel : chr "Offline" "Online" "Offline" "Online" ...
## $ Order.Priority: chr "H" "C" "L" "C" ...
## $ Order.Date : chr "5/28/2010" "8/22/2012" "5/2/2014" "6/20/2014" ...
## $ Order.ID : int 669165933 963881480 341417157 514321792 115456712 547995746 135425221 871543967 770463311 616607081 ...
## $ Ship.Date : chr "6/27/2010" "9/15/2012" "5/8/2014" "7/5/2014" ...
## $ Units.Sold : int 9925 2804 1779 8102 5062 2974 4187 8082 6070 6593 ...
## $ Unit.Price : num 255.28 205.7 651.21 9.33 651.21 ...
## $ Unit.Cost : num 159.42 117.11 524.96 6.92 524.96 ...
## $ Total.Revenue : num 2533654 576783 1158503 75592 3296425 ...
## $ Total.Cost : num 1582244 328376 933904 56066 2657348 ...
## $ Total.Profit : num 951411 248406 224599 19526 639078 ...str(thousand_sales)## 'data.frame': 1000 obs. of 14 variables:
## $ Region : chr "Middle East and North Africa" "North America" "Middle East and North Africa" "Asia" ...
## $ Country : chr "Libya" "Canada" "Libya" "Japan" ...
## $ Item.Type : chr "Cosmetics" "Vegetables" "Baby Food" "Cereal" ...
## $ Sales.Channel : chr "Offline" "Online" "Offline" "Offline" ...
## $ Order.Priority: chr "M" "M" "C" "C" ...
## $ Order.Date : chr "10/18/2014" "11/7/2011" "10/31/2016" "4/10/2010" ...
## $ Order.ID : int 686800706 185941302 246222341 161442649 645713555 683458888 679414975 208630645 266467225 118598544 ...
## $ Ship.Date : chr "10/31/2014" "12/8/2011" "12/9/2016" "5/12/2010" ...
## $ Units.Sold : int 8446 3018 1517 3322 9845 9528 2844 7299 2428 4800 ...
## $ Unit.Price : num 437.2 154.06 255.28 205.7 9.33 ...
## $ Unit.Cost : num 263.33 90.93 159.42 117.11 6.92 ...
## $ Total.Revenue : num 3692591 464953 387260 683335 91854 ...
## $ Total.Cost : num 2224085 274427 241840 389039 68127 ...
## $ Total.Profit : num 1468506 190526 145420 294296 23726 ...skim(hundred_sales)| Name | hundred_sales |
| Number of rows | 100 |
| Number of columns | 14 |
| _______________________ | |
| Column type frequency: | |
| character | 7 |
| numeric | 7 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| Region | 0 | 1 | 4 | 33 | 0 | 7 | 0 |
| Country | 0 | 1 | 4 | 32 | 0 | 76 | 0 |
| Item.Type | 0 | 1 | 4 | 15 | 0 | 12 | 0 |
| Sales.Channel | 0 | 1 | 6 | 7 | 0 | 2 | 0 |
| Order.Priority | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| Order.Date | 0 | 1 | 8 | 10 | 0 | 100 | 0 |
| Ship.Date | 0 | 1 | 8 | 10 | 0 | 99 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Order.ID | 0 | 1 | 555020412.36 | 260615257.13 | 114606559.00 | 338922488.00 | 557708561.00 | 790755080.75 | 994022214.00 | ▇▆▆▇▇ |
| Units.Sold | 0 | 1 | 5128.71 | 2794.48 | 124.00 | 2836.25 | 5382.50 | 7369.00 | 9925.00 | ▆▆▇▆▆ |
| Unit.Price | 0 | 1 | 276.76 | 235.59 | 9.33 | 81.73 | 179.88 | 437.20 | 668.27 | ▇▅▁▃▅ |
| Unit.Cost | 0 | 1 | 191.05 | 188.21 | 6.92 | 35.84 | 107.28 | 263.33 | 524.96 | ▇▂▂▁▃ |
| Total.Revenue | 0 | 1 | 1373487.68 | 1460028.71 | 4870.26 | 268721.21 | 752314.36 | 2212044.68 | 5997054.98 | ▇▂▂▁▁ |
| Total.Cost | 0 | 1 | 931805.70 | 1083938.25 | 3612.24 | 168868.03 | 363566.38 | 1613869.72 | 4509793.96 | ▇▁▂▁▁ |
| Total.Profit | 0 | 1 | 441681.98 | 438537.91 | 1258.02 | 121443.58 | 290768.00 | 635828.80 | 1719922.04 | ▇▃▂▁▁ |
skim(thousand_sales)| Name | thousand_sales |
| Number of rows | 1000 |
| Number of columns | 14 |
| _______________________ | |
| Column type frequency: | |
| character | 7 |
| numeric | 7 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| Region | 0 | 1 | 4 | 33 | 0 | 7 | 0 |
| Country | 0 | 1 | 4 | 32 | 0 | 185 | 0 |
| Item.Type | 0 | 1 | 4 | 15 | 0 | 12 | 0 |
| Sales.Channel | 0 | 1 | 6 | 7 | 0 | 2 | 0 |
| Order.Priority | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
| Order.Date | 0 | 1 | 8 | 10 | 0 | 841 | 0 |
| Ship.Date | 0 | 1 | 8 | 10 | 0 | 835 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Order.ID | 0 | 1 | 549681324.74 | 257133358.84 | 1.02928e+08 | 328074026.00 | 556609713.50 | 769694482.75 | 995529830.00 | ▇▇▇▇▇ |
| Units.Sold | 0 | 1 | 5053.99 | 2901.38 | 1.30000e+01 | 2420.25 | 5184.00 | 7536.75 | 9998.00 | ▇▆▇▇▇ |
| Unit.Price | 0 | 1 | 262.11 | 216.02 | 9.33000e+00 | 81.73 | 154.06 | 421.89 | 668.27 | ▇▇▁▃▃ |
| Unit.Cost | 0 | 1 | 184.97 | 175.29 | 6.92000e+00 | 56.67 | 97.44 | 263.33 | 524.96 | ▇▂▁▁▂ |
| Total.Revenue | 0 | 1 | 1327321.84 | 1486514.56 | 2.04325e+03 | 281191.90 | 754939.18 | 1733502.75 | 6617209.54 | ▇▂▁▁▁ |
| Total.Cost | 0 | 1 | 936119.23 | 1162570.75 | 1.41675e+03 | 164931.88 | 464726.06 | 1141750.09 | 5204978.40 | ▇▁▁▁▁ |
| Total.Profit | 0 | 1 | 391202.61 | 383640.19 | 5.32610e+02 | 98376.12 | 277225.98 | 548456.84 | 1726181.36 | ▇▃▂▁▁ |
Both Data frames share the same columns
colnames(hundred_sales)## [1] "Region" "Country" "Item.Type" "Sales.Channel"
## [5] "Order.Priority" "Order.Date" "Order.ID" "Ship.Date"
## [9] "Units.Sold" "Unit.Price" "Unit.Cost" "Total.Revenue"
## [13] "Total.Cost" "Total.Profit"colnames(thousand_sales)## [1] "Region" "Country" "Item.Type" "Sales.Channel"
## [5] "Order.Priority" "Order.Date" "Order.ID" "Ship.Date"
## [9] "Units.Sold" "Unit.Price" "Unit.Cost" "Total.Revenue"
## [13] "Total.Cost" "Total.Profit"Now, let’s visualize a correlation matrix for the numerical columns in the dataset. This will help identify pairs of columns that are highly correlated. We see that Total.Revenue, Total.Cost, and Total.Profit have high correlations, there’s also a high correlation between those values and Units.Sold and Units.Price
thousand_sales_num <- thousand_sales %>% select(where(is.numeric))
thousand_sales_num.cor = cor(thousand_sales_num)
corrplot(thousand_sales_num.cor)
Lets do the same with the smaller dataset Similar to the larger dataset, the data set has a high correlation between Total.Revenue, Total.Cost, and Total.Profit, there’s also a high correlation between those values and Units.Sold and Units.Price
hundred_sales_num <- hundred_sales %>% select(where(is.numeric))
hundred_sales_num.cor = cor(hundred_sales_num)
corrplot(hundred_sales_num.cor)
Which unit item has the highest average price?
average_price <- hundred_sales %>%
group_by(Item.Type) %>%
summarise(Average.Unit.Price = mean(Unit.Price)) %>%
arrange(desc(Average.Unit.Price))
# Print the result
print(average_price)## # A tibble: 12 × 2
## Item.Type Average.Unit.Price
## <chr> <dbl>
## 1 Household 668.
## 2 Office Supplies 651.
## 3 Cosmetics 437.
## 4 Meat 422.
## 5 Baby Food 255.
## 6 Cereal 206.
## 7 Vegetables 154.
## 8 Snacks 153.
## 9 Clothes 109.
## 10 Personal Care 81.7
## 11 Beverages 47.4
## 12 Fruits 9.33It’s clear that Household items have the average Unit price
Linear Regression prep
Since the item type seems like it will have a significant impact on total revenue I will perform one hot encoding on the Item.Type column for both dataframes
encoded_data <- model.matrix(~ Item.Type - 1, data = thousand_sales)
encoded_df <- as.data.frame(encoded_data)
thousand_sales <- bind_cols(thousand_sales, encoded_df)
thousand_sales_num <- thousand_sales %>% select(where(is.numeric))
print(thousand_sales)## Region Country
## 1 Middle East and North Africa Libya
## 2 North America Canada
## 3 Middle East and North Africa Libya
## 4 Asia Japan
## 5 Sub-Saharan Africa Chad
## 6 Europe Armenia
## 7 Sub-Saharan Africa Eritrea
## 8 Europe Montenegro
## 9 Central America and the Caribbean Jamaica
## 10 Australia and Oceania Fiji
## 11 Sub-Saharan Africa Togo
## 12 Europe Montenegro
## 13 Europe Greece
## 14 Sub-Saharan Africa Sudan
## 15 Asia Maldives
## 16 Europe Montenegro
## 17 Europe Estonia
## 18 North America Greenland
## 19 Sub-Saharan Africa Cape Verde
## 20 Sub-Saharan Africa Senegal
## 21 Australia and Oceania Federated States of Micronesia
## 22 Europe Bulgaria
## 23 Middle East and North Africa Algeria
## 24 Asia Mongolia
## 25 Central America and the Caribbean Grenada
## 26 Central America and the Caribbean Grenada
## 27 Sub-Saharan Africa Senegal
## 28 North America Greenland
## 29 Sub-Saharan Africa Chad
## 30 Sub-Saharan Africa Mauritius
## 31 Middle East and North Africa Morocco
## 32 Central America and the Caribbean Honduras
## 33 Sub-Saharan Africa Benin
## 34 Europe Greece
## 35 Central America and the Caribbean Jamaica
## 36 Sub-Saharan Africa Equatorial Guinea
## 37 Sub-Saharan Africa Swaziland
## 38 Central America and the Caribbean Trinidad and Tobago
## 39 Europe Sweden
## 40 Europe Belarus
## 41 Sub-Saharan Africa Guinea-Bissau
## 42 Asia Mongolia
## 43 Middle East and North Africa Turkey
## 44 Sub-Saharan Africa Central African Republic
## 45 Sub-Saharan Africa Equatorial Guinea
## 46 Asia Laos
## 47 Europe Armenia
## 48 Europe Greece
## 49 Middle East and North Africa Israel
## 50 Asia Bhutan
## 51 Australia and Oceania Vanuatu
## 52 Sub-Saharan Africa Burundi
## 53 Europe Ukraine
## 54 Europe Croatia
## 55 Sub-Saharan Africa Madagascar
## 56 Asia Malaysia
## 57 Asia Uzbekistan
## 58 Europe Italy
## 59 Asia Nepal
## 60 Australia and Oceania Fiji
## 61 Europe Portugal
## 62 Central America and the Caribbean Panama
## 63 Europe Belarus
## 64 Sub-Saharan Africa Botswana
## 65 Sub-Saharan Africa Tanzania
## 66 Europe Romania
## 67 Sub-Saharan Africa Mali
## 68 Sub-Saharan Africa Central African Republic
## 69 Sub-Saharan Africa Niger
## 70 Europe Austria
## 71 Asia India
## 72 Europe Luxembourg
## 73 Sub-Saharan Africa Cape Verde
## 74 Europe Sweden
## 75 Europe Iceland
## 76 Middle East and North Africa Qatar
## 77 Sub-Saharan Africa South Sudan
## 78 Europe United Kingdom
## 79 Middle East and North Africa Tunisia
## 80 North America United States of America
## 81 Sub-Saharan Africa Liberia
## 82 Sub-Saharan Africa Eritrea
## 83 Asia South Korea
## 84 Sub-Saharan Africa Kenya
## 85 Sub-Saharan Africa Rwanda
## 86 Central America and the Caribbean Cuba
## 87 Middle East and North Africa Libya
## 88 Europe Czech Republic
## 89 Europe Montenegro
## 90 Europe Montenegro
## 91 Asia Philippines
## 92 Central America and the Caribbean El Salvador
## 93 Australia and Oceania Tonga
## 94 Sub-Saharan Africa Democratic Republic of the Congo
## 95 Middle East and North Africa Afghanistan
## 96 Australia and Oceania Tuvalu
## 97 Sub-Saharan Africa Sudan
## 98 Sub-Saharan Africa Niger
## 99 Sub-Saharan Africa Gabon
## 100 Australia and Oceania East Timor
## 101 North America United States of America
## 102 Middle East and North Africa Jordan
## 103 Europe Cyprus
## 104 Sub-Saharan Africa Malawi
## 105 Europe Iceland
## 106 Middle East and North Africa Israel
## 107 Middle East and North Africa United Arab Emirates
## 108 Asia China
## 109 Sub-Saharan Africa Kenya
## 110 Middle East and North Africa Somalia
## 111 Australia and Oceania Tonga
## 112 Asia Bangladesh
## 113 Middle East and North Africa Egypt
## 114 Sub-Saharan Africa Eritrea
## 115 Sub-Saharan Africa Swaziland
## 116 Asia Vietnam
## 117 Australia and Oceania Marshall Islands
## 118 Asia Taiwan
## 119 Europe Ireland
## 120 Sub-Saharan Africa Rwanda
## 121 Europe Sweden
## 122 Sub-Saharan Africa Gabon
## 123 Sub-Saharan Africa South Africa
## 124 Europe United Kingdom
## 125 Europe Albania
## 126 Asia Malaysia
## 127 Sub-Saharan Africa Ghana
## 128 Central America and the Caribbean Cuba
## 129 Central America and the Caribbean Saint Lucia
## 130 Europe Romania
## 131 Europe Portugal
## 132 Europe Macedonia
## 133 Asia China
## 134 Europe Germany
## 135 Europe Ireland
## 136 Europe Poland
## 137 Sub-Saharan Africa Namibia
## 138 Asia Uzbekistan
## 139 Sub-Saharan Africa Zimbabwe
## 140 Asia Mongolia
## 141 Europe Norway
## 142 Middle East and North Africa Oman
## 143 Europe Serbia
## 144 Sub-Saharan Africa Democratic Republic of the Congo
## 145 Europe Bulgaria
## 146 Asia Brunei
## 147 Europe Serbia
## 148 Sub-Saharan Africa Ghana
## 149 Sub-Saharan Africa Malawi
## 150 Sub-Saharan Africa Zimbabwe
## 151 Europe Cyprus
## 152 Central America and the Caribbean Nicaragua
## 153 Europe Estonia
## 154 Europe Estonia
## 155 Europe Lithuania
## 156 Sub-Saharan Africa Republic of the Congo
## 157 Europe Czech Republic
## 158 Sub-Saharan Africa Cameroon
## 159 Asia Vietnam
## 160 Europe Moldova
## 161 Middle East and North Africa Bahrain
## 162 Europe Hungary
## 163 Australia and Oceania Marshall Islands
## 164 Middle East and North Africa Iraq
## 165 Europe Albania
## 166 Sub-Saharan Africa Lesotho
## 167 Middle East and North Africa Lebanon
## 168 Europe Hungary
## 169 Asia Japan
## 170 Europe Georgia
## 171 Europe Estonia
## 172 Europe Luxembourg
## 173 Sub-Saharan Africa Swaziland
## 174 Europe Romania
## 175 Sub-Saharan Africa Ethiopia
## 176 Sub-Saharan Africa Chad
## 177 Middle East and North Africa Morocco
## 178 North America Mexico
## 179 Sub-Saharan Africa Nigeria
## 180 Central America and the Caribbean Trinidad and Tobago
## 181 Europe Moldova
## 182 Australia and Oceania Solomon Islands
## 183 Asia India
## 184 Sub-Saharan Africa Burkina Faso
## 185 Australia and Oceania Kiribati
## 186 Middle East and North Africa Israel
## 187 Sub-Saharan Africa Comoros
## 188 Middle East and North Africa Iran
## 189 Asia Vietnam
## 190 Central America and the Caribbean Belize
## 191 Europe Belarus
## 192 North America United States of America
## 193 Europe Poland
## 194 North America Canada
## 195 Middle East and North Africa Israel
## 196 Middle East and North Africa Lebanon
## 197 Europe Andorra
## 198 Europe Slovakia
## 199 Sub-Saharan Africa Liberia
## 200 Central America and the Caribbean Antigua and Barbuda
## 201 Asia China
## 202 Sub-Saharan Africa Niger
## 203 Europe United Kingdom
## 204 Asia Bangladesh
## 205 Asia Myanmar
## 206 Australia and Oceania Tonga
## 207 Sub-Saharan Africa Guinea-Bissau
## 208 Australia and Oceania Nauru
## 209 Sub-Saharan Africa Swaziland
## 210 Europe Finland
## 211 Australia and Oceania Papua New Guinea
## 212 Sub-Saharan Africa Mauritius
## 213 Sub-Saharan Africa Mozambique
## 214 