Exploratory Restaurant Revenue
Jonathan Christopher Pandiana
2024-07-01
1. Explaination
This Exploratory aims to see what aspects of restaurants that influences the revenue of the restaurant. By focusing on the location of the restaurant and also the cuisines of the restaurant, I aim to see what should other restaurant owners do in order to increase their revenue.
2. Input Data
Restaurant <- read.csv("Data/restaurant_data.csv")
head(Restaurant,10)## Name Location Cuisine Rating Seating.Capacity Average.Meal.Price
## 1 Restaurant 0 Rural Japanese 4.0 38 73.98
## 2 Restaurant 1 Downtown Mexican 3.2 76 28.11
## 3 Restaurant 2 Rural Italian 4.7 48 48.29
## 4 Restaurant 3 Rural Italian 4.4 34 51.55
## 5 Restaurant 4 Downtown Japanese 4.9 88 75.98
## 6 Restaurant 5 Downtown Indian 4.4 79 35.69
## 7 Restaurant 6 Rural Mexican 4.9 49 35.30
## 8 Restaurant 7 Suburban Japanese 4.1 61 73.65
## 9 Restaurant 8 Rural Japanese 4.2 37 73.75
## 10 Restaurant 9 Rural French 5.0 32 59.80
## Marketing.Budget Social.Media.Followers Chef.Experience.Years
## 1 2224 23406 13
## 2 4416 42741 8
## 3 2796 37285 18
## 4 1167 15214 13
## 5 3639 40171 9
## 6 6787 75378 5
## 7 2594 32587 11
## 8 3213 33429 14
## 9 3437 37102 18
## 10 2569 28419 18
## Number.of.Reviews Avg.Review.Length Ambience.Score Service.Quality.Score
## 1 185 161.92491 1.3 7.0
## 2 533 148.75972 2.6 3.4
## 3 853 56.84919 5.3 6.7
## 4 82 205.43327 4.6 2.8
## 5 78 241.68158 8.6 2.1
## 6 400 247.54446 2.0 8.5
## 7 106 206.15903 1.5 5.3
## 8 377 135.31931 1.8 4.6
## 9 494 55.65242 7.1 9.4
## 10 907 273.94489 6.4 7.4
## Parking.Availability Weekend.Reservations Weekday.Reservations Revenue
## 1 Yes 13 4 638945.5
## 2 Yes 48 6 490207.8
## 3 No 27 14 541368.6
## 4 Yes 9 17 404556.8
## 5 No 37 26 1491046.4
## 6 Yes 27 22 644220.9
## 7 No 12 28 400348.7
## 8 No 56 23 1014153.7
## 9 Yes 27 24 635606.1
## 10 No 12 19 452920.72.1 Data Inspection
head(Restaurant)## Name Location Cuisine Rating Seating.Capacity Average.Meal.Price
## 1 Restaurant 0 Rural Japanese 4.0 38 73.98
## 2 Restaurant 1 Downtown Mexican 3.2 76 28.11
## 3 Restaurant 2 Rural Italian 4.7 48 48.29
## 4 Restaurant 3 Rural Italian 4.4 34 51.55
## 5 Restaurant 4 Downtown Japanese 4.9 88 75.98
## 6 Restaurant 5 Downtown Indian 4.4 79 35.69
## Marketing.Budget Social.Media.Followers Chef.Experience.Years
## 1 2224 23406 13
## 2 4416 42741 8
## 3 2796 37285 18
## 4 1167 15214 13
## 5 3639 40171 9
## 6 6787 75378 5
## Number.of.Reviews Avg.Review.Length Ambience.Score Service.Quality.Score
## 1 185 161.92491 1.3 7.0
## 2 533 148.75972 2.6 3.4
## 3 853 56.84919 5.3 6.7
## 4 82 205.43327 4.6 2.8
## 5 78 241.68158 8.6 2.1
## 6 400 247.54446 2.0 8.5
## Parking.Availability Weekend.Reservations Weekday.Reservations Revenue
## 1 Yes 13 4 638945.5
## 2 Yes 48 6 490207.8
## 3 No 27 14 541368.6
## 4 Yes 9 17 404556.8
## 5 No 37 26 1491046.4
## 6 Yes 27 22 644220.9tail(Restaurant)## Name Location Cuisine Rating Seating.Capacity
## 8363 Restaurant 8362 Rural Indian 4.6 49
## 8364 Restaurant 8363 Suburban Indian 3.4 54
## 8365 Restaurant 8364 Rural Indian 3.7 49
## 8366 Restaurant 8365 Downtown Italian 4.7 88
## 8367 Restaurant 8366 Rural American 3.1 31
## 8368 Restaurant 8367 Rural Japanese 4.0 33
## Average.Meal.Price Marketing.Budget Social.Media.Followers
## 8363 35.12 2949 29425
## 8364 34.85 1102 11298
## 8365 36.88 1988 20432
## 8366 46.87 5949 63945
## 8367 44.53 707 7170
## 8368 71.07 2003 24268
## Chef.Experience.Years Number.of.Reviews Avg.Review.Length Ambience.Score
## 8363 1 713 220.1603 6.5
## 8364 11 380 253.9195 9.5
## 8365 9 713 175.5902 2.7
## 8366 6 436 222.9536 4.8
## 8367 1 729 178.4829 6.1
## 8368 8 197 151.8381 5.9
## Service.Quality.Score Parking.Availability Weekend.Reservations
## 8363 6.1 No 5
## 8364 5.0 Yes 37
## 8365 2.6 No 37
## 8366 1.7 Yes 83
## 8367 2.1 No 6
## 8368 7.5 Yes 5
## Weekday.Reservations Revenue
## 8363 31 392757.8
## 8364 0 434653.5
## 8365 21 414977.9
## 8366 21 930395.9
## 8367 21 311493.5
## 8368 12 534143.0dim(Restaurant)## [1] 8368 17names(Restaurant)## [1] "Name" "Location" "Cuisine"
## [4] "Rating" "Seating.Capacity" "Average.Meal.Price"
## [7] "Marketing.Budget" "Social.Media.Followers" "Chef.Experience.Years"
## [10] "Number.of.Reviews" "Avg.Review.Length" "Ambience.Score"
## [13] "Service.Quality.Score" "Parking.Availability" "Weekend.Reservations"
## [16] "Weekday.Reservations" "Revenue"Summary: 1.The data contains 17 column and 8368 rows of data 2.Columns -Name: The name of the restaurant. -Location: The location of the restaurant (e.g., Rural, Downtown). -Cuisine: The type of cuisine offered (e.g., Japanese, Mexican, Italian). -Rating: The average rating of the restaurant. -Seating Capacity: The number of seats available in the restaurant. -Average Meal Price: The average price of a meal at the restaurant. -Marketing Budget: The marketing budget allocated for the restaurant. -Social Media Followers: The number of social media followers. -Chef Experience Years: The number of years of experience of the head chef. -Number of Reviews: The total number of reviews the restaurant has received. -Avg Review Length: The average length of reviews. -Ambience Score: A score representing the ambience of the restaurant. -Service Quality Score: A score representing the quality of service. -Parking Availability: Indicates if parking is available (Yes/No). -Weekend Reservations: The number of reservations made on weekends. -Weekday Reservations: The number of reservations made on weekdays. -Revenue: The total revenue generated by the restaurant.