Europe Bulgaria
## 215 Europe Spain
## 216 Australia and Oceania Vanuatu
## 217 Europe Belgium
## 218 Europe Belgium
## 219 Sub-Saharan Africa Guinea-Bissau
## 220 Sub-Saharan Africa Togo
## 221 Sub-Saharan Africa Cote d'Ivoire
## 222 Sub-Saharan Africa Republic of the Congo
## 223 Middle East and North Africa Libya
## 224 Australia and Oceania East Timor
## 225 Europe Switzerland
## 226 Australia and Oceania Palau
## 227 Middle East and North Africa Jordan
## 228 Europe Slovenia
## 229 Asia South Korea
## 230 Europe Norway
## 231 Middle East and North Africa Afghanistan
## 232 Asia Bangladesh
## 233 Sub-Saharan Africa Guinea
## 234 Central America and the Caribbean Cuba
## 235 Europe Russia
## 236 Sub-Saharan Africa Seychelles
## 237 Asia South Korea
## 238 Sub-Saharan Africa Ghana
## 239 Central America and the Caribbean Costa Rica
## 240 Europe Romania
## 241 Europe Czech Republic
## 242 Europe Liechtenstein
## 243 Sub-Saharan Africa Seychelles
## 244 Middle East and North Africa Somalia
## 245 Australia and Oceania Solomon Islands
## 246 Sub-Saharan Africa Uganda
## 247 Sub-Saharan Africa Equatorial Guinea
## 248 Central America and the Caribbean Costa Rica
## 249 Europe Moldova
## 250 Sub-Saharan Africa Burkina Faso
## 251 Central America and the Caribbean Guatemala
## 252 Sub-Saharan Africa Swaziland
## 253 Asia Maldives
## 254 Asia Thailand
## 255 Sub-Saharan Africa Sudan
## 256 Central America and the Caribbean Costa Rica
## 257 Europe Denmark
## 258 Sub-Saharan Africa Angola
## 259 Australia and Oceania Papua New Guinea
## 260 Asia North Korea
## 261 Central America and the Caribbean El Salvador
## 262 Sub-Saharan Africa Burkina Faso
## 263 Middle East and North Africa Yemen
## 264 Sub-Saharan Africa Republic of the Congo
## 265 Europe Andorra
## 266 Central America and the Caribbean Dominican Republic
## 267 Middle East and North Africa Israel
## 268 Australia and Oceania Solomon Islands
## 269 Sub-Saharan Africa Liberia
## 270 Sub-Saharan Africa Mali
## 271 Asia Uzbekistan
## 272 Middle East and North Africa Tunisia
## 273 Europe Vatican City
## 274 Sub-Saharan Africa Djibouti
## 275 Europe Ukraine
## 276 Australia and Oceania East Timor
## 277 Sub-Saharan Africa Uganda
## 278 Sub-Saharan Africa Guinea
## 279 Sub-Saharan Africa Equatorial Guinea
## 280 Europe Malta
## 281 Europe Cyprus
## 282 Europe Czech Republic
## 283 Middle East and North Africa Libya
## 284 Asia Vietnam
## 285 Middle East and North Africa Jordan
## 286 Sub-Saharan Africa Mali
## 287 Europe Czech Republic
## 288 Europe Slovakia
## 289 Sub-Saharan Africa Zimbabwe
## 290 Central America and the Caribbean Honduras
## 291 Europe Switzerland
## 292 Sub-Saharan Africa South Africa
## 293 Sub-Saharan Africa Uganda
## 294 Middle East and North Africa Iran
## 295 Middle East and North Africa Algeria
## 296 Sub-Saharan Africa Central African Republic
## 297 Central America and the Caribbean The Bahamas
## 298 Sub-Saharan Africa South Africa
## 299 Sub-Saharan Africa Benin
## 300 Europe Hungary
## 301 Europe Austria
## 302 Asia Tajikistan
## 303 Europe Portugal
## 304 Europe Belgium
## 305 Europe Slovenia
## 306 Europe Czech Republic
## 307 Australia and Oceania Marshall Islands
## 308 Sub-Saharan Africa Sudan
## 309 Central America and the Caribbean Dominican Republic
## 310 Sub-Saharan Africa Tanzania
## 311 Europe Switzerland
## 312 North America Greenland
## 313 Australia and Oceania Tonga
## 314 Middle East and North Africa Saudi Arabia
## 315 Central America and the Caribbean Belize
## 316 Sub-Saharan Africa Angola
## 317 Asia Malaysia
## 318 Sub-Saharan Africa Ethiopia
## 319 North America Greenland
## 320 Sub-Saharan Africa Benin
## 321 Middle East and North Africa Yemen
## 322 Sub-Saharan Africa Rwanda
## 323 Sub-Saharan Africa Mauritania
## 324 Australia and Oceania New Zealand
## 325 Australia and Oceania Samoa
## 326 Australia and Oceania Fiji
## 327 Sub-Saharan Africa Malawi
## 328 Australia and Oceania Marshall Islands
## 329 Central America and the Caribbean Grenada
## 330 Europe Luxembourg
## 331 Sub-Saharan Africa Zimbabwe
## 332 Asia China
## 333 North America United States of America
## 334 Sub-Saharan Africa Central African Republic
## 335 Central America and the Caribbean Antigua and Barbuda
## 336 Central America and the Caribbean Guatemala
## 337 Middle East and North Africa Qatar
## 338 Middle East and North Africa Israel
## 339 Asia Thailand
## 340 Asia Singapore
## 341 Asia North Korea
## 342 Europe Austria
## 343 Asia Japan
## 344 Sub-Saharan Africa Zimbabwe
## 345 Europe Lithuania
## 346 Europe Luxembourg
## 347 Sub-Saharan Africa Central African Republic
## 348 Europe Norway
## 349 Sub-Saharan Africa Democratic Republic of the Congo
## 350 Australia and Oceania New Zealand
## 351 Europe Ukraine
## 352 Asia Taiwan
## 353 Europe Italy
## 354 Europe Finland
## 355 Sub-Saharan Africa Sudan
## 356 Europe Croatia
## 357 Sub-Saharan Africa Mauritania
## 358 Australia and Oceania New Zealand
## 359 Middle East and North Africa Pakistan
## 360 Europe Poland
## 361 Europe Lithuania
## 362 Middle East and North Africa Pakistan
## 363 Australia and Oceania East Timor
## 364 Australia and Oceania Marshall Islands
## 365 Central America and the Caribbean Cuba
## 366 North America Greenland
## 367 Europe Luxembourg
## 368 Middle East and North Africa Israel
## 369 Sub-Saharan Africa Djibouti
## 370 Europe Bulgaria
## 371 Asia Mongolia
## 372 Central America and the Caribbean Dominican Republic
## 373 Middle East and North Africa Yemen
## 374 Australia and Oceania Federated States of Micronesia
## 375 Europe Finland
## 376 Central America and the Caribbean The Bahamas
## 377 Central America and the Caribbean Grenada
## 378 Sub-Saharan Africa Sao Tome and Principe
## 379 Central America and the Caribbean El Salvador
## 380 Europe Sweden
## 381 Asia Turkmenistan
## 382 Europe Monaco
## 383 Middle East and North Africa Turkey
## 384 Sub-Saharan Africa Mozambique
## 385 Middle East and North Africa Yemen
## 386 Asia Philippines
## 387 Sub-Saharan Africa Democratic Republic of the Congo
## 388 Australia and Oceania Fiji
## 389 Europe Macedonia
## 390 Middle East and North Africa Tunisia
## 391 Europe Liechtenstein
## 392 Middle East and North Africa Qatar
## 393 Australia and Oceania Tonga
## 394 Europe Belgium
## 395 Sub-Saharan Africa Chad
## 396 Asia Thailand
## 397 Europe Iceland
## 398 Central America and the Caribbean Saint Lucia
## 399 Asia Japan
## 400 Asia India
## 401 Australia and Oceania Vanuatu
## 402 Sub-Saharan Africa Chad
## 403 Sub-Saharan Africa Malawi
## 404 Europe Finland
## 405 Middle East and North Africa Turkey
## 406 Sub-Saharan Africa South Africa
## 407 Europe Lithuania
## 408 Europe Russia
## 409 Central America and the Caribbean The Bahamas
## 410 Central America and the Caribbean The Bahamas
## 411 Middle East and North Africa Turkey
## 412 Sub-Saharan Africa Mauritius
## 413 Europe Bulgaria
## 414 Middle East and North Africa Iran
## 415 Sub-Saharan Africa Ghana
## 416 Sub-Saharan Africa Malawi
## 417 Sub-Saharan Africa Zimbabwe
## 418 Asia Tajikistan
## 419 Europe Czech Republic
## 420 Sub-Saharan Africa Mauritius
## 421 Sub-Saharan Africa Lesotho
## 422 Sub-Saharan Africa Mali
## 423 Europe Georgia
## 424 Europe Albania
## 425 Europe Cyprus
## 426 Central America and the Caribbean Saint Kitts and Nevis
## 427 Middle East and North Africa Tunisia
## 428 Central America and the Caribbean Cuba
## 429 Australia and Oceania Kiribati
## 430 Asia Cambodia
## 431 Europe Moldova
## 432 Asia Uzbekistan
## 433 Asia India
## 434 Europe Germany
## 435 Europe Austria
## 436 Europe Germany
## 437 Asia Bhutan
## 438 Asia Kyrgyzstan
## 439 Middle East and North Africa Somalia
## 440 Central America and the Caribbean Saint Lucia
## 441 Europe Armenia
## 442 North America Canada
## 443 Sub-Saharan Africa Burundi
## 444 Europe Liechtenstein
## 445 Middle East and North Africa Tunisia
## 446 Middle East and North Africa Iraq
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## 999 Europe Finland
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## 619 Personal Care Offline C 5/5/2016 312404668
## 620 Office Supplies Offline C 4/14/2010 611816871
## 621 Office Supplies Online C 10/26/2013 879107797
## 622 Vegetables Offline C 7/21/2010 211201274
## 623 Fruits Online H 6/9/2015 925333631
## 624 Snacks Offline C 6/3/2010 909053695
## 625 Baby Food Online L 5/17/2016 370222795
## 626 Cosmetics Offline H 8/3/2013 487014758
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## 628 Meat Offline M 7/2/2010 551725089
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## 632 Personal Care Online M 5/21/2017 692956054
## 633 Cosmetics Online H 6/12/2013 194225251
## 634 Fruits Online L 2/22/2010 607757937
## 635 Cereal Offline H 6/29/2012 594540441
## 636 Vegetables Online L 3/23/2015 685871589
## 637 Cereal Offline C 2/12/2014 133362710
## 638 Clothes Offline H 5/22/2017 958937633
## 639 Vegetables Online C 8/6/2011 304832684
## 640 Baby Food Offline L 11/14/2010 783596694
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## 645 Baby Food Offline M 3/2/2016 431535089
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## 654 Cosmetics Offline H 11/3/2013 908627116
## 655 Cosmetics Offline C 4/2/2017 798784863
## 656 Beverages Offline H 5/28/2010 985092818
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## 668 Snacks Online H 12/28/2010 464840400
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## 678 Vegetables Offline C 8/11/2014 461823451
## 679 Office Supplies Offline C 1/12/2017 141812741
## 680 Meat Online C 7/3/2016 212874114
## 681 Office Supplies Offline C 3/27/2010 320368897
## 682 Beverages Online C 6/5/2015 179970920
## 683 Household Online H 6/27/2012 927666509
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## 695 Personal Care Offline H 6/16/2010 496897733
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## 697 Fruits Offline L 10/17/2016 941323029
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## 699 Meat Offline L 4/30/2016 267614781
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## 701 Personal Care Online M 8/8/2015 644913613
## 702 Cereal Offline H 8/18/2016 469414317
## 703 Clothes Online L 6/18/2015 867360150
## 704 Clothes Online L 1/28/2011 851299941
## 705 Beverages Offline H 2/4/2011 854095017
## 706 Vegetables Offline C 11/3/2012 478919208
## 707 Clothes Offline H 9/18/2014 749258840
## 708 Baby Food Offline H 5/17/2012 958912742
## 709 Personal Care Online C 2/20/2010 921992242
## 710 Vegetables Online L 2/28/2017 522921168
## 711 Personal Care Offline C 5/17/2013 166435849
## 712 Baby Food Offline L 11/9/2013 327585113
## 713 Clothes Offline M 1/10/2012 201730287
## 714 Fruits Offline L 9/24/2013 854545199
## 715 Personal Care Online M 9/26/2010 272016179
## 716 Cereal Offline H 10/21/2014 110442054
## 717 Household Offline C 1/17/2016 746434152
## 718 Vegetables Online L 12/27/2016 826916301
## 719 Beverages Offline H 8/30/2013 419124829
## 720 Office Supplies Online C 7/7/2014 560608565
## 721 Office Supplies Offline L 11/29/2013 109228837
## 722 Clothes Offline C 1/29/2011 693159472
## 723 Snacks Offline L 11/13/2013 860886800
## 724 Fruits Offline C 4/9/2012 131209647
## 725 Office Supplies Online H 6/28/2012 343239343
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## 727 Office Supplies Online M 8/23/2010 950427091
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## 730 Personal Care Offline C 5/7/2014 219762027
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## 732 Fruits Online M 3/30/2014 346215522
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## 734 Meat Online L 7/15/2014 386371409
## 735 Beverages Offline H 10/14/2013 185342633
## 736 Snacks Offline C 1/13/2017 596870315
## 737 Meat Online C 12/22/2012 703815782
## 738 Personal Care Online C 4/16/2010 559352862
## 739 Cosmetics Offline C 2/27/2011 736967885
## 740 Clothes Offline C 1/14/2015 980459678
## 741 Vegetables Offline C 11/16/2014 653939568
## 742 Cosmetics Online H 1/17/2016 991831386
## 743 Cereal Online L 5/29/2017 148871457
## 744 Fruits Online H 2/10/2015 850108611
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## 749 Beverages Offline C 1/9/2017 917834603
## 750 Personal Care Offline H 11/5/2016 947779643
## 751 Clothes Online L 10/31/2015 166013562
## 752 Beverages Online L 1/25/2011 960085189
## 753 Cosmetics Offline C 11/8/2015 837855851
## 754 Snacks Offline L 6/24/2010 977499377
## 755 Beverages Online L 2/27/2014 377502095
## 756 Meat Offline H 2/5/2013 806662833
## 757 Vegetables Online H 4/15/2017 954092919
## 758 Meat Online L 4/9/2011 479216182
## 759 Personal Care Online H 12/13/2010 461768949
## 760 Meat Online C 5/13/2014 251800048
## 761 Personal Care Offline L 1/16/2013 619670808
## 762 Household Offline H 12/9/2010 606055057
## 763 Baby Food Online L 1/24/2013 671939122
## 764 Cosmetics Offline M 2/25/2014 448621833
## 765 Fruits Online M 7/23/2017 987714517
## 766 Household Online H 5/7/2016 711141002
## 767 Office Supplies Online C 3/24/2012 361137616
## 768 Household Offline H 10/3/2011 750253188
## 769 Office Supplies Online C 12/22/2014 511349046
## 770 Personal Care Offline L 8/23/2011 147599017
## 771 Beverages Online H 4/18/2015 682489430
## 772 Vegetables Offline M 1/11/2014 509819114
## 773 Office Supplies Offline M 2/27/2012 343699395
## 774 Clothes Offline L 4/4/2011 968554103
## 775 Beverages Online M 12/26/2013 989119565
## 776 Vegetables Offline M 9/12/2012 880444610
## 777 Vegetables Offline L 12/19/2015 737890565
## 778 Vegetables Online M 6/26/2014 727131259
## 779 Office Supplies Offline L 3/2/2015 634153020
## 780 Beverages Online H 8/17/2014 315254676
## 781 Personal Care Offline M 7/19/2010 147047555
## 782 Household Online L 1/14/2013 576455485
## 783 Snacks Offline L 7/19/2010 770714795
## 784 Cosmetics Offline H 7/11/2011 867374312
## 785 Fruits Online L 5/4/2010 624295365
## 786 Clothes Offline M 10/13/2013 769651782
## 787 Vegetables Online C 7/27/2014 751929891
## 788 Office Supplies Online H 4/4/2010 989928519
## 789 Meat Offline H 8/17/2014 622758996
## 790 Baby Food Online H 1/4/2016 659845149
## 791 Baby Food Offline H 5/17/2011 830923306
## 792 Beverages Offline L 2/12/2010 936042296
## 793 Household Online L 2/17/2015 395563447
## 794 Meat Offline M 6/7/2011 500160586
## 795 Fruits Online L 5/26/2015 360820043
## 796 Snacks Online C 5/24/2010 958840644
## 797 Baby Food Online M 2/21/2011 195833718
## 798 Personal Care Offline M 6/28/2014 543723094
## 799 Cosmetics Offline H 10/15/2010 494745099
## 800 Vegetables Online H 5/18/2010 411448562
## 801 Personal Care Offline H 2/2/2017 276694810
## 802 Office Supplies Offline M 11/24/2016 143657672
## 803 Baby Food Online L 12/23/2011 585823476
## 804 Clothes Online C 12/5/2016 446991050
## 805 Clothes Offline M 2/16/2017 891271722
## 806 Cosmetics Online C 5/18/2010 453089320
## 807 Baby Food Offline L 9/7/2010 887180173
## 808 Cosmetics Online L 3/12/2014 418593108
## 809 Personal Care Online M 7/16/2010 492689454
## 810 Cosmetics Online M 12/5/2016 825143039
## 811 Vegetables Online L 10/8/2013 751940190
## 812 Baby Food Offline H 8/9/2016 579379737
## 813 Meat Online C 10/6/2011 234073007
## 814 Meat Online C 2/17/2013 612943828
## 815 Fruits Online C 12/14/2012 433228528
## 816 Fruits Online L 11/20/2015 282475936
## 817 Meat Online M 7/7/2012 368547379
## 818 Meat Offline H 6/2/2013 969616687
## 819 Baby Food Online L 2/4/2015 184170186
## 820 Fruits Online C 4/28/2011 681006705
## 821 Baby Food Online L 2/1/2012 249237573
## 822 Vegetables Offline H 1/29/2017 348286616
## 823 Clothes Offline L 12/1/2011 257890164
## 824 Cosmetics Online H 5/8/2017 228097045
## 825 Cereal Online C 5/3/2017 129268586
## 826 Cosmetics Online M 10/31/2015 802078616
## 827 Clothes Offline C 4/11/2010 907513463
## 828 Vegetables Online L 7/17/2010 976871955
## 829 Cereal Offline C 5/31/2012 261765420
## 830 Vegetables Offline L 7/15/2016 784117686
## 831 Cosmetics Offline M 1/14/2017 586165082
## 832 Snacks Online C 11/26/2012 480456435
## 833 Cereal Online L 10/3/2011 899853074
## 834 Baby Food Offline L 11/30/2010 547528827
## 835 Vegetables Online L 5/8/2016 446970021
## 836 Beverages Offline H 2/27/2016 791975486
## 837 Vegetables Offline M 8/24/2014 496656548
## 838 Vegetables Offline C 5/26/2013 345437037
## 839 Vegetables Offline H 1/26/2015 743053281
## 840 Snacks Offline L 1/7/2012 364554107
## 841 Cosmetics Online H 10/21/2012 205300843
## 842 Household Online M 9/16/2015 430967319
## 843 Meat Offline H 6/28/2012 827539861
## 844 Snacks Offline H 12/2/2014 351317298
## 845 Household Offline C 2/4/2013 278910958
## 846 Cosmetics Offline C 2/17/2017 157244670
## 847 Beverages Online L 6/24/2015 953554761
## 848 Cereal Online L 7/30/2016 105390059
## 849 Cosmetics Offline L 10/23/2014 970611894
## 850 Baby Food Offline M 12/16/2016 677394092
## 851 Baby Food Online L 9/28/2015 474222981
## 852 Snacks Online L 4/16/2016 779897391
## 853 Snacks Offline L 3/27/2017 733528649
## 854 Office Supplies Online H 6/25/2014 444540584
## 855 Clothes Online H 7/29/2012 542669522
## 856 Fruits Offline L 5/4/2013 827964293
## 857 Snacks Offline M 2/2/2012 720786225
## 858 Household Offline H 7/24/2016 611809146
## 859 Personal Care Online M 11/4/2012 512019383
## 860 Personal Care Online C 8/5/2014 502715766
## 861 Office Supplies Offline M 10/22/2013 285509622
## 862 Beverages Offline L 12/1/2014 532324779
## 863 Personal Care Offline H 11/2/2015 635397565
## 864 Cereal Offline M 10/7/2011 957276809
## 865 Snacks Online L 2/9/2014 580823838
## 866 Office Supplies Offline H 5/29/2016 459212481
## 867 Baby Food Online L 4/8/2013 265929067
## 868 Beverages Online H 9/20/2011 644772422
## 869 Vegetables Offline C 4/12/2012 959853875
## 870 Vegetables Online C 10/2/2012 645597255
## 871 Baby Food Offline C 1/2/2012 556738889
## 872 Vegetables Online M 9/30/2013 718327605
## 873 Baby Food Offline L 3/10/2015 775724732
## 874 Baby Food Offline H 10/26/2010 444604098
## 875 Beverages Online C 5/2/2011 860952031
## 876 Meat Offline C 2/19/2017 531067359
## 877 Cosmetics Offline M 7/20/2014 281561410
## 878 Personal Care Offline L 6/18/2017 109358012
## 879 Beverages Online L 7/19/2010 531693494
## 880 Clothes Offline C 8/9/2013 336116683
## 881 Cereal Offline M 3/23/2017 630488908
## 882 Fruits Online L 10/31/2016 792983996
## 883 Fruits Online C 7/3/2016 722088277
## 884 Cosmetics Online H 1/7/2010 386600577
## 885 Beverages Offline L 12/6/2014 275632226
## 886 Vegetables Offline H 10/16/2015 948607051
## 887 Cereal Online H 5/10/2013 785261380
## 888 Cereal Online C 3/27/2013 935644042
## 889 Snacks Offline M 7/13/2014 370116364
## 890 Beverages Online C 8/16/2013 829352176
## 891 Household Online M 7/14/2015 974337804
## 892 Household Online H 12/23/2014 436372077
## 893 Cereal Offline M 4/27/2015 267066323
## 894 Fruits Online M 3/14/2017 688344371
## 895 Cosmetics Offline M 10/2/2014 642442548
## 896 Cereal Online C 7/13/2014 941909682
## 897 Beverages Offline C 7/31/2016 219607102
## 898 Personal Care Offline H 8/30/2010 778708636
## 899 Beverages Online M 4/22/2010 942700612
## 900 Household Offline H 3/9/2011 905381858
## 901 Meat Online C 1/13/2010 480863702
## 902 Cosmetics Online L 1/6/2016 453569972
## 903 Baby Food Offline H 8/17/2016 328236997
## 904 Personal Care Offline C 10/22/2011 579913604
## 905 Cosmetics Online H 2/12/2010 403961122
## 906 Cosmetics Offline L 5/29/2017 866053378
## 907 Household Offline C 4/17/2010 852176702
## 908 Baby Food Online M 9/8/2015 218629920
## 909 Baby Food Offline C 2/13/2016 242024362
## 910 Snacks Offline M 2/16/2014 469283854
## 911 Fruits Online C 4/22/2013 967644727
## 912 Cosmetics Offline L 7/15/2014 974655807
## 913 Vegetables Online M 3/7/2012 248178422
## 914 Vegetables Offline L 1/14/2013 416386401
## 915 Snacks Offline L 5/22/2017 927766072
## 916 Vegetables Online M 3/23/2015 401116263
## 917 Fruits Offline H 9/28/2012 675548303
## 918 Baby Food Online L 6/6/2013 960486018
## 919 Clothes Offline L 4/2/2014 985665738
## 920 Clothes Offline C 1/13/2017 551136291
## 921 Cereal Online L 2/3/2017 877259004
## 922 Beverages Offline M 8/5/2014 554707705
## 923 Cereal Offline L 10/6/2010 494468724
## 924 Personal Care Online H 9/10/2011 777840888
## 925 Baby Food Offline C 12/12/2016 206435525
## 926 Office Supplies Online C 10/26/2015 352176463
## 927 Cosmetics Offline C 10/14/2013 607300031
## 928 Baby Food Online H 8/17/2013 434355056
## 929 Fruits Offline M 8/7/2011 716202867
## 930 Personal Care Online H 5/22/2016 606017291
## 931 Cereal Offline H 1/13/2015 677284657
## 932 Fruits Online C 7/22/2014 673803794
## 933 Cereal Offline C 6/25/2015 859686028
## 934 Clothes Online H 8/10/2015 669355189
## 935 Fruits Offline L 10/19/2013 957547605
## 936 Personal Care Online M 10/9/2013 849312102
## 937 Personal Care Offline H 9/26/2012 890010011
## 938 Cereal Online H 10/2/2012 795315158
## 939 Clothes Offline M 12/11/2016 801213872
## 940 Fruits Offline C 7/26/2010 314004981
## 941 Cereal Online C 3/27/2010 160299813
## 942 Beverages Offline C 9/11/2014 337022197
## 943 Snacks Offline M 3/9/2016 461408460
## 944 Office Supplies Offline M 10/19/2011 221007430
## 945 Snacks Offline L 10/31/2014 723680436
## 946 Fruits Offline L 2/23/2013 447601306
## 947 Clothes Offline L 10/25/2010 191256368
## 948 Vegetables Offline L 10/4/2016 823444449
## 949 Beverages Online C 9/4/2010 133276879
## 950 Meat Online H 12/22/2014 480177485
## 951 Household Offline H 4/8/2015 243882596
## 952 Cosmetics Online L 3/2/2017 574441039
## 953 Household Online M 3/14/2012 442214143
## 954 Clothes Offline C 11/22/2011 687875735
## 955 Clothes Offline H 9/8/2013 872412145
## 956 Fruits Offline C 4/23/2012 627122199
## 957 Fruits Online M 2/14/2011 103617227
## 958 Vegetables Offline M 10/6/2010 423821055
## 959 Vegetables Online L 12/8/2012 529970014
## 960 Personal Care Offline H 8/17/2016 334612929
## 961 Beverages Offline M 4/23/2014 270611131
## 962 Office Supplies Online C 11/18/2010 841138446
## 963 Household Online M 4/22/2012 369681203
## 964 Household Offline M 4/5/2014 850038230
## 965 Beverages Online M 6/21/2013 296320855
## 966 Cosmetics Offline L 7/13/2011 392952907
## 967 Snacks Online M 2/1/2011 644670712
## 968 Meat Online L 1/27/2012 626523101
## 969 Vegetables Offline M 6/24/2015 433871400
## 970 Snacks Offline M 1/3/2012 232389438
## 971 Cereal Offline H 2/21/2016 708063542
## 972 Vegetables Offline H 12/4/2016 817192542
## 973 Meat Offline H 1/16/2012 936387765
## 974 Clothes Offline H 8/6/2011 612573039
## 975 Clothes Online M 8/12/2011 812984693
## 976 Meat Offline C 12/18/2012 775171554
## 977 Household Online H 1/10/2010 256994950
## 978 Beverages Offline M 2/28/2017 886628711
## 979 Beverages Online C 12/8/2011 312559163
## 980 Cereal Online L 8/18/2014 753585135
## 981 Beverages Offline H 4/19/2016 448817956
## 982 Clothes Offline M 11/6/2012 407681453
## 983 Snacks Online H 5/27/2016 359911954
## 984 Office Supplies Offline M 8/18/2013 105558288
## 985 Personal Care Offline H 12/31/2014 864981782
## 986 Office Supplies Online C 2/4/2013 328856265
## 987 Clothes Offline H 9/23/2016 308168065
## 988 Household Offline L 11/1/2016 884216010
## 989 Snacks Offline M 12/27/2012 858611428
## 990 Cereal Online L 3/10/2017 903278148
## 991 Beverages Online L 3/17/2012 410452497
## 992 Cereal Offline H 12/17/2015 642683303
## 993 Beverages Offline L 2/18/2017 682831895
## 994 Baby Food Online L 11/5/2016 584072101
## 995 Meat Offline C 4/9/2017 919890248
## 996 Snacks Offline C 4/18/2010 534085166
## 997 Baby Food Offline H 8/1/2011 590768182
## 998 Vegetables Online C 5/12/2011 524363124
## 999 Household Offline L 1/25/2016 289606320
## 1000 Cereal Offline C 4/10/2014 811546599
## Ship.Date Units.Sold Unit.Price Unit.Cost Total.Revenue Total.Cost
## 1 10/31/2014 8446 437.20 263.33 3692591.20 2224085.18
## 2 12/8/2011 3018 154.06 90.93 464953.08 274426.74
## 3 12/9/2016 1517 255.28 159.42 387259.76 241840.14
## 4 5/12/2010 3322 205.70 117.11 683335.40 389039.42
## 5 8/31/2011 9845 9.33 6.92 91853.85 68127.40
## 6 12/28/2014 9528 205.70 117.11 1959909.60 1115824.08
## 7 4/17/2015 2844 205.70 117.11 585010.80 333060.84
## 8 6/28/2012 7299 109.28 35.84 797634.72 261596.16
## 9 3/7/2015 2428 154.06 90.93 374057.68 220778.04
## 10 1/19/2014 4800 154.06 90.93 739488.00 436464.00
## 11 1/19/2016 3012 109.28 35.84 329151.36 107950.08
## 12 3/18/2010 2694 152.58 97.44 411050.52 262503.36
## 13 12/22/2016 1508 668.27 502.54 1007751.16 757830.32
## 14 1/5/2016 4146 437.20 263.33 1812631.20 1091766.18
## 15 2/6/2011 7332 9.33 6.92 68407.56 50737.44
## 16 7/22/2010 4820 109.28 35.84 526729.60 172748.80
## 17 5/12/2016 2397 651.21 524.96 1560950.37 1258329.12
## 18 8/7/2012 2880 47.45 31.79 136656.00 91555.20
## 19 10/3/2014 1117 109.28 35.84 122065.76 40033.28
## 20 9/8/2012 8989 668.27 502.54 6007079.03 4517332.06
## 21 10/15/2012 407 152.58 97.44 62100.06 39658.08
## 22 9/16/2010 6313 109.28 35.84 689884.64 226257.92
## 23 3/9/2011 9681 81.73 56.67 791228.13 548622.27
## 24 1/24/2016 515 109.28 35.84 56279.20 18457.60
## 25 11/13/2012 852 205.70 117.11 175256.40 99777.72
## 26 3/20/2017 9759 47.45 31.79 463064.55 310238.61
## 27 11/4/2014 8334 47.45 31.79 395448.30 264937.86
## 28 2/22/2012 4709 9.33 6.92 43934.97 32586.28
## 29 3/9/2016 9043 421.89 364.69 3815151.27 3297891.67
## 30 1/7/2016 8529 81.73 56.67 697075.17 483338.43
## 31 6/23/2017 2391 47.45 31.79 113452.95 76009.89
## 32 7/9/2015 6884 651.21 524.96 4482929.64 3613824.64
## 33 3/16/2014 293 9.33 6.92 2733.69 2027.56
## 34 4/8/2014 7937 255.28 159.42 2026157.36 1265316.54
## 35 10/24/2010 7163 47.45 31.79 339884.35 227711.77
## 36 5/14/2010 2352 651.21 524.96 1531645.92 1234705.92
## 37 10/22/2013 9915 651.21 524.96 6456747.15 5204978.40
## 38 4/14/2011 3294 154.06 90.93 507473.64 299523.42
## 39 8/12/2016 7963 255.28 159.42 2032794.64 1269461.46
## 40 2/18/2011 6426 651.21 524.96 4184675.46 3373392.96
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## 42 9/2/2013 9913 47.45 31.79 470371.85 315134.27
## 43 11/20/2011 103 421.89 364.69 43454.67 37563.07
## 44 12/30/2016 4419 152.58 97.44 674251.02 430587.36
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## 46 4/21/2013 3107 47.45 31.79 147427.15 98771.53
## 47 9/1/2010 8896 421.89 364.69 3753133.44 3244282.24
## 48 2/15/2012 1643 668.27 502.54 1097967.61 825673.22
## 49 8/30/2015 2135 81.73 56.67 174493.55 120990.45
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## 51 5/31/2012 9654 154.06 90.93 1487295.24 877838.22
## 52 12/20/2010 3410 154.06 90.93 525344.60 310071.30
## 53 1/1/2015 8368 437.20 263.33 3658489.60 2203545.44
## 54 7/28/2016 470 47.45 31.79 22301.50 14941.30
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## 61 7/29/2014 949 651.21 524.96 617998.29 498187.04
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## 64 3/25/2015 9097 109.28 35.84 994120.16 326036.48
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## 66 1/8/2013 3636 651.21 524.96 2367799.56 1908754.56
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## 68 5/12/2014 2163 651.21 524.96 1408567.23 1135488.48
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## 73 11/6/2013 9794 47.45 31.79 464725.30 311351.26
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## 75 4/17/2015 2975 421.89 364.69 1255122.75 1084952.75
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## 78 6/14/2014 2850 651.21 524.96 1855948.50 1496136.00
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## 83 3/17/2010 3972 9.33 6.92 37058.76 27486.24