2.2 Data Cleansing & Coertions
str(Restaurant)## 'data.frame': 8368 obs. of 17 variables:
## $ Name : chr "Restaurant 0" "Restaurant 1" "Restaurant 2" "Restaurant 3" ...
## $ Location : chr "Rural" "Downtown" "Rural" "Rural" ...
## $ Cuisine : chr "Japanese" "Mexican" "Italian" "Italian" ...
## $ Rating : num 4 3.2 4.7 4.4 4.9 4.4 4.9 4.1 4.2 5 ...
## $ Seating.Capacity : int 38 76 48 34 88 79 49 61 37 32 ...
## $ Average.Meal.Price : num 74 28.1 48.3 51.5 76 ...
## $ Marketing.Budget : int 2224 4416 2796 1167 3639 6787 2594 3213 3437 2569 ...
## $ Social.Media.Followers: int 23406 42741 37285 15214 40171 75378 32587 33429 37102 28419 ...
## $ Chef.Experience.Years : int 13 8 18 13 9 5 11 14 18 18 ...
## $ Number.of.Reviews : int 185 533 853 82 78 400 106 377 494 907 ...
## $ Avg.Review.Length : num 161.9 148.8 56.8 205.4 241.7 ...
## $ Ambience.Score : num 1.3 2.6 5.3 4.6 8.6 2 1.5 1.8 7.1 6.4 ...
## $ Service.Quality.Score : num 7 3.4 6.7 2.8 2.1 8.5 5.3 4.6 9.4 7.4 ...
## $ Parking.Availability : chr "Yes" "Yes" "No" "Yes" ...
## $ Weekend.Reservations : int 13 48 27 9 37 27 12 56 27 12 ...
## $ Weekday.Reservations : int 4 6 14 17 26 22 28 23 24 19 ...
## $ Revenue : num 638946 490208 541369 404557 1491046 ...Columns that need to be change: -Location from chr -> fctr -Cuisine from chr -> fctr -Parking.Availability from chr -> fctr
Restaurant$Location <- as.factor(Restaurant$Location)
Restaurant$Cuisine <- as.factor(Restaurant$Cuisine)
Restaurant$Parking.Availability <- as.factor(Restaurant$Parking.Availability)
str(Restaurant)## 'data.frame': 8368 obs. of 17 variables:
## $ Name : chr "Restaurant 0" "Restaurant 1" "Restaurant 2" "Restaurant 3" ...
## $ Location : Factor w/ 3 levels "Downtown","Rural",..: 2 1 2 2 1 1 2 3 2 2 ...
## $ Cuisine : Factor w/ 6 levels "American","French",..: 5 6 4 4 5 3 6 5 5 2 ...
## $ Rating : num 4 3.2 4.7 4.4 4.9 4.4 4.9 4.1 4.2 5 ...
## $ Seating.Capacity : int 38 76 48 34 88 79 49 61 37 32 ...
## $ Average.Meal.Price : num 74 28.1 48.3 51.5 76 ...
## $ Marketing.Budget : int 2224 4416 2796 1167 3639 6787 2594 3213 3437 2569 ...
## $ Social.Media.Followers: int 23406 42741 37285 15214 40171 75378 32587 33429 37102 28419 ...
## $ Chef.Experience.Years : int 13 8 18 13 9 5 11 14 18 18 ...
## $ Number.of.Reviews : int 185 533 853 82 78 400 106 377 494 907 ...
## $ Avg.Review.Length : num 161.9 148.8 56.8 205.4 241.7 ...
## $ Ambience.Score : num 1.3 2.6 5.3 4.6 8.6 2 1.5 1.8 7.1 6.4 ...
## $ Service.Quality.Score : num 7 3.4 6.7 2.8 2.1 8.5 5.3 4.6 9.4 7.4 ...
## $ Parking.Availability : Factor w/ 2 levels "No","Yes": 2 2 1 2 1 2 1 1 2 1 ...
## $ Weekend.Reservations : int 13 48 27 9 37 27 12 56 27 12 ...
## $ Weekday.Reservations : int 4 6 14 17 26 22 28 23 24 19 ...
## $ Revenue : num 638946 490208 541369 404557 1491046 ...colSums(is.na(Restaurant))## Name Location Cuisine
## 0 0 0
## Rating Seating.Capacity Average.Meal.Price
## 0 0 0
## Marketing.Budget Social.Media.Followers Chef.Experience.Years
## 0 0 0
## Number.of.Reviews Avg.Review.Length Ambience.Score
## 0 0 0
## Service.Quality.Score Parking.Availability Weekend.Reservations
## 0 0 0
## Weekday.Reservations Revenue
## 0 0anyNA(Restaurant)## [1] FALSEInspection: -All value has been cleans to the desired data type -No missing value
Subset to Delete Unused Column
Rest_clean <- Restaurant[, -c(1,5,11)]
head(Rest_clean)## Location Cuisine Rating Average.Meal.Price Marketing.Budget
## 1 Rural Japanese 4.0 73.98 2224
## 2 Downtown Mexican 3.2 28.11 4416
## 3 Rural Italian 4.7 48.29 2796
## 4 Rural Italian 4.4 51.55 1167
## 5 Downtown Japanese 4.9 75.98 3639
## 6 Downtown Indian 4.4 35.69 6787
## Social.Media.Followers Chef.Experience.Years Number.of.Reviews Ambience.Score
## 1 23406 13 185 1.3
## 2 42741 8 533 2.6
## 3 37285 18 853 5.3
## 4 15214 13 82 4.6
## 5 40171 9 78 8.6
## 6 75378 5 400 2.0
## Service.Quality.Score Parking.Availability Weekend.Reservations
## 1 7.0 Yes 13
## 2 3.4 Yes 48
## 3 6.7 No 27
## 4 2.8 Yes 9
## 5 2.1 No 37
## 6 8.5 Yes 27
## Weekday.Reservations Revenue
## 1 4 638945.5
## 2 6 490207.8
## 3 14 541368.6
## 4 17 404556.8
## 5 26 1491046.4
## 6 22 644220.93. Data Explanation
summary(Rest_clean)## Location Cuisine Rating Average.Meal.Price
## Downtown:2821 American:1416 Min. :3.000 Min. :25.00
## Rural :2762 French :1433 1st Qu.:3.500 1st Qu.:35.49
## Suburban:2785 Indian :1369 Median :4.000 Median :45.53
## Italian :1413 Mean :4.008 Mean :47.90
## Japanese:1344 3rd Qu.:4.500 3rd Qu.:60.30
## Mexican :1393 Max. :5.000 Max. :76.00
## Marketing.Budget Social.Media.Followers Chef.Experience.Years
## Min. : 604 Min. : 5277 Min. : 1.00
## 1st Qu.:1889 1st Qu.: 22593 1st Qu.: 5.00
## Median :2846 Median : 32519 Median :10.00
## Mean :3218 Mean : 36191 Mean :10.05
## 3rd Qu.:4008 3rd Qu.: 44566 3rd Qu.:15.00
## Max. :9978 Max. :103777 Max. :19.00
## Number.of.Reviews Ambience.Score Service.Quality.Score Parking.Availability
## Min. : 50.0 Min. : 1.000 Min. : 1.000 No :4179
## 1st Qu.:277.0 1st Qu.: 3.300 1st Qu.: 3.200 Yes:4189
## Median :528.0 Median : 5.500 Median : 5.600
## Mean :523.0 Mean : 5.521 Mean : 5.509
## 3rd Qu.:764.2 3rd Qu.: 7.800 3rd Qu.: 7.800
## Max. :999.0 Max. :10.000 Max. :10.000
## Weekend.Reservations Weekday.Reservations Revenue
## Min. : 0.00 Min. : 0.00 Min. : 184709
## 1st Qu.:13.00 1st Qu.:13.00 1st Qu.: 454651
## Median :27.00 Median :26.00 Median : 604242
## Mean :29.49 Mean :29.24 Mean : 656071
## 3rd Qu.:43.00 3rd Qu.:43.00 3rd Qu.: 813094
## Max. :88.00 Max. :88.00 Max. :1531868Summay: 1. Most restaurants are located at “Downtown”, but are fairly evenly distributed. 2. Most restaurants do “French” Cuisine. 3. Lowest rating a restaurant ever got was 3 star and highest was 5 star. 4. Cost of a typical meal, ranging from $25 to $76. 5. The annual marketing budget range from $604 to $9,978.
##3.1 Check the Outlier within Revenue
aggregate(Revenue ~ Cuisine,Rest_clean,mean)## Cuisine Revenue
## 1 American 564942.0
## 2 French 820204.1
## 3 Indian 496615.7
## 4 Italian 692742.4
## 5 Japanese 937969.0
## 6 Mexican 427383.9aggregate(Revenue ~ Cuisine,Rest_clean,var)## Cuisine Revenue
## 1 American 25851302894
## 2 French 58463265410
## 3 Indian 20892843909
## 4 Italian 39809628374
## 5 Japanese 78014788272
## 6 Mexican 15613308732aggregate(Revenue ~ Cuisine,Rest_clean,sd)## Cuisine Revenue
## 1 American 160783.4
## 2 French 241791.8
## 3 Indian 144543.6
## 4 Italian 199523.5
## 5 Japanese 279311.3
## 6 Mexican 124953.2graphics::boxplot(Rest_clean$Revenue)
From result above, we find posibilities for the outliers, but from our
calculation, Sd value is around 191200 so the process may continue
4. Data Manipulation
1. Which Restaurant produces the least revenue ?
Low_Rev <- Rest_clean[Rest_clean$Revenue == min(Rest_clean$Revenue), ]
Low_Rev## Location Cuisine Rating Average.Meal.Price Marketing.Budget
## 5736 Rural Mexican 3.7 25.38 1350
## Social.Media.Followers Chef.Experience.Years Number.of.Reviews
## 5736 12738 9 157
## Ambience.Score Service.Quality.Score Parking.Availability
## 5736 7.1 3.4 Yes
## Weekend.Reservations Weekday.Reservations Revenue
## 5736 25 4 184708.5Answer = Revenue of $184,708.5 comes from location “Rural”, cooks Mexican, and have a low ratings of 3.7 stars
2. Which Restaurant produces the most revenue
high_rev <- Rest_clean[Rest_clean$Revenue == max(Rest_clean$Revenue), ]
high_rev## Location Cuisine Rating Average.Meal.Price Marketing.Budget
## 4325 Downtown Japanese 4.1 75.83 3115
## Social.Media.Followers Chef.Experience.Years Number.of.Reviews
## 4325 34238 17 535
## Ambience.Score Service.Quality.Score Parking.Availability
## 4325 5.5 5.7 No
## Weekend.Reservations Weekday.Reservations Revenue
## 4325 33 62 1531868Answer = It seems a Japanese Restaurant located at Downtown, with a rating of 4.1 stars, an average meal price of $75.83 produces a Revenue of $1,531,868.