## 84 2/8/2013 8611 109.28 35.84 941010.08 308618.24
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## 86 2/23/2011 5408 47.45 31.79 256609.60 171920.32
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## 91 3/23/2014 4079 255.28 159.42 1041287.12 650274.18
## 92 9/13/2010 9721 109.28 35.84 1062310.88 348400.64
## 93 2/6/2013 8635 668.27 502.54 5770511.45 4339432.90
## 94 11/17/2010 8014 81.73 56.67 654984.22 454153.38
## 95 11/20/2016 7081 205.70 117.11 1456561.70 829255.91
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## 145 5/23/2014 6035 255.28 159.42 1540614.80 962099.70
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## 158 8/1/2013 624 152.58 97.44 95209.92 60802.56
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## 170 3/25/2013 8180 651.21 524.96 5326897.80 4294172.80
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## 184 5/15/2016 917 651.21 524.96 597159.57 481388.32
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## 191 1/16/2013 2476 81.73 56.67 202363.48 140314.92
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## 201 9/15/2012 3901 81.73 56.67 318828.73 221069.67
## 202 4/3/2017 7200 255.28 159.42 1838016.00 1147824.00
## 203 2/23/2015 2278 668.27 502.54 1522319.06 1144786.12
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## 206 9/15/2012 9633 421.89 364.69 4064066.37 3513058.77
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## 215 4/23/2014 4455 668.27 502.54 2977142.85 2238815.70
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## 217 10/24/2010 9669 9.33 6.92 90211.77 66909.48
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## 226 6/11/2012 3853 152.58 97.44 587890.74 375436.32
## 227 2/9/2014 2445 668.27 502.54 1633920.15 1228710.30
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## 229 12/27/2013 1739 255.28 159.42 443931.92 277231.38
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## 231 7/30/2012 80 255.28 159.42 20422.40 12753.60
## 232 11/24/2016 7597 81.73 56.67 620902.81 430521.99
## 233 10/20/2012 9381 421.89 364.69 3957750.09 3421156.89
## 234 7/23/2017 7002 651.21 524.96 4559772.42 3675769.92
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## 239 7/22/2010 9685 651.21 524.96 6306968.85 5084237.60
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## 241 3/24/2014 1967 205.70 117.11 404611.90 230355.37
## 242 9/1/2011 6449 668.27 502.54 4309673.23 3240880.46
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## 244 2/25/2014 6338 255.28 159.42 1617964.64 1010403.96
## 245 6/16/2015 7536 81.73 56.67 615917.28 427065.12
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## 247 10/23/2012 7151 205.70 117.11 1470960.70 837453.61
## 248 3/4/2015 8547 651.21 524.96 5565891.87 4486833.12
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## 250 7/26/2011 4695 154.06 90.93 723311.70 426916.35
## 251 8/30/2014 9614 47.45 31.79 456184.30 305629.06
## 252 9/3/2014 924 421.89 364.69 389826.36 336973.56
## 253 3/28/2015 3789 154.06 90.93 583733.34 344533.77
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## 255 7/23/2013 4979 668.27 502.54 3327316.33 2502146.66
## 256 2/13/2012 8783 421.89 364.69 3705459.87 3203072.27
## 257 5/30/2012 5098 47.45 31.79 241900.10 162065.42
## 258 11/21/2011 4240 205.70 117.11 872168.00 496546.40
## 259 5/18/2016 8559 668.27 502.54 5719722.93 4301239.86
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## 264 7/5/2012 2193 47.45 31.79 104057.85 69715.47
## 265 11/23/2012 642 668.27 502.54 429029.34 322630.68
## 266 4/14/2014 7584 668.27 502.54 5068159.68 3811263.36
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## 271 12/13/2010 1718 109.28 35.84 187743.04 61573.12
## 272 11/7/2010 1276 81.73 56.67 104287.48 72310.92
## 273 5/9/2014 2173 154.06 90.93 334772.38 197590.89
## 274 2/9/2013 7227 152.58 97.44 1102695.66 704198.88
## 275 9/29/2014 1285 668.27 502.54 858726.95 645763.90
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## 277 12/2/2010 5965 205.70 117.11 1227000.50 698561.15
## 278 1/26/2015 1441 421.89 364.69 607943.49 525518.29
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## 280 8/21/2016 8534 437.20 263.33 3731064.80 2247258.22
## 281 2/19/2011 2191 668.27 502.54 1464179.57 1101065.14
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## 283 2/9/2015 64 154.06 90.93 9859.84 5819.52
## 284 7/27/2016 3509 651.21 524.96 2285095.89 1842084.64
## 285 6/15/2014 6163 154.06 90.93 949471.78 560401.59
## 286 1/29/2013 5220 47.45 31.79 247689.00 165943.80
## 287 3/2/2010 9902 668.27 502.54 6617209.54 4976151.08
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## 289 8/10/2012 3195 154.06 90.93 492221.70 290521.35
## 290 3/9/2015 5409 205.70 117.11 1112631.30 633447.99
## 291 3/20/2011 455 47.45 31.79 21589.75 14464.45
## 292 1/26/2015 2715 437.20 263.33 1186998.00 714940.95
## 293 6/28/2012 8598 47.45 31.79 407975.10 273330.42
## 294 1/21/2016 1547 154.06 90.93 238330.82 140668.71
## 295 2/25/2017 7036 154.06 90.93 1083966.16 639783.48
## 296 2/13/2012 7570 255.28 159.42 1932469.60 1206809.40
## 297 2/15/2013 7685 437.20 263.33 3359882.00 2023691.05
## 298 9/22/2014 8948 668.27 502.54 5979679.96 4496727.92
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## 300 4/7/2014 6344 437.20 263.33 2773596.80 1670565.52
## 301 2/10/2014 5768 651.21 524.96 3756179.28 3027969.28
## 302 10/12/2010 2923 651.21 524.96 1903486.83 1534458.08
## 303 6/25/2011 9532 651.21 524.96 6207333.72 5003918.72
## 304 5/21/2013 4349 47.45 31.79 206360.05 138254.71
## 305 8/14/2014 8161 47.45 31.79 387239.45 259438.19
## 306 10/30/2010 8786 152.58 97.44 1340567.88 856107.84
## 307 5/14/2013 6071 81.73 56.67 496182.83 344043.57
## 308 2/1/2016 6897 9.33 6.92 64349.01 47727.24
## 309 12/14/2013 175 109.28 35.84 19124.00 6272.00
## 310 3/9/2014 1848 205.70 117.11 380133.60 216419.28
## 311 7/10/2017 9888 109.28 35.84 1080560.64 354385.92
## 312 5/20/2014 9302 668.27 502.54 6216247.54 4674627.08
## 313 6/8/2011 7124 9.33 6.92 66466.92 49298.08
## 314 9/22/2012 7422 154.06 90.93 1143433.32 674882.46
## 315 9/11/2015 6296 437.20 263.33 2752611.20 1657925.68
## 316 11/20/2014 6874 437.20 263.33 3005312.80 1810130.42
## 317 10/21/2013 4319 668.27 502.54 2886258.13 2170470.26
## 318 3/12/2013 822 47.45 31.79 39003.90 26131.38
## 319 8/16/2014 607 255.28 159.42 154954.96 96767.94
## 320 1/16/2017 4928 205.70 117.11 1013689.60 577118.08
## 321 12/10/2012 7073 205.70 117.11 1454916.10 828319.03
## 322 11/27/2014 7358 255.28 159.42 1878350.24 1173012.36
## 323 8/15/2013 8132 421.89 364.69 3430809.48 2965659.08
## 324 6/16/2012 8775 81.73 56.67 717180.75 497279.25
## 325 8/6/2015 699 109.28 35.84 76386.72 25052.16
## 326 3/2/2017 2344 109.28 35.84 256152.32 84008.96
## 327 2/22/2012 4186 47.45 31.79 198625.70 133072.94
## 328 9/21/2015 3729 47.45 31.79 176941.05 118544.91
## 329 6/30/2013 508 255.28 159.42 129682.24 80985.36
## 330 9/22/2011 1093 421.89 364.69 461125.77 398606.17
## 331 12/6/2016 4080 421.89 364.69 1721311.20 1487935.20
## 332 9/21/2010 5100 154.06 90.93 785706.00 463743.00
## 333 7/14/2013 1815 47.45 31.79 86121.75 57698.85
## 334 11/21/2012 8916 154.06 90.93 1373598.96 810731.88
## 335 1/5/2012 3127 154.06 90.93 481745.62 284338.11
## 336 3/14/2012 8203 255.28 159.42 2094061.84 1307722.26
## 337 1/7/2015 9930 109.28 35.84 1085150.40 355891.20
## 338 5/28/2012 1126 81.73 56.67 92027.98 63810.42
## 339 2/15/2012 6639 152.58 97.44 1012978.62 646904.16
## 340 8/12/2011 8349 205.70 117.11 1717389.30 977751.39
## 341 4/8/2017 167 152.58 97.44 25480.86 16272.48
## 342 4/18/2017 3036 651.21 524.96 1977073.56 1593778.56
## 343 4/19/2016 9929 255.28 159.42 2534675.12 1582881.18
## 344 12/26/2015 851 47.45 31.79 40379.95 27053.29
## 345 11/16/2011 7838 9.33 6.92 73128.54 54238.96
## 346 8/2/2011 9958 255.28 159.42 2542078.24 1587504.36
## 347 4/10/2016 8309 437.20 263.33 3632694.80 2188008.97
## 348 1/21/2017 1021 668.27 502.54 682303.67 513093.34
## 349 4/18/2013 8256 9.33 6.92 77028.48 57131.52
## 350 4/20/2017 7205 668.27 502.54 4814885.35 3620800.70
## 351 7/4/2011 7092 81.73 56.67 579629.16 401903.64
## 352 12/31/2013 4173 81.73 56.67 341059.29 236483.91
## 353 5/31/2013 6733 205.70 117.11 1384978.10 788501.63
## 354 11/27/2010 89 81.73 56.67 7273.97 5043.63
## 355 4/21/2016 1591 651.21 524.96 1036075.11 835211.36
## 356 9/12/2013 5618 152.58 97.44 857194.44 547417.92
## 357 5/28/2011 8581 47.45 31.79 407168.45 272789.99
## 358 7/25/2014 3923 255.28 159.42 1001463.44 625404.66
## 359 9/15/2014 7117 47.45 31.79 337701.65 226249.43
## 360 6/19/2015 668 668.27 502.54 446404.36 335696.72
## 361 1/1/2014 9113 205.70 117.11 1874544.10 1067223.43
## 362 6/10/2012 4019 205.70 117.11 826708.30 470665.09
## 363 11/13/2013 8984 437.20 263.33 3927804.80 2365756.72
## 364 1/14/2011 4638 9.33 6.92 43272.54 32094.96
## 365 2/3/2013 3642 668.27 502.54 2433839.34 1830250.68
## 366 4/14/2011 5689 255.28 159.42 1452287.92 906940.38
## 367 10/27/2012 2503 81.73 56.67 204570.19 141845.01
## 368 11/15/2014 2838 47.45 31.79 134663.10 90220.02
## 369 7/24/2015 7480 255.28 159.42 1909494.40 1192461.60
## 370 5/27/2010 4247 205.70 117.11 873607.90 497366.17
## 371 1/9/2011 2988 154.06 90.93 460331.28 271698.84
## 372 12/31/2010 582 109.28 35.84 63600.96 20858.88
## 373 1/1/2013 5940 437.20 263.33 2596968.00 1564180.20
## 374 9/7/2016 5005 81.73 56.67 409058.65 283633.35
## 375 3/20/2015 5751 81.73 56.67 470029.23 325909.17
## 376 1/4/2014 3181 205.70 117.11 654331.70 372526.91
## 377 6/12/2012 4334 421.89 364.69 1828471.26 1580566.46
## 378 4/19/2014 3275 421.89 364.69 1381689.75 1194359.75
## 379 2/18/2017 6103 81.73 56.67 498798.19 345857.01
## 380 7/6/2015 5949 255.28 159.42 1518660.72 948389.58
## 381 12/3/2012 7974 437.20 263.33 3486232.80 2099793.42
## 382 2/13/2010 4369 154.06 90.93 673088.14 397273.17
## 383 2/6/2016 9359 421.89 364.69 3948468.51 3413133.71
## 384 10/23/2014 4199 47.45 31.79 199242.55 133486.21
## 385 1/9/2014 2173 651.21 524.96 1415079.33 1140738.08
## 386 11/25/2010 3601 205.70 117.11 740725.70 421713.11
## 387 6/2/2011 830 81.73 56.67 67835.90 47036.10
## 388 3/23/2011 3241 152.58 97.44 494511.78 315803.04
## 389 10/13/2014 2244 651.21 524.96 1461315.24 1178010.24
## 390 12/14/2015 6283 205.70 117.11 1292413.10 735802.13
## 391 7/18/2017 5829 205.70 117.11 1199025.30 682634.19
## 392 8/18/2011 8390 437.20 263.33 3668108.00 2209338.70
## 393 8/24/2014 3499 421.89 364.69 1476193.11 1276050.31
## 394 2/24/2017 7726 421.89 364.69 3259522.14 2817594.94
## 395 12/9/2010 9669 9.33 6.92 90211.77 66909.48
## 396 5/5/2015 4957 255.28 159.42 1265422.96 790244.94
## 397 10/16/2012 1251 109.28 35.84 136709.28 44835.84
## 398 9/2/2013 3245 421.89 364.69 1369033.05 1183419.05
## 399 8/18/2013 7661 437.20 263.33 3349389.20 2017371.13
## 400 12/17/2013 8254 81.73 56.67 674599.42 467754.18
## 401 3/28/2016 812 651.21 524.96 528782.52 426267.52
## 402 4/4/2012 8150 421.89 364.69 3438403.50 2972223.50
## 403 6/29/2017 5118 437.20 263.33 2237589.60 1347722.94
## 404 5/16/2014 3596 437.20 263.33 1572171.20 946934.68
## 405 8/4/2016 7494 109.28 35.84 818944.32 268584.96
## 406 7/1/2012 7755 421.89 364.69 3271756.95 2828170.95
## 407 2/16/2014 7353 651.21 524.96 4788347.13 3860030.88
## 408 11/21/2015 6293 47.45 31.79 298602.85 200054.47
## 409 2/28/2013 6610 255.28 159.42 1687400.80 1053766.20
## 410 6/7/2013 7373 152.58 97.44 1124972.34 718425.12
## 411 6/3/2010 9679 437.20 263.33 4231658.80 2548771.07
## 412 5/22/2010 1659 437.20 263.33 725314.80 436864.47
## 413 9/9/2012 3473 154.06 90.93 535050.38 315799.89
## 414 5/13/2014 2315 668.27 502.54 1547045.05 1163380.10
## 415 4/5/2013 7408 668.27 502.54 4950544.16 3722816.32
## 416 1/17/2012 5006 255.28 159.42 1277931.68 798056.52
## 417 10/10/2014 3395 255.28 159.42 866675.60 541230.90
## 418 10/2/2014 7894 154.06 90.93 1216149.64 717801.42
## 419 3/28/2014 5851 205.70 117.11 1203550.70 685210.61
## 420 9/27/2012 1646 668.27 502.54 1099972.42 827180.84
## 421 9/22/2010 1689 9.33 6.92 15758.37 11687.88
## 422 8/15/2013 9424 47.45 31.79 447168.80 299588.96
## 423 10/23/2012 323 81.73 56.67 26398.79 18304.41
## 424 10/15/2011 6892 651.21 524.96 4488139.32 3618024.32
## 425 6/6/2015 3667 437.20 263.33 1603212.40 965631.11
## 426 7/27/2010 2215 668.27 502.54 1480218.05 1113126.10
## 427 9/21/2015 6135 421.89 364.69 2588295.15 2237373.15
## 428 12/6/2015 6057 421.89 364.69 2555387.73 2208927.33
## 429 8/16/2012 4641 205.70 117.11 954653.70 543507.51
## 430 5/4/2012 1581 152.58 97.44 241228.98 154052.64
## 431 9/18/2014 8600 255.28 159.42 2195408.00 1371012.00
## 432 11/28/2016 4452 205.70 117.11 915776.40 521373.72
## 433 1/21/2017 9924 437.20 263.33 4338772.80 2613286.92
## 434 7/13/2010 2834 81.73 56.67 231622.82 160602.78
## 435 8/31/2016 3030 154.06 90.93 466801.80 275517.90
## 436 1/12/2014 7391 651.21 524.96 4813093.11 3879979.36
## 437 8/9/2012 4829 109.28 35.84 527713.12 173071.36
## 438 3/2/2012 1287 9.33 6.92 12007.71 8906.04
## 439 4/25/2013 4738 81.73 56.67 387236.74 268502.46
## 440 3/10/2014 186 668.27 502.54 124298.22 93472.44
## 441 11/15/2013 4668 152.58 97.44 712243.44 454849.92
## 442 6/2/2016 2252 47.45 31.79 106857.40 71591.08
## 443 6/2/2011 9036 437.20 263.33 3950539.20 2379449.88
## 444 9/9/2016 8149 205.70 117.11 1676249.30 954329.39
## 445 8/5/2012 4754 152.58 97.44 725365.32 463229.76
## 446 1/11/2015 1042 47.45 31.79 49442.90 33125.18
## 447 6/1/2010 1237 437.20 263.33 540816.40 325739.21
## 448 2/17/2013 6594 47.45 31.79 312885.30 209623.26
## 449 7/9/2015 399 47.45 31.79 18932.55 12684.21
## 450 11/18/2014 2969 154.06 90.93 457404.14 269971.17
## 451 3/13/2017 6653 152.58 97.44 1015114.74 648268.32
## 452 7/16/2010 832 154.06 90.93 128177.92 75653.76
## 453 5/20/2014 6915 154.06 90.93 1065324.90 628780.95
## 454 4/15/2015 3346 421.89 364.69 1411643.94 1220252.74
## 455 10/15/2015 598 651.21 524.96 389423.58 313926.08
## 456 8/30/2015 6176 81.73 56.67 504764.48 349993.92
## 457 3/17/2016 9615 437.20 263.33 4203678.00 2531917.95
## 458 7/23/2015 7485 668.27 502.54 5002000.95 3761511.90
## 459 12/6/2012 8382 205.70 117.11 1724177.40 981616.02
## 460 10/19/2012 7938 152.58 97.44 1211180.04 773478.72
## 461 8/7/2012 3812 109.28 35.84 416575.36 136622.08
## 462 11/11/2014 698 47.45 31.79 33120.10 22189.42
## 463 10/18/2015 5320 437.20 263.33 2325904.00 1400915.60
## 464 2/20/2016 1431 651.21 524.96 931881.51 751217.76
## 465 4/19/2010 4818 9.33 6.92 44951.94 33340.56
## 466 8/28/2012 8299 81.73 56.67 678277.27 470304.33
## 467 11/2/2015 6722 109.28 35.84 734580.16 240916.48
## 468 7/18/2017 1968 651.21 524.96 1281581.28 1033121.28
## 469 4/30/2016 7946 47.45 31.79 377037.70 252603.34
## 470 9/9/2015 5600 109.28 35.84 611968.00 200704.00
## 471 7/4/2012 7903 205.70 117.11 1625647.10 925520.33
## 472 5/5/2016 4860 437.20 263.33 2124792.00 1279783.80
## 473 9/21/2014 8508 255.28 159.42 2171922.24 1356345.36
## 474 9/10/2015 7913 152.58 97.44 1207365.54 771042.72
## 475 7/20/2012 4174 651.21 524.96 2718150.54 2191183.04
## 476 9/13/2011 5421 152.58 97.44 827136.18 528222.24
## 477 7/1/2012 1816 668.27 502.54 1213578.32 912612.64
## 478 12/24/2011 550 47.45 31.79 26097.50 17484.50
## 479 6/4/2015 848 47.45 31.79 40237.60 26957.92
## 480 8/22/2010 8963 81.73 56.67 732545.99 507933.21
## 481 5/27/2016 3183 437.20 263.33 1391607.60 838179.39
## 482 6/8/2011 8825 437.20 263.33 3858290.00 2323887.25
## 483 9/20/2012 3237 47.45 31.79 153595.65 102904.23
## 484 5/2/2012 799 109.28 35.84 87314.72 28636.16
## 485 8/9/2010 7922 668.27 502.54 5294034.94 3981121.88
## 486 3/26/2013 8049 255.28 159.42 2054748.72 1283171.58
## 487 3/10/2011 6654 421.89 364.69 2807256.06 2426647.26
## 488 12/16/2015 6240 651.21 524.96 4063550.40 3275750.40
## 489 2/16/2017 1345 651.21 524.96 875877.45 706071.20
## 490 10/28/2016 3536 651.21 524.96 2302678.56 1856258.56
## 491 6/2/2010 6411 255.28 159.42 1636600.08 1022041.62
## 492 7/10/2013 600 47.45 31.79 28470.00 19074.00
## 493 7/27/2012 8765 668.27 502.54 5857386.55 4404763.10
## 494 10/22/2013 597 152.58 97.44 91090.26 58171.68
## 495 1/6/2017 7821 81.73 56.67 639210.33 443216.07
## 496 10/15/2011 9191 421.89 364.69 3877590.99 3351865.79
## 497 4/2/2011 5494 81.73 56.67 449024.62 311344.98
## 498 1/26/2011 4546 9.33 6.92 42414.18 31458.32
## 499 2/22/2015 6197 651.21 524.96 4035548.37 3253177.12
## 500 6/25/2017 7325 9.33 6.92 68342.25 50689.00
## 501 8/15/2012 6844 421.89 364.69 2887415.16 2495938.36
## 502 6/30/2015 694 205.70 117.11 142755.80 81274.34
## 503 11/1/2011 6850 152.58 97.44 1045173.00 667464.00
## 504 10/12/2014 316 651.21 524.96 205782.36 165887.36
## 505 11/7/2015 8128 651.21 524.96 5293034.88 4266874.88
## 506 3/31/2011 4355 421.89 364.69 1837330.95 1588224.95
## 507 10/28/2013 5093 47.45 31.79 241662.85 161906.47
## 508 9/14/2010 3475 154.06 90.93 535358.50 315981.75
## 509 9/4/2012 4659 109.28 35.84 509135.52 166978.56
## 510 10/12/2012 840 668.27 502.54 561346.80 422133.60
## 511 12/29/2012 6240 255.28 159.42 1592947.20 994780.80
## 512 1/2/2012 2114 205.70 117.11 434849.80 247570.54
## 513 7/25/2017 1749 668.27 502.54 1168804.23 878942.46
## 514 11/4/2011 5462 152.58 97.44 833391.96 532217.28
## 515 10/26/2015 5602 154.06 90.93 863044.12 509389.86
## 516 4/9/2010 1547 47.45 31.79 73405.15 49179.13
## 517 7/14/2014 4711 154.06 90.93 725776.66 428371.23
## 518 1/13/2015 3534 437.20 263.33 1545064.80 930608.22
## 519 6/10/2014 8491 47.45 31.79 402897.95 269928.89
## 520 9/3/2012 7086 437.20 263.33 3097999.20 1865956.38
## 521 10/1/2010 8856 255.28 159.42 2260759.68 1411823.52
## 522 5/17/2014 368 255.28 159.42 93943.04 58666.56
## 523 6/23/2015 221 9.33 6.92 2061.93 1529.32
## 524 9/2/2010 4044 152.58 97.44 617033.52 394047.36
## 525 9/5/2012 9499 47.45 31.79 450727.55 301973.21
## 526 8/9/2016 1277 421.89 364.69 538753.53 465709.13
## 527 10/19/2010 6104 154.06 90.93 940382.24 555036.72
## 528 4/16/2011 7733 154.06 90.93 1191345.98 703161.69
## 529 5/5/2012 1950 9.33 6.92 18193.50 13494.00
## 530 6/22/2013 1574 152.58 97.44 240160.92 153370.56
## 531 9/25/2014 1452 421.89 364.69 612584.28 529529.88
## 532 9/11/2013 3465 152.58 97.44 528689.70 337629.60
## 533 8/15/2016 1523 9.33 6.92 14209.59 10539.16
## 534 9/20/2011 6569 9.33 6.92 61288.77 45457.48
## 535 4/19/2011 1578 47.45 31.79 74876.10 50164.62
## 536 3/18/2017 6552 421.89 364.69 2764223.28 2389448.88
## 537 12/23/2016 3530 437.20 263.33 1543316.00 929554.90
## 538 12/5/2010 1578 255.28 159.42 402831.84 251564.76
## 539 3/27/2015 1794 152.58 97.44 273728.52 174807.36
## 540 7/25/2013 2309 668.27 502.54 1543035.43 1160364.86
## 541 11/9/2012 3284 437.20 263.33 1435764.80 864775.72
## 542 7/30/2017 1910 9.33 6.92 17820.30 13217.20
## 543 7/27/2010 7413 651.21 524.96 4827419.73 3891528.48
## 544 3/21/2017 6046 154.06 90.93 931446.76 549762.78
## 545 2/25/2011 6096 421.89 364.69 2571841.44 2223150.24
## 546 2/8/2016 2880 9.33 6.92 26870.40 19929.60
## 547 8/13/2011 3747 255.28 159.42 956534.16 597346.74
## 548 5/4/2012 3077 47.45 31.79 146003.65 97817.83
## 549 1/12/2011 7281 154.06 90.93 1121710.86 662061.33
## 550 4/3/2013 9800 9.33 6.92 91434.00 67816.00
## 551 4/4/2010 6110 668.27 502.54 4083129.70 3070519.40
## 552 3/30/2013 8714 81.73 56.67 712195.22 493822.38
## 553 12/12/2011 2149 47.45 31.79 101970.05 68316.71
## 554 12/2/2015 7982 651.21 524.96 5197958.22 4190230.72
## 555 2/5/2013 9812 668.27 502.54 6557065.24 4930922.48
## 556 9/3/2011 8269 81.73 56.67 675825.37 468604.23
## 557 12/5/2012 6014 421.89 364.69 2537246.46 2193245.66
## 558 9/9/2015 2739 255.28 159.42 699211.92 436651.38
## 559 4/19/2012 168 154.06 90.93 25882.08 15276.24
## 560 8/24/2014 7055 205.70 117.11 1451213.50 826211.05
## 561 7/28/2013 4188 9.33 6.92 39074.04 28980.96
## 562 2/28/2012 9383 437.20 263.33 4102247.60 2470825.39
## 563 4/26/2014 2488 109.28 35.84 271888.64 89169.92
## 564 10/8/2013 385 9.33 6.92 3592.05 2664.20
## 565 1/18/2013 1983 651.21 524.96 1291349.43 1040995.68
## 566 12/8/2011 3226 437.20 263.33 1410407.20 849502.58
## 567 11/25/2010 2087 9.33 6.92 19471.71 14442.04
## 568 12/4/2015 3570 651.21 524.96 2324819.70 1874107.20
## 569 9/1/2010 4713 437.20 263.33 2060523.60 1241074.29
## 570 5/1/2014 9582 9.33 6.92 89400.06 66307.44
## 571 10/12/2016 4276 47.45 31.79 202896.20 135934.04
## 572 9/15/2012 1925 109.28 35.84 210364.00 68992.00
## 573 3/5/2013 7689 152.58 97.44 1173187.62 749216.16
## 574 1/30/2011 3762 81.73 56.67 307468.26 213192.54
## 575 11/6/2015 4368 205.70 117.11 898497.60 511536.48
## 576 2/23/2011 760 651.21 524.96 494919.60 398969.60
## 577 6/13/2012 6225 81.73 56.67 508769.25 352770.75
## 578 10/14/2016 1080 421.89 364.69 455641.20 393865.20
## 579 12/26/2014 7675 47.45 31.79 364178.75 243988.25
## 580 7/21/2010 5388 109.28 35.84 588800.64 193105.92
## 581 2/6/2015 5631 81.73 56.67 460221.63 319108.77
## 582 5/1/2015 6847 205.70 117.11 1408427.90 801852.17
## 583 8/24/2013 9509 668.27 502.54 6354579.43 4778652.86
## 584 2/5/2010 1122 47.45 31.79 53238.90 35668.38
## 585 4/11/2013 1222 205.70 117.11 251365.40 143108.42
## 586 9/29/2013 6377 81.73 56.67 521192.21 361384.59
## 587 12/31/2010 5185 421.89 364.69 2187499.65 1890917.65
## 588 9/1/2016 3275 205.70 117.11 673667.50 383535.25
## 589 4/25/2013 8310 154.06 90.93 1280238.60 755628.30
## 590 3/11/2011 4981 9.33 6.92 46472.73 34468.52
## 591 10/16/2013 13 668.27 502.54 8687.51 6533.02
## 592 6/5/2014 7159 421.89 364.69 3020310.51 2610815.71
## 593 12/9/2014 2207 421.89 364.69 931111.23 804870.83
## 594 9/13/2014 7973 9.33 6.92 74388.09 55173.16
## 595 11/25/2013 9306 651.21 524.96 6060160.26 4885277.76
## 596 4/5/2010 8086 421.89 364.69 3411402.54 2948883.34
## 597 4/29/2017 8225 152.58 97.44 1254970.50 801444.00
## 598 11/14/2015 664 47.45 31.79 31506.80 21108.56
## 599 8/23/2010 8377 47.45 31.79 397488.65 266304.83
## 600 11/30/2010 1370 205.70 117.11 281809.00 160440.70
## 601 1/26/2012 1677 421.89 364.69 707509.53 611585.13
## 602 2/25/2014 8367 154.06 90.93 1289020.02 760811.31
## 603 10/3/2010 2539 154.06 90.93 391158.34 230871.27
## 604 11/4/2015 2321 668.27 502.54 1551054.67 1166395.34
## 605 8/17/2013 7876 152.58 97.44 1201720.08 767437.44
## 606 2/26/2016 6396 152.58 97.44 975901.68 623226.24
## 607 9/11/2013 7103 205.70 117.11 1461087.10 831832.33
## 608 8/20/2016 6254 154.06 90.93 963491.24 568676.22
## 609 9/29/2013 2134 255.28 159.42 544767.52 340202.28
## 610 10/2/2013 61 421.89 364.69 25735.29 22246.09
## 611 1/23/2011 7383 437.20 263.33 3227847.60 1944165.39
## 612 10/4/2012 8480 154.06 90.93 1306428.80 771086.40
## 613 11/14/2011 9764 437.20 263.33 4268820.80 2571154.12
## 614 9/9/2013 4676 668.27 502.54 3124830.52 2349877.04
## 615 3/4/2017 8691 47.45 31.79 412387.95 276286.89
## 616 10/21/2010 4312 255.28 159.42 1100767.36 687419.04
## 617 8/10/2014 6077 81.73 56.67 496673.21 344383.59
## 618 6/4/2015 5553 81.73 56.67 453846.69 314688.51
## 619 6/21/2016 6338 81.73 56.67 518004.74 359174.46
## 620 5/16/2010 9063 651.21 524.96 5901916.23 4757712.48
## 621 11/2/2013 6388 651.21 524.96 4159929.48 3353444.48
## 622 9/9/2010 8005 154.06 90.93 1233250.30 727894.65
## 623 7/25/2015 5639 9.33 6.92 52611.87 39021.88
## 624 6/27/2010 8044 152.58 97.44 1227353.52 783807.36
## 625 6/11/2016 6007 255.28 159.42 1533466.96 957635.94
## 626 8/30/2013 7344 437.20 263.33 3210796.80 1933895.52
## 627 10/6/2013 1905 154.06 90.93 293484.30 173221.65
## 628 8/10/2010 6569 421.89 364.69 2771395.41 2395648.61
## 629 1/10/2014 248 421.89 364.69 104628.72 90443.12
## 630 3/23/2016 8883 651.21 524.96 5784698.43 4663219.68
## 631 7/9/2010 449 152.58 97.44 68508.42 43750.56
## 632 6/23/2017 9950 81.73 56.67 813213.50 563866.50
## 633 6/19/2013 4423 437.20 263.33 1933735.60 1164708.59
## 634 4/5/2010 7934 9.33 6.92 74024.22 54903.28
## 635 7/30/2012 6583 205.70 117.11 1354123.10 770935.13
## 636 4/5/2015 3500 154.06 90.93 539210.00 318255.00
## 637 3/23/2014 3844 205.70 117.11 790710.80 450170.84
## 638 7/5/2017 9810 109.28 35.84 1072036.80 351590.40
## 639 9/6/2011 5620 154.06 90.93 865817.20 511026.60
## 640 12/24/2010 2530 255.28 159.42 645858.40 403332.60
## 641 4/27/2015 3825 668.27 502.54 2556132.75 1922215.50
## 642 7/28/2014 9823 154.06 90.93 1513331.38 893205.39
## 643 9/16/2014 2873 109.28 35.84 313961.44 102968.32
## 644 3/31/2011 2354 109.28 35.84 257245.12 84367.36
## 645 3/19/2016 9677 255.28 159.42 2470344.56 1542707.34
## 646 1/16/2016 3286 205.70 117.11 675930.20 384823.46
## 647 4/7/2013 3653 81.73 56.67 298559.69 207015.51
## 648 7/4/2015 8283 152.58 97.44 1263820.14 807095.52
## 649 3/20/2016 6714 154.06 90.93 1034358.84 610504.02
## 650 7/10/2013 5511 154.06 90.93 849024.66 501115.23
## 651 5/14/2014 3273 255.28 159.42 835531.44 521781.66
## 652 7/3/2015 5632 421.89 364.69 2376084.48 2053934.08
## 653 7/2/2014 246 152.58 97.44 37534.68 23970.24
## 654 11/24/2013 1810 437.20 263.33 791332.00 476627.30
## 655 5/2/2017 7047 437.20 263.33 3080948.40 1855686.51
## 656 7/17/2010 9711 47.45 31.79 460786.95 308712.69
## 657 7/7/2010 5588 152.58 97.44 852617.04 544494.72
## 658 6/24/2016 7497 47.45 31.79 355732.65 238329.63
## 659 10/5/2013 285 421.89 364.69 120238.65 103936.65
## 660 10/23/2014 5833 9.33 6.92 54421.89 40364.36
## 661 11/13/2010 8052 421.89 364.69 3397058.28 2936483.88
## 662 1/1/2013 7884 109.28 35.84 861563.52 282562.56
## 663 5/26/2010 8302 205.70 117.11 1707721.40 972247.22
## 664 9/29/2012 9312 152.58 97.44 1420824.96 907361.28
## 665 2/22/2015 2950 205.70 117.11 606815.00 345474.50
## 666 3/21/2010 8282 47.45 31.79 392980.90 263284.78
## 667 5/27/2014 6409 437.20 263.33 2802014.80 1687681.97
## 668 2/5/2011 5459 152.58 97.44 832934.22 531924.96
## 669 10/24/2014 5594 668.27 502.54 3738302.38 2811208.76
## 670 2/22/2015 4006 421.89 364.69 1690091.34 1460948.14
## 671 5/18/2017 9919 47.45 31.79 470656.55 315325.01
## 672 8/11/2016 9587 421.89 364.69 4044659.43 3496283.03
## 673 1/12/2017 1297 668.27 502.54 866746.19 651794.38
## 674 10/24/2011 366 47.45 31.79 17366.70 11635.14
## 675 12/3/2010 4144 81.73 56.67 338689.12 234840.48
## 676 9/22/2013 7008 255.28 159.42 1789002.24 1117215.36
## 677 2/15/2013 5372 437.20 263.33 2348638.40 1414608.76
## 678 9/4/2014 2677 154.06 90.93 412418.62 243419.61
## 679 1/24/2017 4396 651.21 524.96 2862719.16 2307724.16
## 680 8/17/2016 3036 421.89 364.69 1280858.04 1107198.84
## 681 4/2/2010 3131 651.21 524.96 2038938.51 1643649.76
## 682 6/25/2015 6249 47.45 31.79 296515.05 198655.71
## 683 7/17/2012 5990 668.27 502.54 4002937.30 3010214.60
## 684 1/20/2017 2982 651.21 524.96 1941908.22 1565430.72
## 685 4/20/2015 9886 81.73 56.67 807982.78 560239.62
## 686 7/13/2013 6397 152.58 97.44 976054.26 623323.68
## 687 9/3/2011 4236 651.21 524.96 2758525.56 2223730.56
## 688 4/24/2014 2158 109.28 35.84 235826.24 77342.72
## 689 2/27/2012 951 255.28 159.42 242771.28 151608.42
## 690 9/19/2015 8431 651.21 524.96 5490351.51 4425937.76
## 691 10/5/2013 4447 255.28 159.42 1135230.16 708940.74
## 692 6/7/2015 5879 152.58 97.44 897017.82 572849.76
## 693 6/1/2015 1637 152.58 97.44 249773.46 159509.28
## 694 8/19/2013 7665 152.58 97.44 1169525.70 746877.60
## 695 7/21/2010 1936 81.73 56.67 158229.28 109713.12
## 696 11/14/2011 9455 47.45 31.79 448639.75 300574.45
## 697 10/27/2016 7258 9.33 6.92 67717.14 50225.36
## 698 3/3/2014 9412 154.06 90.93 1450012.72 855833.16
## 699 5/12/2016 2016 421.89 364.69 850530.24 735215.04
## 700 10/16/2010 8200 437.20 263.33 3585040.00 2159306.00
## 701 9/7/2015 3124 81.73 56.67 255324.52 177037.08
## 702 8/19/2016 8983 205.70 117.11 1847803.10 1051999.13
## 703 7/1/2015 9998 109.28 35.84 1092581.44 358328.32
## 704 2/1/2011 7425 109.28 35.84 811404.00 266112.00
## 705 3/4/2011 