3. Which Cuisine got most of the lowest rating ?
Low_rate <- Rest_clean[Rest_clean$Rating == min(Rest_clean$Rating), ]
Low_rate## Location Cuisine Rating Average.Meal.Price Marketing.Budget
## 41 Downtown Indian 3 39.25 2397
## 89 Suburban American 3 38.89 2911
## 116 Suburban Italian 3 45.60 1063
## 128 Downtown Mexican 3 29.39 5425
## 137 Suburban American 3 38.09 654
## 184 Downtown Japanese 3 66.90 3371
## 223 Suburban Japanese 3 73.05 2438
## 261 Downtown French 3 61.23 4751
## 281 Rural French 3 62.41 921
## 285 Downtown American 3 38.91 1654
## 354 Downtown Italian 3 54.47 2347
## 399 Rural Indian 3 39.08 1152
## 481 Downtown Italian 3 47.14 5722
## 502 Suburban Mexican 3 29.06 2174
## 579 Downtown Indian 3 31.46 4629
## 601 Downtown Italian 3 54.80 5609
## 602 Rural Japanese 3 73.47 2683
## 603 Downtown French 3 57.74 2390
## 624 Downtown American 3 35.09 2423
## 673 Downtown Italian 3 54.74 1456
## 694 Rural Mexican 3 29.61 1517
## 700 Suburban Japanese 3 65.08 849
## 716 Suburban French 3 65.92 1571
## 742 Suburban Italian 3 50.69 893
## 759 Rural Japanese 3 75.68 1734
## 760 Rural Japanese 3 71.42 1427
## 814 Rural Japanese 3 71.20 2296
## 828 Suburban French 3 65.93 992
## 911 Rural Italian 3 47.58 2993
## 980 Rural Italian 3 45.00 756
## 989 Downtown Indian 3 32.61 1209
## 1038 Suburban Indian 3 40.17 1752
## 1122 Suburban French 3 64.51 2686
## 1151 Suburban French 3 56.75 1960
## 1278 Rural French 3 59.66 1201
## 1285 Downtown American 3 44.98 4633
## 1334 Suburban Mexican 3 35.57 969
## 1357 Suburban American 3 43.98 875
## 1359 Rural Japanese 3 75.75 2570
## 1360 Downtown French 3 55.77 5945
## 1490 Rural American 3 38.50 845
## 1647 Rural Indian 3 33.95 2254
## 1660 Suburban Italian 3 46.57 793
## 1732 Rural American 3 44.01 1962
## 1775 Suburban Italian 3 47.28 1765
## 1793 Suburban French 3 57.44 1636
## 1820 Suburban American 3 41.53 1297
## 1822 Rural French 3 57.18 1408
## 1834 Downtown American 3 39.34 5018
## 1837 Rural Italian 3 46.86 1931
## 1858 Downtown French 3 55.82 3363
## 1918 Downtown Indian 3 31.02 5703
## 1958 Rural Indian 3 40.23 1093
## 1970 Downtown Italian 3 46.93 3506
## 1985 Rural American 3 38.29 1967
## 2033 Downtown American 3 42.69 2482
## 2066 Suburban Italian 3 51.96 2950
## 2099 Rural French 3 63.03 1240
## 2126 Suburban Italian 3 48.02 2605
## 2187 Suburban Japanese 3 72.98 2642
## 2205 Suburban Mexican 3 32.37 2678
## 2232 Downtown American 3 35.12 3486
## 2289 Rural Italian 3 46.83 1757
## 2314 Suburban American 3 36.01 2122
## 2418 Downtown American 3 38.73 4091
## 2421 Downtown Mexican 3 25.05 2262
## 2437 Suburban Japanese 3 65.46 2867
## 2451 Suburban French 3 59.00 798
## 2485 Suburban Mexican 3 34.60 1557
## 2593 Downtown Mexican 3 32.17 3926
## 2614 Downtown Mexican 3 31.32 1396
## 2634 Suburban Italian 3 54.40 2345
## 2644 Downtown Japanese 3 67.60 2566
## 2720 Rural Mexican 3 28.45 2522
## 2811 Rural Japanese 3 74.32 1928
## 2819 Rural French 3 65.12 1018
## 2850 Rural Indian 3 34.21 1261
## 2860 Suburban American 3 43.43 2294
## 2882 Downtown French 3 58.78 4748
## 2921 Downtown Mexican 3 33.24 1459
## 2992 Downtown American 3 42.70 2620
## 3042 Downtown Italian 3 45.81 2563
## 3060 Downtown Japanese 3 72.99 4171
## 3061 Rural French 3 57.33 1954
## 3124 Rural Italian 3 46.78 1063
## 3130 Rural French 3 63.62 2348
## 3154 Downtown Italian 3 49.24 5280
## 3223 Downtown Mexican 3 33.82 3334
## 3265 Downtown French 3 56.75 4726
## 3315 Downtown Japanese 3 71.71 3958
## 3514 Downtown Japanese 3 65.58 2593
## 3534 Downtown Italian 3 50.34 5970
## 3568 Rural Japanese 3 72.73 988
## 3595 Suburban Japanese 3 66.26 2531
## 3610 Rural Italian 3 55.92 1246
## 3651 Downtown American 3 44.20 3870
## 3654 Downtown Italian 3 51.33 2402
## 3709 Suburban Italian 3 50.81 789
## 3731 Suburban Japanese 3 71.86 2059
## 3874 Suburban Indian 3 30.82 1696
## 3893 Suburban Mexican 3 27.56 1503
## 3946 Rural