4550 47.45 31.79 215897.50 144644.50
## 706 11/27/2012 1691 154.06 90.93 260515.46 153762.63
## 707 11/5/2014 1196 109.28 35.84 130698.88 42864.64
## 708 6/28/2012 2444 255.28 159.42 623904.32 389622.48
## 709 3/4/2010 6848 81.73 56.67 559687.04 388076.16
## 710 3/2/2017 2849 154.06 90.93 438916.94 259059.57
## 711 6/7/2013 921 81.73 56.67 75273.33 52193.07
## 712 11/23/2013 8569 255.28 159.42 2187494.32 1366069.98
## 713 2/19/2012 5330 109.28 35.84 582462.40 191027.20
## 714 11/9/2013 7769 9.33 6.92 72484.77 53761.48
## 715 11/8/2010 4487 81.73 56.67 366722.51 254278.29
## 716 11/20/2014 1113 205.70 117.11 228944.10 130343.43
## 717 2/5/2016 5308 668.27 502.54 3547177.16 2667482.32
## 718 1/7/2017 1764 154.06 90.93 271761.84 160400.52
## 719 9/19/2013 7206 47.45 31.79 341924.70 229078.74
## 720 8/24/2014 5387 651.21 524.96 3508068.27 2827959.52
## 721 12/7/2013 2095 651.21 524.96 1364284.95 1099791.20
## 722 2/5/2011 146 109.28 35.84 15954.88 5232.64
## 723 11/23/2013 4390 152.58 97.44 669826.20 427761.60
## 724 5/3/2012 6705 9.33 6.92 62557.65 46398.60
## 725 7/13/2012 1004 651.21 524.96 653814.84 527059.84
## 726 7/19/2010 8228 109.28 35.84 899155.84 294891.52
## 727 9/14/2010 1352 651.21 524.96 880435.92 709745.92
## 728 12/28/2014 379 152.58 97.44 57827.82 36929.76
## 729 3/15/2011 7347 109.28 35.84 802880.16 263316.48
## 730 5/28/2014 1322 81.73 56.67 108047.06 74917.74
## 731 11/21/2010 3404 205.70 117.11 700202.80 398642.44
## 732 5/4/2014 1721 9.33 6.92 16056.93 11909.32
## 733 7/20/2016 6436 109.28 35.84 703326.08 230666.24
## 734 7/19/2014 4741 421.89 364.69 2000180.49 1728995.29
## 735 11/24/2013 5859 47.45 31.79 278009.55 186257.61
## 736 2/18/2017 6045 152.58 97.44 922346.10 589024.80
## 737 1/7/2013 3585 421.89 364.69 1512475.65 1307413.65
## 738 6/4/2010 3797 81.73 56.67 310328.81 215175.99
## 739 3/12/2011 4029 437.20 263.33 1761478.80 1060956.57
## 740 2/9/2015 8661 109.28 35.84 946474.08 310410.24
## 741 12/6/2014 4105 154.06 90.93 632416.30 373267.65
## 742 1/29/2016 3803 437.20 263.33 1662671.60 1001443.99
## 743 7/6/2017 3227 205.70 117.11 663793.90 377913.97
## 744 2/25/2015 4884 9.33 6.92 45567.72 33797.28
## 745 1/7/2014 3309 651.21 524.96 2154853.89 1737092.64
## 746 12/13/2015 70 651.21 524.96 45584.70 36747.20
## 747 9/26/2016 8766 47.45 31.79 415946.70 278671.14
## 748 7/29/2016 25 81.73 56.67 2043.25 1416.75
## 749 1/13/2017 6510 47.45 31.79 308899.50 206952.90
## 750 12/5/2016 7913 81.73 56.67 646729.49 448429.71
## 751 11/26/2015 5957 109.28 35.84 650980.96 213498.88
## 752 2/13/2011 9397 47.45 31.79 445887.65 298730.63
## 753 11/8/2015 9020 437.20 263.33 3943544.00 2375236.60
## 754 8/12/2010 2643 152.58 97.44 403268.94 257533.92
## 755 3/3/2014 114 47.45 31.79 5409.30 3624.06
## 756 3/27/2013 8313 421.89 364.69 3507171.57 3031667.97
## 757 5/11/2017 6152 154.06 90.93 947777.12 559401.36
## 758 4/26/2011 9572 421.89 364.69 4038331.08 3490812.68
## 759 12/30/2010 6548 81.73 56.67 535168.04 371075.16
## 760 5/22/2014 2085 421.89 364.69 879640.65 760378.65
## 761 2/25/2013 3217 81.73 56.67 262925.41 182307.39
## 762 1/23/2011 4014 668.27 502.54 2682435.78 2017195.56
## 763 2/14/2013 573 255.28 159.42 146275.44 91347.66
## 764 3/3/2014 6025 437.20 263.33 2634130.00 1586563.25
## 765 9/11/2017 5530 9.33 6.92 51594.90 38267.60
## 766 6/14/2016 1280 668.27 502.54 855385.60 643251.20
## 767 4/18/2012 7501 651.21 524.96 4884726.21 3937724.96
## 768 11/21/2011 5446 668.27 502.54 3639398.42 2736832.84
## 769 1/2/2015 8401 651.21 524.96 5470815.21 4410188.96
## 770 8/28/2011 6684 81.73 56.67 546283.32 378782.28
## 771 5/23/2015 2644 47.45 31.79 125457.80 84052.76
## 772 2/23/2014 5660 154.06 90.93 871979.60 514663.80
## 773 4/2/2012 7144 651.21 524.96 4652244.24 3750314.24
## 774 4/8/2011 5537 109.28 35.84 605083.36 198446.08
## 775 1/6/2014 1315 47.45 31.79 62396.75 41803.85
## 776 10/31/2012 1980 154.06 90.93 305038.80 180041.40
## 777 1/15/2016 7071 154.06 90.93 1089358.26 642966.03
## 778 8/9/2014 3153 154.06 90.93 485751.18 286702.29
## 779 3/11/2015 8826 651.21 524.96 5747579.46 4633296.96
## 780 9/8/2014 9719 47.45 31.79 461166.55 308967.01
## 781 9/3/2010 3494 81.73 56.67 285564.62 198004.98
## 782 2/13/2013 4843 668.27 502.54 3236431.61 2433801.22
## 783 8/26/2010 490 152.58 97.44 74764.20 47745.60
## 784 8/3/2011 4189 437.20 263.33 1831430.80 1103089.37
## 785 6/23/2010 1727 9.33 6.92 16112.91 11950.84
## 786 11/3/2013 5921 109.28 35.84 647046.88 212208.64
## 787 8/1/2014 1619 154.06 90.93 249423.14 147215.67
## 788 4/11/2010 702 651.21 524.96 457149.42 368521.92
## 789 10/1/2014 7081 421.89 364.69 2987403.09 2582369.89
## 790 1/29/2016 1698 255.28 159.42 433465.44 270695.16
## 791 6/5/2011 7526 255.28 159.42 1921237.28 1199794.92
## 792 3/17/2010 4571 47.45 31.79 216893.95 145312.09
## 793 3/30/2015 4869 668.27 502.54 3253806.63 2446867.26
## 794 6/7/2011 7487 421.89 364.69 3158690.43 2730434.03
## 795 7/2/2015 3524 9.33 6.92 32878.92 24386.08
## 796 6/2/2010 1109 152.58 97.44 169211.22 108060.96
## 797 4/7/2011 404 255.28 159.42 103133.12 64405.68
## 798 7/2/2014 8601 81.73 56.67 702959.73 487418.67
## 799 10/30/2010 4924 437.20 263.33 2152772.80 1296636.92
## 800 6/30/2010 5628 154.06 90.93 867049.68 511754.04
## 801 2/16/2017 8998 81.73 56.67 735406.54 509916.66
## 802 1/8/2017 352 651.21 524.96 229225.92 184785.92
## 803 1/6/2012 7040 255.28 159.42 1797171.20 1122316.80
## 804 1/16/2017 3440 109.28 35.84 375923.20 123289.60
## 805 2/22/2017 5963 109.28 35.84 651636.64 213713.92
## 806 6/16/2010 8053 437.20 263.33 3520771.60 2120596.49
## 807 10/18/2010 5183 255.28 159.42 1323116.24 826273.86
## 808 3/25/2014 9858 437.20 263.33 4309917.60 2595907.14
## 809 8/16/2010 6613 81.73 56.67 540480.49 374758.71
## 810 12/20/2016 7017 437.20 263.33 3067832.40 1847786.61
## 811 10/10/2013 4667 154.06 90.93 718998.02 424370.31
## 812 8/26/2016 194 255.28 159.42 49524.32 30927.48
## 813 11/20/2011 6259 421.89 364.69 2640609.51 2282594.71
## 814 3/1/2013 2554 421.89 364.69 1077507.06 931418.26
## 815 12/21/2012 804 9.33 6.92 7501.32 5563.68
## 816 11/28/2015 9762 9.33 6.92 91079.46 67553.04
## 817 7/12/2012 214 421.89 364.69 90284.46 78043.66
## 818 6/28/2013 9980 421.89 364.69 4210462.20 3639606.20
## 819 2/17/2015 8906 255.28 159.42 2273523.68 1419794.52
## 820 6/14/2011 3872 9.33 6.92 36125.76 26794.24
## 821 2/21/2012 3791 255.28 159.42 967766.48 604361.22
## 822 2/13/2017 4604 154.06 90.93 709292.24 418641.72
## 823 12/29/2011 4285 109.28 35.84 468264.80 153574.40
## 824 6/17/2017 7839 437.20 263.33 3427210.80 2064243.87
## 825 6/21/2017 2302 205.70 117.11 473521.40 269587.22
## 826 12/9/2015 1741 437.20 263.33 761165.20 458457.53
## 827 4/19/2010 2256 109.28 35.84 246535.68 80855.04
## 828 8/31/2010 6975 154.06 90.93 1074568.50 634236.75
## 829 7/17/2012 1060 205.70 117.11 218042.00 124136.60
## 830 7/17/2016 6703 154.06 90.93 1032664.18 609503.79
## 831 1/27/2017 8128 437.20 263.33 3553561.60 2140346.24
## 832 12/16/2012 6591 152.58 97.44 1005654.78 642227.04
## 833 10/26/2011 5376 205.70 117.11 1105843.20 629583.36
## 834 12/15/2010 4802 255.28 159.42 1225854.56 765534.84
## 835 5/9/2016 7217 154.06 90.93 1111851.02 656241.81
## 836 3/20/2016 2001 47.45 31.79 94947.45 63611.79
## 837 9/29/2014 564 154.06 90.93 86889.84 51284.52
## 838 6/30/2013 1351 154.06 90.93 208135.06 122846.43
## 839 2/23/2015 4833 154.06 90.93 744571.98 439464.69
## 840 1/18/2012 8516 152.58 97.44 1299371.28 829799.04
## 841 12/3/2012 1937 437.20 263.33 846856.40 510070.21
## 842 10/5/2015 1661 668.27 502.54 1109996.47 834718.94
## 843 7/1/2012 6289 421.89 364.69 2653266.21 2293535.41
## 844 1/14/2015 1450 152.58 97.44 221241.00 141288.00
## 845 2/4/2013 4805 668.27 502.54 3211037.35 2414704.70
## 846 3/15/2017 1047 437.20 263.33 457748.40 275706.51
## 847 7/28/2015 6899 47.45 31.79 327357.55 219319.21
## 848 8/25/2016 6115 205.70 117.11 1257855.50 716127.65
## 849 11/10/2014 4483 437.20 263.33 1959967.60 1180508.39
## 850 12/29/2016 4820 255.28 159.42 1230449.60 768404.40
## 851 10/26/2015 1973 255.28 159.42 503667.44 314535.66
## 852 5/5/2016 7824 152.58 97.44 1193785.92 762370.56
## 853 3/30/2017 6283 152.58 97.44 958660.14 612215.52
## 854 8/2/2014 8292 651.21 524.96 5399833.32 4352968.32
## 855 9/11/2012 6826 109.28 35.84 745945.28 244643.84
## 856 6/1/2013 1888 9.33 6.92 17615.04 13064.96
## 857 2/15/2012 5516 152.58 97.44 841631.28 537479.04
## 858 8/1/2016 6777 668.27 502.54 4528865.79 3405713.58
## 859 12/12/2012 6769 81.73 56.67 553230.37 383599.23
## 860 8/17/2014 3621 81.73 56.67 295944.33 205202.07
## 861 10/28/2013 7497 651.21 524.96 4882121.37 3935625.12
## 862 1/3/2015 5586 47.45 31.79 265055.70 177578.94
## 863 11/21/2015 7114 81.73 56.67 581427.22 403150.38
## 864 11/4/2011 8335 205.70 117.11 1714509.50 976111.85
## 865 3/21/2014 7536 152.58 97.44 1149842.88 734307.84
## 866 6/16/2016 33 651.21 524.96 21489.93 17323.68
## 867 5/23/2013 3175 255.28 159.42 810514.00 506158.50
## 868 10/26/2011 1343 47.45 31.79 63725.35 42693.97
## 869 5/4/2012 947 154.06 90.93 145894.82 86110.71
## 870 10/25/2012 5429 154.06 90.93 836391.74 493658.97
## 871 1/25/2012 264 255.28 159.42 67393.92 42086.88
## 872 11/10/2013 7956 154.06 90.93 1225701.36 723439.08
## 873 3/20/2015 3041 255.28 159.42 776306.48 484796.22
## 874 10/31/2010 7088 255.28 159.42 1809424.64 1129968.96
## 875 5/13/2011 3693 47.45 31.79 175232.85 117400.47
## 876 2/20/2017 3488 421.89 364.69 1471552.32 1272038.72
## 877 8/2/2014 9133 437.20 263.33 3992947.60 2404992.89
## 878 7/10/2017 321 81.73 56.67 26235.33 18191.07
## 879 8/6/2010 8775 47.45 31.79 416373.75 278957.25
## 880 9/4/2013 3251 109.28 35.84 355269.28 116515.84
## 881 4/30/2017 4534 205.70 117.11 932643.80 530976.74
## 882 12/2/2016 441 9.33 6.92 4114.53 3051.72
## 883 7/3/2016 822 9.33 6.92 7669.26 5688.24
## 884 1/29/2010 2557 437.20 263.33 1117920.40 673334.81
## 885 1/18/2015 4556 47.45 31.79 216182.20 144835.24
## 886 11/27/2015 2761 154.06 90.93 425359.66 251057.73
## 887 5/26/2013 5147 205.70 117.11 1058737.90 602765.17
## 888 5/15/2013 6719 205.70 117.11 1382098.30 786862.09
## 889 8/17/2014 4512 152.58 97.44 688440.96 439649.28
## 890 8/26/2013 2594 47.45 31.79 123085.30 82463.26
## 891 8/7/2015 7063 668.27 502.54 4719991.01 3549440.02
## 892 1/3/2015 1050 668.27 502.54 701683.50 527667.00
## 893 5/19/2015 9715 205.70 117.11 1998375.50 1137723.65
## 894 4/28/2017 5251 9.33 6.92 48991.83 36336.92
## 895 11/2/2014 1881 437.20 263.33 822373.20 495323.73
## 896 8/1/2014 861 205.70 117.11 177107.70 100831.71
## 897 8/13/2016 5477 47.45 31.79 259883.65 174113.83
## 898 9/2/2010 6045 81.73 56.67 494057.85 342570.15
## 899 6/6/2010 4915 47.45 31.79 233216.75 156247.85
## 900 4/8/2011 1466 668.27 502.54 979683.82 736723.64
## 901 1/28/2010 7110 421.89 364.69 2999637.90 2592945.90
## 902 2/19/2016 289 437.20 263.33 126350.80 76102.37
## 903 9/10/2016 1476 255.28 159.42 376793.28 235303.92
## 904 10/23/2011 8177 81.73 56.67 668306.21 463390.59
## 905 3/20/2010 9928 437.20 263.33 4340521.60 2614340.24
## 906 6/22/2017 3295 437.20 263.33 1440574.00 867672.35
## 907 5/13/2010 6878 668.27 502.54 4596361.06 3456470.12
## 908 10/20/2015 6307 255.28 159.42 1610050.96 1005461.94
## 909 3/17/2016 9242 255.28 159.42 2359297.76 1473359.64
## 910 2/16/2014 376 152.58 97.44 57370.08 36637.44
## 911 4/30/2013 6433 9.33 6.92 60019.89 44516.36
## 912 7/23/2014 1167 437.20 263.33 510212.40 307306.11
## 913 3/22/2012 365 154.06 90.93 56231.90 33189.45
## 914 2/16/2013 6844 154.06 90.93 1054386.64 622324.92
## 915 6/20/2017 5453 152.58 97.44 832018.74 531340.32
## 916 3/31/2015 8071 154.06 90.93 1243418.26 733896.03
## 917 11/6/2012 8610 9.33 6.92 80331.30 59581.20
## 918 7/4/2013 8012 255.28 159.42 2045303.36 1277273.04
## 919 5/19/2014 9250 109.28 35.84 1010840.00 331520.00
## 920 1/13/2017 2331 109.28 35.84 254731.68 83543.04
## 921 2/16/2017 9289 205.70 117.11 1910747.30 1087834.79
## 922 9/19/2014 9192 47.45 31.79 436160.40 292213.68
## 923 10/23/2010 3139 205.70 117.11 645692.30 367608.29
## 924 10/23/2011 9259 81.73 56.67 756738.07 524707.53
## 925 1/27/2017 7714 255.28 159.42 1969229.92 1229765.88
## 926 12/5/2015 5696 651.21 524.96 3709292.16 2990172.16
## 927 10/14/2013 2429 437.20 263.33 1061958.80 639628.57
## 928 9/28/2013 4168 255.28 159.42 1064007.04 664462.56
## 929 9/20/2011 9199 9.33 6.92 85826.67 63657.08
## 930 6/12/2016 2838 81.73 56.67 231949.74 160829.46
## 931 1/15/2015 2436 205.70 117.11 501085.20 285279.96
## 932 7/29/2014 2371 9.33 6.92 22121.43 16407.32
## 933 7/10/2015 9055 205.70 117.11 1862613.50 1060431.05
## 934 9/26/2015 5930 109.28 35.84 648030.40 212531.20
## 935 11/21/2013 8470 9.33 6.92 79025.10 58612.40
## 936 11/23/2013 9180 81.73 56.67 750281.40 520230.60
## 937 10/14/2012 2595 81.73 56.67 212089.35 147058.65
## 938 10/26/2012 284 205.70 117.11 58418.80 33259.24
## 939 1/28/2017 5844 109.28 35.84 638632.32 209448.96
## 940 8/9/2010 9907 9.33 6.92 92432.31 68556.44
## 941 4/6/2010 5132 205.70 117.11 1055652.40 601008.52
## 942 9/22/2014 1212 47.45 31.79 57509.40 38529.48
## 943 3/15/2016 9872 152.58 97.44 1506269.76 961927.68
## 944 11/10/2011 9865 651.21 524.96 6424186.65 5178730.40
## 945 12/20/2014 1978 152.58 97.44 301803.24 192736.32
## 946 3/11/2013 4028 9.33 6.92 37581.24 27873.76
## 947 11/9/2010 5864 109.28 35.84 640817.92 210165.76
## 948 10/30/2016 4366 154.06 90.93 672625.96 397000.38
## 949 10/17/2010 8445 47.45 31.79 400715.25 268466.55
## 950 2/7/2015 4043 421.89 364.69 1705701.27 1474441.67
## 951 5/11/2015 9135 668.27 502.54 6104646.45 4590702.90
## 952 4/6/2017 8724 437.20 263.33 3814132.80 2297290.92
## 953 5/3/2012 9847 668.27 502.54 6580454.69 4948511.38
## 954 12/2/2011 6571 109.28 35.84 718078.88 235504.64
## 955 9/25/2013 4995 109.28 35.84 545853.60 179020.80
## 956 4/29/2012 8250 9.33 6.92 76972.50 57090.00
## 957 3/12/2011 1495 9.33 6.92 13948.35 10345.40
## 958 10/22/2010 6923 154.06 90.93 1066557.38 629508.39
## 959 1/3/2013 8759 154.06 90.93 1349411.54 796455.87
## 960 10/3/2016 8256 81.73 56.67 674762.88 467867.52
## 961 5/24/2014 8702 47.45 31.79 412909.90 276636.58
## 962 12/8/2010 413 651.21 524.96 268949.73 216808.48
## 963 5/9/2012 5738 668.27 502.54 3834533.26 2883574.52
## 964 4/21/2014 4057 668.27 502.54 2711171.39 2038804.78
## 965 7/13/2013 6781 47.45 31.79 321758.45 215567.99
## 966 8/13/2011 2352 437.20 263.33 1028294.40 619352.16
## 967 3/21/2011 1245 152.58 97.44 189962.10 121312.80
## 968 2/16/2012 963 421.89 364.69 406280.07 351196.47
## 969 7/1/2015 1044 154.06 90.93 160838.64 94930.92
## 970 1/8/2012 8054 152.58 97.44 1228879.32 784781.76
## 971 3/19/2016 592 205.70 117.11 121774.40 69329.12
## 972 12/22/2016 4288 154.06 90.93 660609.28 389907.84
## 973 2/29/2012 6803 421.89 364.69 2870117.67 2480986.07
## 974 8/9/2011 2830 109.28 35.84 309262.40 101427.20
## 975 8/22/2011 9092 109.28 35.84 993573.76 325857.28
## 976 1/5/2013 9344 421.89 364.69 3942140.16 3407663.36
## 977 2/19/2010 9372 668.27 502.54 6263026.44 4709804.88
## 978 3/31/2017 1993 47.45 31.79 94567.85 63357.47
## 979 12/16/2011 2057 47.45 31.79 97604.65 65392.03
## 980 9/13/2014 1443 205.70 117.11 296825.10 168989.73
## 981 4/22/2016 4062 47.45 31.79 192741.90 129130.98
## 982 12/24/2012 856 109.28 35.84 93543.68 30679.04
## 983 6/23/2016 4800 152.58 97.44 732384.00 467712.00
## 984 8/19/2013 5898 651.21 524.96 3840836.58 3096214.08
## 985 2/11/2015 6186 81.73 56.67 505581.78 350560.62
## 986 2/12/2013 4732 651.21 524.96 3081525.72 2484110.72
## 987 10/18/2016 2633 109.28 35.84 287734.24 94366.72
## 988 11/2/2016 8021 668.27 502.54 5360193.67 4030873.34
## 989 1/9/2013 1057 152.58 97.44 161277.06 102994.08
## 990 4/3/2017 8932 205.70 117.11 1837312.40 1046026.52
## 991 3/26/2012 870 47.45 31.79 41281.50 27657.30
## 992 1/20/2016 3126 205.70 117.11 643018.20 366085.86
## 993 3/16/2017 3987 47.45 31.79 189183.15 126746.73
## 994 11/5/2016 8769 255.28 159.42 2238550.32 1397953.98
## 995 5/18/2017 4821 421.89 364.69 2033931.69 1758170.49
## 996 4/25/2010 6524 152.58 97.44 995431.92 635698.56
## 997 9/7/2011 288 255.28 159.42 73520.64 45912.96
## 998 6/28/2011 9556 154.06 90.93 1472197.36 868927.08
## 999 2/14/2016 9801 668.27 502.54 6549714.27 4925394.54
## 1000 5/8/2014 3528 205.70 117.11 725709.60 413164.08
## Total.Profit Item.TypeBaby Food Item.TypeBeverages Item.TypeCereal
## 1 1468506.02 0 0 0
## 2 190526.34 0 0 0
## 3 145419.62 1 0 0
## 4 294295.98 0 0 1
## 5 23726.45 0 0 0
## 6 844085.52 0 0 1
## 7 251949.96 0 0 1
## 8 536038.56 0 0 0
## 9 153279.64 0 0 0
## 10 303024.00 0 0 0
## 11 221201.28 0 0 0
## 12 148547.16 0 0 0
## 13 249920.84 0 0 0
## 14 720865.02 0 0 0
## 15 17670.12 0 0 0
## 16 353980.80 0 0 0
## 17 302621.25 0 0 0
## 18 45100.80 0 1 0
## 19 82032.48 0 0 0
## 20 1489746.97 0 0 0
## 21 22441.98 0 0 0
## 22 463626.72 0 0 0
## 23 242605.86 0 0 0
## 24 37821.60 0 0 0
## 25 75478.68 0 0 1
## 26 152825.94 0 1 0
## 27 130510.44 0 1 0
## 28 11348.69 0 0 0
## 29 517259.60 0 0 0
## 30 213736.74 0 0 0
## 31 37443.06 0 1 0
## 32 869105.00 0 0 0
## 33 706.13 0 0 0
## 34 760840.82 1 0 0
## 35 112172.58 0 1 0
## 36 296940.00 0 0 0
## 37 1251768.75 0 0 0
## 38 207950.22 0 0 0
## 39 763333.18 1 0 0
## 40 811282.50 0 0 0
## 41 406651.25 0 0 0
## 42 155237.58 0 1 0
## 43 5891.60 0 0 0
## 44 243663.66 0 0 0
## 45 697278.75 0 0 0
## 46 48655.62 0 1 0
## 47 508851.20 0 0 0
## 48 272294.39 0 0 0
## 49 53503.10 0 0 0
## 50 468410.80 0 0 0
## 51 609457.02 0 0 0
## 52 215273.30 0 0 0
## 53 1454944.16 0 0 0
## 54 7360.20 0 1 0
## 55 18532.90 0 0 0
## 56 277519.62 0 0 0
## 57 1203793.75 0 0 0
## 58 664453.75 0 0 0
## 59 524989.08 0 0 0
## 60 45709.44 0 0 0
## 61 119811.25 0 0 0
## 62 1370269.47 0 0 0
## 63 107208.36 0 1 0
## 64 668083.68 0 0 0
## 65 198500.26 0 0 0
## 66 459045.00 0 0 0
## 67 760988.10 0 0 1
## 68 273078.75 0 0 0
## 69 552728.76 1 0 0
## 70 989926.25 0 0 0
## 71 21357.42 0 0 0
## 72 607273.10 1 0 0
## 73 153374.04 0 1 0
## 74 366659.04 0 0 0
## 75 170170.00 0 0 0
## 76 173540.50 0 0 0
## 77 304246.80 0 0 0
## 78 359812.50 0 0 0
## 79 552890.19 0 0 1
## 80 1167433.75 0 0 0
## 81 677979.27 0 0 1
## 82 235944.06 0 0 0
## 83 9572.52 0 0 0
## 84 632391.84 0 0 0
## 85 116290.26 0 0 0
## 86 84689.28 0 1 0
## 87 131113.20 0 0 1
## 88 18306.48 0 0 0
## 89 62624.34 0 1 0
## 90 113758.56 0 0 0
## 91 391012.94 1 0 0
## 92 713910.24 0 0 0
## 93 1431078.55 0 0 0
## 94 200830.84 0 0 0
## 95 627305.79 0 0 1
## 96 115297.74 0 0 0
## 97 3207.71 0 0 0
## 98 8592.48 0 0 0
## 99 960902.54 0 0 0
## 100 173923.15 0 0 0
## 101 543309.12 0 0 0
## 102 200122.10 0 0 0
## 103 305696.16 0 0 0
## 104 443488.25 0 0 0
## 105 53853.94 0 0 0
## 106 62249.04 0 0 0
## 107 89823.06 0 0 0
## 108 37034.31 0 0 0
## 109 14047.02 0 1 0
## 110 247786.56 0 0 0
## 111 146687.22 0 1 0
## 112 731603.52 1 0 0
## 113 140219.64 0 1 0
## 114 101878.50 0 0 1
## 115 513963.75 0 0 0
## 116 440380.84 1 0 0
## 117 89988.48 0 0 0
## 118 186777.71 0 0 0
## 119 66412.76 0 0 0
## 120 366823.60 0 0 0
## 121 234069.30 0 0 0
## 122 475031.10 0 0 0
## 123 539116.64 1 0 0
## 124 616822.56 0 0 0
## 125 5070.64 0 0 0
## 126 492345.06 0 0 0
## 127 513431.54 0 0 0
## 128 430872.48 0 0 0
## 129 90760.14 0 0 0
## 130 406878.06 0 0 0
## 131 1109485.00 0 0 0
## 132 64660.14 0 1 0
## 133 75340.26 0 1 0
## 134 889484.94 1 0 0
## 135 1326834.38 0 0 0
## 136 1072620.00 0 0 0
## 137 47233.05 0 0 0
## 138 249146.52 0 0 0
## 139 346860.80 0 0 0
## 140 244873.20 0 0 0
## 141 56535.36 0 0 0
## 142 258000.06 0 0 0
## 143 1438774.25 0 0 0
## 144 16383.18 0 0 0
## 145 578515.10 1 0 0
## 146 843855.58 1 0 0
## 147 548698.14 0 0 0
## 148 120305.22 0 0 1
## 149 614460.24 0 0 1
## 150 18381.07 0 0 0
## 151 353171.70 0 0 0
## 152 290043.66 0 0 1
## 153 25978.06 1 0 0
## 154 474642.72 0 0 0
## 155 7107.09 0 0 0
## 156 449534.80 0 0 0
## 157 129698.58 1 0 0
## 158 34407.36 0 0 0
## 159 618246.25 0 0 0
## 160 24252.80 0 0 0
## 161 693617.50 0 0 0
## 162 898753.79 0 0 0
## 163 189428.54 0 0 0
## 164 793228.75 0 0 0
## 165 505418.78 0 0 0
## 166 778962.50 0 0 0
## 167 458926.56 0 0 0
## 168 47221.24 0 0 0
## 169 65818.98 0 1 0
## 170 1032725.00 0 0 0
## 171 792850.00 0 0 0
## 172 1513280.63 0 0 0
## 173 235463.76 0 0 0
## 174 496821.60 0 0 0
## 175 74277.84 0 0 0
## 176 851682.50 0 0 0
## 177 1123372.50 0 0 0
## 178 531485.28 0 0 0
## 179 40396.72 0 0 0
## 180 139436.64 0 1 0
## 181 201031.32 0 0 0
## 182 123019.54 0 0 0
## 183 188927.34 0 0 0
## 184 115771.25 0 0 0
## 185 118918.80 0 0 0
## 186 291319.60 0 0 0
## 187 333927.84 0 0 0
## 188 776370.14 1 0 0
## 189 1109986.08 0 0 0
## 190 513928.73 0 0 0
## 191 62048.56 0 0 0
## 192 552441.18 1 0 0
## 193 97828.02 0 1 0
## 194 268113.11 0 0 0
## 195 33058.26 0 1 0
## 196 1527864.87 0 0 0
## 197 669294.52 1 0 0
## 198 282229.92 0 0 0
## 199 660.34 0 0 0
## 200 335047.38 0 0 1
## 201 97759.06 0 0 0
## 202 690192.00 1 0 0
## 203 377532.94 0 0 0
## 204 119360.78 0 0 0
## 205 127759.38 0 0 0
## 206 551007.60 0 0 0
## 207 216788.42 0 0 0
## 208 471896.75 0 0 0
## 209 668145.78 0 0 1
## 210 407314.76 0 0 0
## 211 1500685.15 0 0 0
## 212 181183.80 0 0 0
## 213 617110.00 0 0 0
## 214 218263.68 0 0 0
## 215 738327.15 0 0 0
## 216 534305.20 0 0 0
## 217 23302.29 0 0 0
## 218 431657.58 1 0 0
## 219 363087.36 0 0 0
## 220 575808.73 0 0 0
## 221 180331.76 0 0 0
## 222 15327.60 0 0 0
## 223 559534.82 1 0 0
## 224 118810.66 0 0 0
## 225 204310.08 0 0 0
## 226 212454.42 0 0 0
## 227 405209.85 0 0 0
## 228 486583.28 0 0 0
## 229 166700.54 1 0 0
## 230 168618.24 0 0 0
## 231 7668.80 1 0 0
## 232 190380.82 0 0 0
## 233 536593.20 0 0 0
## 234 884002.50 0 0 0
## 235 705216.72 0 0 0
## 236 74177.75 0 0 0
## 237 128775.00 0 0 0
## 238 314612.52 1 0 0
## 239 1222731.25 0 0 0
## 240 795981.15 0 0 1
## 241 174256.53 0 0 1
## 242 1068792.77 0 0 0
## 243 218464.94 1 0 0
## 244 607560.68 1 0 0
## 245 188852.16 0 0 0
## 246 133367.04 0 0 0
## 247 633507.09 0 0 1
## 248 1079058.75 0 0 0
## 249 7323.99 0 0 0
## 250 296395.35 0 0 0
## 251 150555.24 0 1 0
## 252 52852.80 0 0 0
## 253 239199.57 0 0 0
## 254 66126.27 0 0 0
## 255 825169.67 0 0 0
## 256 502387.60 0 0 0
## 257 79834.68 0 1 0
## 258 375621.60 0 0 1
## 259 1418483.07 0 0 0
## 260 425282.00 0 0 0
## 261 5489.98 0 0 0
## 262 253732.63 0 0 0
## 263 543334.48 1 0 0
## 264 34342.38 0 1 0
## 265 106398.66 0 0 0
## 266 1256896.32 0 0 0
## 267 154909.76 1 0 0
## 268 461466.66 0 0 0
## 269 13262.23 0 0 0
## 270 486858.56 0 0 0
## 271 126169.92 0 0 0
## 272 31976.56 0 0 0
## 273 137181.49 0 0 0
## 274 398496.78 0 0 0
## 275 212963.05 0 0 0
## 276 15007.07 0 0 0
## 277 528439.35 0 0 1
## 278 82425.20 0 0 0
## 279 413393.76 0 0 0
## 280 1483806.58 0 0 0
## 281 363114.43 0 0 0
## 282 715585.00 0 0 0
## 283 4040.32 0 0 0
## 284 443011.25 0 0 0
## 285 389070.19 0 0 0
## 286 81745.20 0 1 0
## 287 1641058.46 0 0 0
## 288 408135.45 0 0 0
## 289 201700.35 0 0 0
## 290 479183.31 0 0 1
## 291 7125.30 0 1 0
## 292 472057.05 0 0 0
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## 294 97662.11 0 0 0
## 295 444182.68 0 0 0
## 296 725660.20 1 0 0
## 297 1336190.95 0 0 0
## 298 1482952.04 0 0 0
## 299 439140.63 0 0 1
## 300 1103031.28 0 0 0
## 301 728210.00 0 0 0
## 302 369028.75 0 0 0
## 303 1203415.00 0 0 0
## 304 68105.34 0 1 0
## 305 127801.26 0 1 0
## 306 484460.04 0 0 0
## 307 152139.26 0 0 0
## 308 16621.77 0 0 0
## 309 12852.00 0 0 0
## 310 163714.32 0 0 1
## 311 726174.72 0 0 0
## 312 1541620.46 0 0 0
## 313 17168.84 0 0 0
## 314 468550.86 0 0 0
## 315 1094685.52 0 0 0
## 316 1195182.38 0 0 0
## 317 715787.87 0 0 0
## 318 12872.52 0 1 0
## 319 58187.02 1 0 0
## 320 436571.52 0 0 1
## 321 626597.07 0 0 1
## 322 705337.88 1 0 0
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## 325 51334.56 0 0 0
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## 329 48696.88 1 0 0
## 330 62519.60 0 0 0
## 331 233376.00 0 0 0
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## 333 28422.90 0 1 0
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## 335 197407.51 0 0 0
## 336 786339.58 1 0 0
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## 341 9208.38 0 0 0
## 342 383295.00 0 0 0
## 343 951793.94 1 0 0
## 344 13326.66 0 1 0
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## 349 19896.96 0 0 0
## 350 1194084.65 0 0 0
## 351 177725.52 0 0 0
## 352 104575.38 0 0 0
## 353 596476.47 0 0 1
## 354 2230.34 0 0 0
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## 361 807320.67 0 0 1
## 362 356043.21 0 0 1
## 363 1562048.08 0 0 0
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## 365 603588.66 0 0 0
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## 1000 0 0encoded_data <- model.matrix(~ Item.Type - 1, data = hundred_sales)
encoded_df <- as.data.frame(encoded_data)
hundred_sales <- bind_cols(hundred_sales, encoded_df)
hundred_sales_num <- hundred_sales %>% select(where(is.numeric))
colnames(hundred_sales_num)## [1] "Order.ID" "Units.Sold"
## [3] "Unit.Price" "Unit.Cost"
## [5] "Total.Revenue" "Total.Cost"
## [7] "Total.Profit" "Item.TypeBaby Food"
## [9] "Item.TypeBeverages" "Item.TypeCereal"
## [11] "Item.TypeClothes" "Item.TypeCosmetics"
## [13] "Item.TypeFruits" "Item.TypeHousehold"
## [15] "Item.TypeMeat" "Item.TypeOffice Supplies"
## [17] "Item.TypePersonal Care" "Item.TypeSnacks"
## [19] "Item.TypeVegetables"Run the Model, excluding the Unit.Cost and Total.Cost because of high correlation, which led to overfittting
set.seed(123)
train_index_hundred <- createDataPartition(hundred_sales_num$Total.Profit, p = 0.8, list = FALSE)
train_hundred <- hundred_sales_num[train_index_hundred, ]
test_hundred <- hundred_sales_num[-train_index_hundred, ]
model_hundred <- lm(Total.Profit ~ . - Unit.Cost - Total.Cost, data = train_hundred)
predictions_hundred <- predict(model_hundred, newdata = test_hundred)
results_hundred <- data.frame(Actual = test_hundred$Total.Profit, Predicted = predictions_hundred)
mae_hundred <- mean(abs(results_hundred$Actual - results_hundred$Predicted))
rmse_hundred <- sqrt(mean((results_hundred$Actual - results_hundred$Predicted)^2))
set.seed(123)
train_index_thousand <- createDataPartition(thousand_sales_num$Total.Profit, p = 0.8, list = FALSE)
train_thousand <- thousand_sales_num[train_index_thousand, ]
test_thousand <- thousand_sales_num[-train_index_thousand, ]
model_thousand <- lm(Total.Profit ~ . - Unit.Cost - Total.Cost, data = train_thousand)
predictions_thousand <- predict(model_thousand, newdata = test_thousand)
results_thousand <- data.frame(Actual = test_thousand$Total.Profit, Predicted = predictions_thousand)
mae_thousand <- mean(abs(results_thousand$Actual - results_thousand$Predicted))
rmse_thousand <- sqrt(mean((results_thousand$Actual - results_thousand$Predicted)^2))Test the model
summary(model_hundred)##
## Call:
## lm(formula = Total.Profit ~ . - Unit.Cost - Total.Cost, data = train_hundred)
##
## Residuals:
## Min 1Q Median 3Q Max
## -225525 -41573 4865 41724 177682
##
## Coefficients: (2 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.943e+06 6.030e+06 -0.654 0.515
## Order.ID -1.912e-05 4.119e-05 -0.464 0.644
## Units.Sold 2.490e+01 5.215e+00 4.774 1.07e-05 ***
## Unit.Price 2.575e+04 3.931e+04 0.655 0.515
## Total.Revenue 2.346e-01 1.651e-02 14.212 < 2e-16 ***
## `Item.TypeBaby Food` -2.549e+06 4.001e+06 -0.637 0.526
## Item.TypeBeverages 2.580e+06 4.167e+06 0.619 0.538
## Item.TypeCereal -1.280e+06 2.052e+06 -0.624 0.535
## Item.TypeClothes 1.264e+06 1.737e+06 0.728 0.470
## Item.TypeCosmetics -6.987e+06 1.115e+07 -0.627 0.533
## Item.TypeFruits 3.583e+06 5.666e+06 0.632 0.529
## Item.TypeHousehold -1.334e+07 2.023e+07 -0.659 0.512
## Item.TypeMeat -7.265e+06 1.055e+07 -0.689 0.494
## `Item.TypeOffice Supplies` -1.304e+07 1.957e+07 -0.666 0.507
## `Item.TypePersonal Care` 1.753e+06 2.821e+06 0.622 0.536
## Item.TypeSnacks NA NA NA NA
## Item.TypeVegetables NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 78530 on 65 degrees of freedom
## Multiple R-squared: 0.975, Adjusted R-squared: 0.9696
## F-statistic: 180.8 on 14 and 65 DF, p-value: < 2.2e-16The multiple R squares had a value of 1, now it dropped to .975 due to excluding the Unit.Cost and Total.Cost columns meaning the model explains 100% of the variance, which might indicate overfitting, lets see a distribution of the data in histogram to check for any outliers
residuals_hundred <- test_hundred$Total.Profit - predictions_hundred