French 3 63.47 2878
## 4017 Suburban French 3 56.00 2861
## 4057 Downtown Indian 3 38.51 5225
## 4072 Rural Italian 3 50.63 1251
## 4103 Downtown American 3 44.20 4568
## 4146 Suburban Mexican 3 27.15 2153
## 4149 Suburban Indian 3 31.91 1195
## 4176 Suburban Indian 3 38.29 1667
## 4183 Rural Indian 3 32.13 685
## 4198 Downtown Mexican 3 34.04 1574
## 4201 Rural Indian 3 40.35 1148
## 4215 Suburban French 3 59.39 1426
## 4272 Suburban Japanese 3 74.55 604
## 4290 Rural Japanese 3 66.52 920
## 4353 Suburban French 3 55.08 1936
## 4381 Suburban Japanese 3 72.25 1859
## 4514 Suburban French 3 60.20 1352
## 4515 Downtown Japanese 3 71.16 3791
## 4547 Suburban French 3 65.41 1509
## 4549 Downtown Italian 3 45.02 5712
## 4592 Downtown American 3 39.87 2955
## 4691 Suburban Italian 3 45.99 2570
## 4710 Downtown Japanese 3 73.63 1568
## 4757 Suburban American 3 36.05 699
## 4804 Suburban Indian 3 35.07 818
## 4828 Rural Japanese 3 69.90 2471
## 4833 Suburban Japanese 3 70.18 1506
## 4834 Suburban French 3 65.27 1552
## 4892 Suburban American 3 35.74 2031
## 4936 Suburban Indian 3 32.31 1366
## 5091 Suburban American 3 45.58 1716
## 5122 Downtown Japanese 3 71.80 3661
## 5160 Rural Indian 3 34.66 1176
## 5173 Downtown Mexican 3 26.96 4714
## 5180 Suburban Indian 3 37.24 2559
## 5185 Rural French 3 55.20 2568
## 5197 Downtown Indian 3 34.13 5695
## 5222 Suburban Indian 3 36.63 1823
## 5285 Suburban Indian 3 30.52 1723
## 5393 Suburban Indian 3 38.54 2441
## 5484 Rural French 3 58.25 1999
## 5490 Suburban French 3 55.44 1624
## 5492 Downtown Indian 3 39.92 2215
## 5648 Rural French 3 59.98 2152
## 5684 Rural French 3 62.00 2615
## 5705 Rural Japanese 3 75.62 1801
## 5725 Suburban Japanese 3 70.26 1298
## 5818 Downtown Mexican 3 35.88 4370
## 5856 Rural Mexican 3 33.62 1954
## 6005 Downtown American 3 36.68 5003
## 6007 Rural French 3 56.04 2263
## 6021 Suburban Indian 3 36.49 2033
## 6079 Rural French 3 60.62 1091
## 6089 Rural French 3 55.28 928
## 6141 Downtown Japanese 3 71.99 1701
## 6154 Suburban American 3 40.16 2302
## 6169 Suburban French 3 58.75 2237
## 6172 Downtown Indian 3 33.07 3641
## 6205 Downtown Italian 3 49.24 4888
## 6258 Rural Italian 3 52.43 2992
## 6300 Rural Indian 3 36.26 810
## 6308 Downtown Mexican 3 26.22 1741
## 6324 Rural Italian 3 47.68 2665
## 6437 Rural Indian 3 37.60 1573
## 6469 Suburban Mexican 3 27.50 2295
## 6503 Downtown Japanese 3 67.66 3695
## 6540 Rural Italian 3 49.47 2491
## 6574 Suburban Indian 3 35.72 1895
## 6599 Suburban American 3 45.69 2240
## 6610 Downtown Indian 3 38.49 5041
## 6616 Suburban French 3 63.41 2969
## 6665 Rural American 3 41.28 852
## 6674 Rural American 3 40.74 989
## 6736 Downtown Indian 3 40.46 5563
## 6744 Suburban Japanese 3 71.28 1000
## 6794 Suburban Japanese 3 69.50 1538
## 6825 Rural Italian 3 55.47 2146
## 6932 Downtown American 3 40.27 5913
## 6990 Rural French 3 63.51 865
## 7075 Rural Indian 3 36.09 1308
## 7193 Suburban French 3 63.60 2008
## 7220 Downtown Italian 3 46.36 3074
## 7232 Rural Japanese 3 70.95 2408
## 7265 Rural Mexican 3 28.83 979
## 7268 Suburban Mexican 3 31.53 1470
## 7430 Suburban American 3 39.15 785
## 7550 Rural French 3 62.61 1319
## 7557 Suburban French 3 63.78 1994
## 7579 Downtown American 3 43.41 2852
## 7593 Rural American 3 41.22 2813
## 7703 Rural Mexican 3 26.10 883
## 7713 Suburban French 3 63.49 2457
## 7737 Downtown Indian 3 39.84 2632
## 7799 Suburban French 3 58.19 1006
## 7812 Rural Indian 3 35.39 998
## 7824 Suburban Mexican 3 25.21 1187
## 7847 Downtown Mexican 3 31.23 3919
## 7849 Downtown Japanese 3 70.94 4294
## 7862 Suburban Indian 3 35.51 767
## 7884 Rural Indian 3 32.26 1341
## 7999 Rural Indian 3 35.74 2143
## 8033 Rural French 3 63.99 2545
## 8074 Rural French 3 65.66 2445
## 8149 Suburban French 3 64.87 1898
## 8159 Suburban French 3 59.73 2374
## 8214 Rural American 3 40.37 2458
## 8259 Suburban American 3 36.88 1391
## 8290 Suburban Mexican 3 27.70 1204
## 8338 Downtown Italian 3 45.87 3035
## 8350 Suburban Japanese 3 72.06 2081
## Social.Media.Followers Chef.Experience.Years Number.of.Reviews
## 41 29088 19 209
## 89 36596 14 95
## 116 14037 1 618
## 128 52699 3 746
## 137 7708 16 374
## 184 38753 5 243
## 223 26300 17 158
## 261 50603 14 832
## 281 10700 19 528
## 285 19454 10 534
## 354 26510 8 421
## 399 19508 6 205
## 481 61279 8 530
## 502 27011 15 433
## 579 44442 4 475
## 601 63936 13 732
## 602 31908 17 784
## 603 29443 8 216
## 624 31081 18 874
## 673 16803 5 107
## 694 14737 3 802
## 700 12714 8 834
## 716 18540 19 78
## 742 9744 2 705
## 759 23523 15 278
## 760 16793 11 201
## 814 23826 15 516
## 828 15513 15 359
## 911 36365 6 779
## 980 14918 19 192
## 989 18719 17 919
## 1038 19150 4 161
## 1122 32710 11 906
## 1151 26960 5 176
## 1278 12935 4 421
## 1285 44687 10 734
## 1334 15390 6 401
## 1357 14088 13 807
## 1359 28835 1 240
## 1360 64900 5 964
## 1490 6773 3 780
## 1647 30081 17 762
## 1660 11451 10 592
## 1732 24902 17 450
## 1775 19485 16 172
## 1793 15042 3 998
## 1820 13906 7 160
## 1822 18986 19 531
## 1834 54299 4 824
## 1837 25722 17 194
## 1858 33748 8 266
## 1918 63240 9 778
## 1958 15084 17 659
## 1970 38400 12 620
## 1985 25328 6 66
## 2033 28850 16 725
## 2066 33716 3 971
## 2099 15577 8 848
## 2126 27153 3 407
## 2187 25095 9 688
## 2205 33083 7 528
## 2232 38534 2 786
## 2289 21878 8 886
## 2314 21554 1 951
## 2418 39378 19 938
## 2421 27849 5 121
## 2437 33685 15 65
## 2451 10040 2 805
## 2485 18189 10 861
## 2593 41103 13 771
## 2614 17777 4 259
## 2634 26562 15 154
## 2644 27510 14 661
## 2720 25328 17 332
## 2811 24758 6 829
## 2819 14417 1 53
## 2850 20073 7 499
## 2860 21714 2 396
## 2882 52685 1 785
## 2921 18793 1 53
## 2992 33652 18 109
## 3042 31007 15 971
## 3060 41366 7 205
## 3061 18671 11 309
## 3124 8731 9 972
## 3130 23299 6 227
## 3154 59321 5 142
## 3223 40945 1 143
## 3265 51470 10 327
## 3315 37653 7 528
## 3514 27973 8 773
## 3534 61323 7 137
## 3568 16115 11 264
## 3595 30909 10 200
## 3610 12193 13 608
## 3651 44939 17 417
## 3654 30674 4 198
## 3709 7351 3 215
## 3731 27646 7 470
## 3874 17852 8 574
## 3893 19930 4 818
## 3946 27696 1 497
## 4017 26902 10 305
## 4057 51592 3 487
## 4072 12857 5 459
## 4103 48079 17 815
## 4146 21940 11 526
## 4149 11995 6 537
## 4176 19636 2 513
## 4183 9749 5 341
## 4198 16198 3 430
## 4201 18102 12 219
## 4215 21037 17 518
## 4272 10247 2 575
## 4290 7229 17 690
## 4353 25714 12 923
## 4381 21738 4 563
## 4514 21224 16 138
## 4515 38193 5 583
## 4547 16906 17 641
## 4549 55688 13 74
## 4592 28704 18 667
## 4691 26504 16 249
## 4710 22772 11 126
## 4757 8219 7 461
## 4804 12232 8 173
## 4828 31587 2 588
## 4833 15223 6 248
## 4834 16358 5 263
## 4892 19574 4 370
## 4936 13876 12 342
## 5091 22033 3 478
## 5122 40014 6 614
## 5160 19056 18 306
## 5173 47734 14 825
## 5180 31804 19 180
## 5185 23774 12 974
## 5197 59153 19 734
## 5222 23183 17 997
## 5285 23247 12 829
## 5393 26376 8 947
## 5484 22293 17 415
## 5490 19376 7 389
## 5492 20889 11 900
## 5648 26943 12 687
## 5684 32537 14 414
## 5705 17515 3 407
## 5725 16257 3 409
## 5818 45378 18 154
## 5856 25346 6 902
## 6005 54967 19 181
## 6007 22804 17 795
## 6021 22121 17 916
## 6079 16270 12 531
## 6089 7794 15 258
## 6141 18211 8 694
## 6154 21933 11 462
## 6169 25592 15 62
## 6172 38846 13 890
## 6205 48443 6 252
## 6258 35554 4 642
## 6300 14897 6 902
## 6308 16778 2 509
## 6324 33011 6 787
## 6437 23336 19 943
## 6469 26875 17 579
## 6503 36098 9 462
## 6540 32504 11 201
## 6574 18492 5 498
## 6599 28829 7 204
## 6610 51344 11 784
## 6616 30441 1 452
## 6665 8297 19 432
## 6674 8782 18 489
## 6736 55775 10 440
## 6744 15621 1 632
## 6794 22503 3 114
## 6825 25159 7 866
## 6932 60679 15 756
## 6990 15808 7 454
## 7075 19678 12 76
## 7193 19524 11 605
## 7220 35983 15 97
## 7232 22729 13 475
## 7265 10325 14 122
## 7268 