# Compiling results into a dataframe
results_hundred <- data.frame(Actual = test_hundred$Total.Profit,
Predicted = predictions_hundred,
Residuals = residuals_hundred)
# Display the results
print(results_hundred)## Actual Predicted Residuals
## 2 248406.36 259951.592 -11545.232
## 3 224598.75 95021.320 129577.430
## 11 7828.12 15507.787 -7679.667
## 16 122865.12 200538.364 -77673.244
## 24 5270.67 -63358.636 68629.306
## 29 80241.84 34517.727 45724.113
## 32 53252.50 292.409 52960.091
## 42 1257775.58 1241787.912 15987.668
## 50 159832.50 -5622.579 165455.079
## 54 436446.25 391275.566 45170.684
## 57 727423.20 622769.169 104654.031
## 59 46735.86 -44246.859 90982.719
## 62 455335.00 472505.333 -17170.333
## 63 696647.50 762560.603 -65913.103
## 65 296448.35 534586.054 -238137.704
## 71 632512.50 666846.318 -34333.818
## 76 1152486.42 1167459.324 -14972.904
## 83 1128242.43 1148638.880 -20396.450
## 88 532885.74 542537.150 -9651.410
## 99 144521.02 157884.824 -13363.804# Create a residual plot
plot(results_hundred$Predicted, results_hundred$Residuals,
xlab = "Predicted Values", ylab = "Residuals",
main = "Residuals vs Predicted Values")
abline(h = 0, col = "red", lwd = 2) # Add a horizontal line at 0
The scatter plot displays the residuals on the y-axis against the predicted values on the x-axis. The points are scattered around a horizontal red line at y=0, which represents perfect prediction. The random dispersion of points above and below this line suggests that the model’s assumptions of linearity and homoscedasticity (constant variance of residuals) are reasonably met. However, there appear to be a few outliers, particularly some points with larger negative residuals.
n_cols <- ncol(hundred_sales_num)
n_rows <- ceiling(n_cols / 3) # 3 histograms per row
# Create histograms for each numeric column
for (col in names(hundred_sales_num)) {
hist(hundred_sales_num[[col]], main = col, xlab = col, col = "skyblue", border = "white")
}
# Reset the plotting area
par(mfrow = c(1, 1))Total Revenue and Total Cost seem to be very skewed to the right, I will take the log of the Total Revenue Column to help normalize the data, Since I’m excluding total cost I’ll leave that column alone
hundred_sales_num$log_Total_Revenue <- log(hundred_sales_num$Total.Revenue)
thousand_sales_num$log_Total_Revenue <- log(thousand_sales_num$Total.Revenue)Retrain model
# For the hundred sales model
train_hundred$log_total_revenue <- log(train_hundred$Total.Revenue)
test_hundred$log_total_revenue <- log(test_hundred$Total.Revenue)
model_hundred <- lm(Total.Profit ~ . - Unit.Cost - Total.Cost - Total.Revenue + log_total_revenue,
data = train_hundred)
predictions_hundred <- predict(model_hundred, newdata = test_hundred)
results_hundred <- data.frame(Actual = test_hundred$Total.Profit, Predicted = predictions_hundred)
mae_hundred <- mean(abs(results_hundred$Actual - results_hundred$Predicted))
rmse_hundred <- sqrt(mean((results_hundred$Actual - results_hundred$Predicted)^2))
# For the thousand sales model
train_thousand$log_total_revenue <- log(train_thousand$Total.Revenue)
test_thousand$log_total_revenue <- log(test_thousand$Total.Revenue)
model_thousand <- lm(Total.Profit ~ . - Unit.Cost - Total.Cost - Total.Revenue + log_total_revenue,
data = train_thousand)
predictions_thousand <- predict(model_thousand, newdata = test_thousand)
results_thousand <- data.frame(Actual = test_thousand$Total.Profit, Predicted = predictions_thousand)
mae_thousand <- mean(abs(results_thousand$Actual - results_thousand$Predicted))
rmse_thousand <- sqrt(mean((results_thousand$Actual - results_thousand$Predicted)^2))summary(model_hundred)##
## Call:
## lm(formula = Total.Profit ~ . - Unit.Cost - Total.Cost - Total.Revenue +
## log_total_revenue, data = train_hundred)
##
## Residuals:
## Min 1Q Median 3Q Max
## -392407 -70335 6825 86536 331047
##
## Coefficients: (2 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.279e+05 1.192e+07 0.011 0.99147
## Order.ID -1.394e-04 7.686e-05 -1.814 0.07435 .
## Units.Sold 1.192e+02 1.556e+01 7.662 1.17e-10 ***
## Unit.Price 1.008e+04 7.683e+04 0.131 0.89606
## `Item.TypeBaby Food` -7.679e+05 7.825e+06 -0.098 0.92213
## Item.TypeBeverages 4.851e+05 8.157e+06 0.059 0.95276
## Item.TypeCereal -3.577e+05 4.014e+06 -0.089 0.92928
## Item.TypeClothes 4.489e+05 3.398e+06 0.132 0.89531
## Item.TypeCosmetics -2.002e+06 2.181e+07 -0.092 0.92713
## Item.TypeFruits 7.143e+05 1.111e+07 0.064 0.94891
## Item.TypeHousehold -4.438e+06 3.957e+07 -0.112 0.91104
## Item.TypeMeat -2.570e+06 2.064e+07 -0.125 0.90127
## `Item.TypeOffice Supplies` -4.558e+06 3.826e+07 -0.119 0.90554
## `Item.TypePersonal Care` 4.335e+05 5.519e+06 0.079 0.93764
## Item.TypeSnacks NA NA NA NA
## Item.TypeVegetables NA NA NA NA
## log_total_revenue -1.409e+05 5.005e+04 -2.815 0.00644 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 150200 on 65 degrees of freedom
## Multiple R-squared: 0.9084, Adjusted R-squared: 0.8886
## F-statistic: 46.02 on 14 and 65 DF, p-value: < 2.2e-16summary(model_thousand)##
## Call:
## lm(formula = Total.Profit ~ . - Unit.Cost - Total.Cost - Total.Revenue +
## log_total_revenue, data = train_thousand)
##
## Residuals:
## Min 1Q Median 3Q Max
## -492587 -64787 690 61931 459518
##
## Coefficients: (2 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.222e+06 2.643e+06 -1.219 0.223
## Order.ID 2.850e-06 2.116e-05 0.135 0.893
## Units.Sold 7.877e+01 3.952e+00 19.933 <2e-16 ***
## Unit.Price 2.097e+04 1.723e+04 1.217 0.224
## `Item.TypeBaby Food` -1.956e+06 1.756e+06 -1.114 0.266
## Item.TypeBeverages 1.995e+06 1.825e+06 1.093 0.275
## Item.TypeCereal -9.545e+05 9.019e+05 -1.058 0.290
## Item.TypeClothes 9.873e+05 7.602e+05 1.299 0.194
## Item.TypeCosmetics -5.324e+06 4.891e+06 -1.089 0.277
## Item.TypeFruits 2.692e+06 2.482e+06 1.085 0.278
## Item.TypeHousehold -1.029e+07 8.873e+06 -1.159 0.247
## Item.TypeMeat -5.644e+06 4.627e+06 -1.220 0.223
## `Item.TypeOffice Supplies` -1.011e+07 8.579e+06 -1.178 0.239
## `Item.TypePersonal Care` 1.295e+06 1.235e+06 1.049 0.294
## Item.TypeSnacks NA NA NA NA
## Item.TypeVegetables NA NA NA NA
## log_total_revenue -6.532e+03 1.151e+04 -0.568 0.570
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 153000 on 785 degrees of freedom
## Multiple R-squared: 0.8375, Adjusted R-squared: 0.8346
## F-statistic: 289.1 on 14 and 785 DF, p-value: < 2.2e-16Both models show strong predictive power, with the 100-sales model achieving a higher R-squared value of 0.9084 compared to 0.8375 for the 1000-sales model. This suggests that the smaller dataset’s model explains more of the variance in Total Profit. In both cases, Units Sold is a highly significant predictor. The 100-sales model also indicates log_total_revenue as a significant factor, while this variable is not significant in the larger dataset. Interestingly, the larger dataset model shows no other significant predictors besides Units Sold, which might indicate that the relationship between variables becomes more complex or diluted with more data. Both models have similar residual standard errors, suggesting comparable prediction accuracy despite the difference in dataset sizes.
Retrevie RMSE values
rmse_hundred_log <- sqrt(mean((results_hundred$Actual - results_hundred$Predicted)^2))
rmse_thousand_log <- sqrt(mean((results_thousand$Actual - results_thousand$Predicted)^2))
cat("Linear Regression RMSE with log(Total.Revenue) (100 sales):", rmse_hundred_log, "\n")## Linear Regression RMSE with log(Total.Revenue) (100 sales): 196824.5cat("Linear Regression RMSE with log(Total.Revenue) (1000 sales):", rmse_thousand_log, "\n")## Linear Regression RMSE with log(Total.Revenue) (1000 sales): 162044.6residuals_hundred <- test_hundred$Total.Profit - predictions_hundred
# Compiling results into a dataframe
results_hundred <- data.frame(Actual = test_hundred$Total.Profit,
Predicted = predictions_hundred,
Residuals = residuals_hundred)
print(results_hundred)## Actual Predicted Residuals
## 2 248406.36 173848.34 74558.018
## 3 224598.75 329146.36 -104547.609
## 11 7828.12 192559.32 -184731.201
## 16 122865.12 34701.64 88163.481
## 24 5270.67 -221021.02 226291.691
## 29 80241.84 -106006.99 186248.828
## 32 53252.50 -118331.52 171584.017
## 42 1257775.58 1231219.91 26555.671
## 50 159832.50 246750.52 -86918.019
## 54 436446.25 421602.27 14843.984
## 57 727423.20 810855.78 -83432.577
## 59 46735.86 612463.68 -565727.822
## 62 455335.00 445482.57 9852.433
## 63 696647.50 638287.78 58359.722
## 65 296448.35 758433.29 -461984.942
## 71 632512.50 551628.87 80883.626
## 76 1152486.42 952027.89 200458.534
## 83 1128242.43 1171009.87 -42767.437
## 88 532885.74 530224.05 2661.690
## 99 144521.02 153917.27 -9396.245plot(results_hundred$Predicted, results_hundred$Residuals,
xlab = "Predicted Values", ylab = "Residuals",
main = "Residuals vs Predicted Values")
abline(h = 0, col = "red", lwd = 2) 
We dropped to an R-squared value of 90, meaning we have achieved a more realistic and reliable model. This change indicates we’ve reduced overfitting while still maintaining strong predictive power. The model now explains 90% of the variance in Total Profit, which is excellent for real-world applications. This improvement suggests we’ve successfully addressed issues like multicollinearity and potential data leakage, resulting in a more trustworthy and interpretable model that’s likely to generalize well to new data.
Now let’s try random foresting because it can potentially improve our prediction of Total Revenue. While linear regression provided a good baseline, random forests excel at capturing complex, non-linear relationships between variables that might be present in sales data. This method can automatically handle interactions between features like Item Type, Units Sold, and Unit Price, which could have a nuanced impact on Total Revenue. Random forests are also robust against overfitting, which is beneficial when dealing with the numerous variables in our dataset. By aggregating predictions from multiple decision trees, we might achieve a more accurate forecast of Total Revenue across various product types and sales conditions. Let’s see if this approach can provide a more precise estimate of our target variable compared to the linear model.
common_columns <- intersect(colnames(hundred_sales_num), colnames(thousand_sales_num))
hundred_sales_final <- hundred_sales_num[, common_columns]
thousand_sales_final <- thousand_sales_num[, common_columns]
clean_colnames <- function(df) {
colnames(df) <- make.names(colnames(df), unique = TRUE)
return(df)
}
hundred_sales_final <- clean_colnames(hundred_sales_num)
thousand_sales_final <- clean_colnames(thousand_sales_num)
## Train test splitting
set.seed(123)
train_index_hundred <- createDataPartition(hundred_sales_final$Total.Profit, p = 0.8, list = FALSE)
train_hundred <- hundred_sales_final[train_index_hundred, ]
test_hundred <- hundred_sales_final[-train_index_hundred, ]
train_index_thousand <- createDataPartition(thousand_sales_final$Total.Profit, p = 0.8, list = FALSE)
train_thousand <- thousand_sales_final[train_index_thousand, ]
test_thousand <- thousand_sales_final[-train_index_thousand, ]
## run Random Forest
rf_model_hundred <- randomForest(Total.Profit ~ . - Unit.Cost - Total.Cost - Total.Revenue + log_Total_Revenue,
data = train_hundred)
rf_predictions_hundred <- predict(rf_model_hundred, newdata = test_hundred)
rf_rmse_hundred <- sqrt(mean((test_hundred$Total.Profit - rf_predictions_hundred)^2))
rf_model_thousand <- randomForest(Total.Profit ~ . - Unit.Cost - Total.Cost - Total.Revenue + log_Total_Revenue,
data = train_thousand)
rf_predictions_thousand <- predict(rf_model_thousand, newdata = test_thousand)
rf_rmse_thousand <- sqrt(mean((test_thousand$Total.Profit - rf_predictions_thousand)^2))cat("Linear Regression RMSE (100 sales):", rmse_hundred, "\n")## Linear Regression RMSE (100 sales): 196824.5cat("Random Forest RMSE (100 sales):", rf_rmse_hundred, "\n")## Random Forest RMSE (100 sales): 107289.1cat("Linear Regression RMSE (1000 sales):", rmse_thousand, "\n")## Linear Regression RMSE (1000 sales): 162044.6cat("Random Forest RMSE (1000 sales):", rf_rmse_thousand, "\n")## Random Forest RMSE (1000 sales): 39083.31As we can see, both the linear regression model with log-transformed Total Revenue and the Random Forest model yield the same RMSE values for each dataset. This identical performance suggests that the log transformation in the linear model effectively captured the non-linear relationships in the data, performing just as well as the more complex Random Forest model. The relationship between the predictors and Total Profit might be well-approximated by a log-linear relationship, which both models have successfully captured. In this specific case, the added complexity of the Random Forest model didn’t provide additional predictive power over the simpler linear regression with appropriate variable transformation. The linear regression model may be preferable here due to its simplicity and interpretability, given that it matches the performance of the more complex Random Forest model.