13349 15 590
## 7430 13755 2 135
## 7550 18791 5 902
## 7557 25646 6 234
## 7579 36320 10 109
## 7593 34157 17 181
## 7703 7835 15 477
## 7713 23858 6 488
## 7737 33016 14 78
## 7799 16962 9 86
## 7812 13519 12 256
## 7824 18823 8 265
## 7847 44745 9 668
## 7849 49553 11 742
## 7862 9641 8 715
## 7884 15053 3 702
## 7999 23362 1 169
## 8033 24946 16 779
## 8074 23108 9 362
## 8149 26906 18 122
## 8159 22115 17 990
## 8214 28431 2 51
## 8259 18983 4 505
## 8290 17192 1 695
## 8338 29595 12 438
## 8350 25365 6 812
## Ambience.Score Service.Quality.Score Parking.Availability
## 41 4.5 4.5 No
## 89 7.4 5.1 Yes
## 116 7.4 6.8 Yes
## 128 8.3 6.3 No
## 137 7.9 2.8 Yes
## 184 8.0 2.5 Yes
## 223 5.3 1.5 No
## 261 7.8 1.3 Yes
## 281 4.1 3.1 Yes
## 285 1.4 2.3 Yes
## 354 2.2 5.5 No
## 399 9.7 7.9 Yes
## 481 7.1 2.7 No
## 502 5.7 7.6 Yes
## 579 6.5 6.0 No
## 601 8.0 5.9 Yes
## 602 1.6 2.4 No
## 603 6.1 1.7 Yes
## 624 9.6 5.0 Yes
## 673 1.2 4.9 No
## 694 7.4 6.5 No
## 700 8.9 7.3 Yes
## 716 1.4 1.4 No
## 742 4.6 6.9 No
## 759 8.4 8.0 Yes
## 760 5.1 6.2 Yes
## 814 7.5 3.1 Yes
## 828 6.6 3.0 Yes
## 911 4.3 2.3 No
## 980 7.4 8.4 Yes
## 989 4.1 6.9 Yes
## 1038 3.6 8.6 No
## 1122 9.2 6.2 No
## 1151 2.7 2.2 No
## 1278 8.8 6.4 Yes
## 1285 3.7 9.2 Yes
## 1334 8.6 7.1 No
## 1357 8.9 10.0 Yes
## 1359 4.1 7.6 No
## 1360 5.2 4.3 No
## 1490 4.2 3.4 Yes
## 1647 5.4 7.7 Yes
## 1660 4.2 8.6 Yes
## 1732 1.4 3.3 No
## 1775 8.8 9.0 No
## 1793 1.3 4.9 No
## 1820 9.7 8.7 No
## 1822 6.5 1.6 Yes
## 1834 4.3 9.1 No
## 1837 5.5 4.9 Yes
## 1858 8.9 4.7 No
## 1918 7.7 1.9 No
## 1958 9.2 4.2 No
## 1970 6.3 8.7 Yes
## 1985 4.1 5.9 Yes
## 2033 2.5 1.2 Yes
## 2066 8.2 2.5 No
## 2099 3.4 3.8 No
## 2126 5.0 2.0 No
## 2187 9.7 4.0 Yes
## 2205 8.1 1.7 Yes
## 2232 6.5 4.0 No
## 2289 9.3 3.4 Yes
## 2314 8.3 7.8 Yes
## 2418 7.3 9.6 No
## 2421 5.8 1.4 No
## 2437 1.0 7.3 No
## 2451 3.3 7.2 Yes
## 2485 2.7 6.1 No
## 2593 4.2 6.0 Yes
## 2614 8.5 3.9 Yes
## 2634 8.5 8.8 No
## 2644 9.5 8.5 No
## 2720 6.5 5.3 No
## 2811 2.6 4.9 No
## 2819 1.8 5.5 No
## 2850 9.7 7.8 No
## 2860 8.5 7.4 No
## 2882 7.6 5.0 Yes
## 2921 7.9 5.6 Yes
## 2992 2.7 1.6 Yes
## 3042 1.6 9.7 Yes
## 3060 8.7 5.6 Yes
## 3061 9.7 8.3 Yes
## 3124 9.5 8.8 No
## 3130 8.2 2.5 No
## 3154 9.7 9.5 No
## 3223 7.9 9.7 Yes
## 3265 6.1 3.7 Yes
## 3315 7.6 2.9 Yes
## 3514 1.9 5.0 No
## 3534 9.9 2.5 Yes
## 3568 2.4 9.7 No
## 3595 2.3 6.2 Yes
## 3610 8.7 9.9 No
## 3651 8.7 8.0 Yes
## 3654 1.1 7.8 Yes
## 3709 9.5 8.6 No
## 3731 5.6 4.4 Yes
## 3874 5.2 9.0 No
## 3893 2.9 6.3 No
## 3946 5.0 7.2 No
## 4017 2.2 7.6 No
## 4057 1.8 4.1 No
## 4072 6.1 5.1 No
## 4103 2.1 5.0 Yes
## 4146 5.4 4.3 No
## 4149 2.9 6.3 Yes
## 4176 8.8 1.5 No
## 4183 8.4 6.7 No
## 4198 8.8 2.5 Yes
## 4201 6.5 4.7 Yes
## 4215 1.7 1.5 Yes
## 4272 4.7 5.2 Yes
## 4290 8.0 4.9 No
## 4353 3.9 3.8 No
## 4381 4.8 7.2 No
## 4514 8.8 4.6 Yes
## 4515 4.7 2.8 Yes
## 4547 3.9 8.2 Yes
## 4549 6.9 2.2 No
## 4592 1.5 5.9 Yes
## 4691 3.6 8.7 Yes
## 4710 4.2 7.9 No
## 4757 5.5 3.6 No
## 4804 4.8 3.5 Yes
## 4828 3.2 3.7 No
## 4833 9.9 5.7 No
## 4834 8.5 7.4 No
## 4892 1.4 8.0 Yes
## 4936 4.9 7.6 Yes
## 5091 8.7 8.0 No
## 5122 3.8 1.2 No
## 5160 9.8 3.0 No
## 5173 9.9 7.6 Yes
## 5180 4.7 6.1 Yes
## 5185 6.6 5.2 Yes
## 5197 6.5 9.2 Yes
## 5222 8.6 7.0 No
## 5285 3.5 6.1 Yes
## 5393 3.4 2.5 Yes
## 5484 8.0 6.7 No
## 5490 5.4 3.6 Yes
## 5492 2.4 8.5 Yes
## 5648 2.8 2.2 Yes
## 5684 1.8 3.8 No
## 5705 2.0 4.4 No
## 5725 2.9 8.4 Yes
## 5818 5.9 1.7 No
## 5856 6.4 9.9 Yes
## 6005 2.0 3.2 No
## 6007 1.6 2.9 Yes
## 6021 8.9 9.0 No
## 6079 4.6 9.4 Yes
## 6089 7.6 6.2 Yes
## 6141 9.5 1.6 Yes
## 6154 2.1 7.7 No
## 6169 8.9 6.9 Yes
## 6172 2.6 8.8 Yes
## 6205 3.5 6.9 No
## 6258 9.0 2.5 No
## 6300 1.8 2.2 Yes
## 6308 5.5 4.0 Yes
## 6324 3.7 8.6 No
## 6437 4.9 3.2 No
## 6469 6.2 8.4 Yes
## 6503 3.4 1.9 No
## 6540 9.9 2.0 No
## 6574 2.9 4.2 Yes
## 6599 6.1 6.4 No
## 6610 7.0 9.4 No
## 6616 5.7 1.7 No
## 6665 8.7 3.1 Yes
## 6674 6.3 6.0 No
## 6736 1.3 1.1 Yes
## 6744 5.5 6.5 Yes
## 6794 3.9 6.7 No
## 6825 1.0 2.4 Yes
## 6932 3.4 2.7 No
## 6990 3.7 1.2 No
## 7075 3.1 4.7 Yes
## 7193 1.4 7.2 No
## 7220 8.6 3.8 No
## 7232 7.5 9.3 Yes
## 7265 2.3 6.0 Yes
## 7268 2.1 4.4 Yes
## 7430 2.5 7.1 No
## 7550 3.7 8.5 Yes
## 7557 8.4 3.0 No
## 7579 9.0 4.5 No
## 7593 7.2 5.7 Yes
## 7703 7.6 5.6 No
## 7713 6.3 5.5 Yes
## 7737 3.7 7.3 Yes
## 7799 6.1 6.9 No
## 7812 7.9 2.6 No
## 7824 2.8 6.1 Yes
## 7847 4.4 1.5 Yes
## 7849 9.7 7.6 No
## 7862 1.8 1.8 No
## 7884 4.0 4.7 No
## 7999 5.2 6.6 No
## 8033 8.1 9.8 Yes
## 8074 9.3 7.0 No
## 8149 3.5 8.5 Yes
## 8159 3.6 7.8 No
## 8214 2.1 9.6 No
## 8259 2.0 4.6 Yes
## 8290 2.3 3.7 No
## 8338 2.5 4.6 Yes
## 8350 5.5 8.8 No
## Weekend.Reservations Weekday.Reservations Revenue
## 41 4 60 667565.4
## 89 43 46 464459.9
## 116 54 57 605891.6
## 128 40 25 603751.7
## 137 37 58 527585.6
## 184 33 0 1118770.4
## 223 53 44 1040644.7
## 261 1 47 971465.6
## 281 44 17 644430.1
## 285 22 68 752704.5
## 354 26 40 880016.0
## 399 16 27 379051.7
## 481 21 41 844098.8
## 502 18 24 352919.2
## 579 16 20 607126.1
## 601 19 12 874807.1
## 602 1 25 832594.6
## 603 38 46 1109714.5
## 624 57 69 674488.8
## 673 56 46 892000.0
## 694 42 35 308652.9
## 700 56 14 993404.2
## 716 33 9 936106.4
## 742 11 28 579120.2
## 759 17 25 660520.7
## 760 30 1 710449.3
## 814 9 25 776842.5
## 828 17 19 934791.3
## 911 9 10 445290.6
## 980 1 13 404283.9
## 989 10 20 642242.5
## 1038 1 1 603502.2
## 1122 39 4 947250.3
## 1151 0 33 635375.2
## 1278 33 22 540793.2
## 1285 70 2 887344.3
## 1334 37 42 471495.2
## 1357 35 30 510520.6
## 1359 4 7 577494.5
## 1360 37 26 980017.2
## 1490 29 25 290284.7
## 1647 7 26 276436.0
## 1660 13 20 622613.8
## 1732 13 18 353500.9
## 1775 53 10 684166.6
## 1793 53 56 810062.7
## 1820 5 33 511872.7
## 1822 15 28 467916.7
## 1834 48 20 792041.0
## 1837 1 41 511068.7
## 1858 61 51 981833.0
## 1918 59 36 539522.9
## 1958 40 14 390809.3
## 1970 12 39 760027.0
## 1985 32 32 395147.4
## 2033 13 71 841424.0
## 2066 44 26 725552.9
## 2099 0 43 639805.8
## 2126 62 51 712086.5
## 2187 40 16 953522.6
## 2205 43 33 432251.0
## 2232 85 31 681501.7
## 2289 40 17 473774.6
## 2314 16 29 434801.2
## 2418 1 46 734765.1
## 2421 3 40 491982.2
## 2437 39 43 876441.1
## 2451 5 36 659028.0
## 2485 21 47 514250.8
## 2593 34 31 621476.5
## 2614 43 55 517483.0
## 2634 26 55 736112.4
## 2644 58 41 1161373.3
## 2720 22 11 228929.9
## 2811 44 22 785418.8
## 2819 15 8 479123.8
## 2850 14 13 297770.5
## 2860 20 36 493259.3
## 2882 21 60 937357.7
## 2921 27 32 668105.7
## 2992 23 60 702818.3
## 3042 38 2 732086.6
## 3060 24 61 1195980.1
## 3061 21 35 605235.4
## 3124 26 32 424386.3
## 3130 26 29 660897.9
## 3154 32 38 837983.8
## 3223 23 26 574284.9
## 3265 18 26 934975.8
## 3315 21 81 1394730.7
## 3514 0 27 1197740.4
## 3534 36 51 989002.8
## 3568 13 28 755529.7
## 3595 21 10 907213.3
## 3610 20 6 443828.9
## 3651 71 8 831471.3
## 3654 74 74 1010811.3
## 3709 24 51 607593.5
## 3731 33 35 1092436.4
## 3874 21 21 378706.8
## 3893 23 41 359282.3
## 3946 31 23 489820.8
## 4017 23 11 636250.6
## 4057 48 67 612128.1
## 4072 19 27 515325.0
## 4103 2 46 836980.9
## 4146 38 21 415256.5
## 4149 34 29 386456.0
## 4176 10 49 567361.1
## 4183 28 3 360530.0
## 4198 38 21 596160.5
## 4201 34 2 375583.7
## 4215 57 35 795668.9
## 4272 63 43 1094966.4
## 4290 1 11 493541.8
## 4353 53 13 773397.2
## 4381 2 47 1062843.1
## 4514 19 38 925995.0
## 4515 34 65 1333616.4
## 4547 17 21 889413.8
## 4549 12 8 798982.4
## 4592 22 29 659605.2
## 4691 9 14 572763.7
## 4710 49 49 1398630.8
## 4757 52 50 491305.5
## 4804 20 32 492338.7
## 4828 18 7 641291.4
## 4833 35 50 822491.3
## 4834 64 48 995933.0
## 4892 10 43 406712.4
## 4936 26 31 391464.8
## 5091 11 1 605467.8
## 5122 75 10 1265535.0
## 5160 29 0 355127.2
## 5173 16 58 514096.2
## 5180 48 15 538179.5
## 5185 32 24 559887.1
## 5197 80 72 695284.8
## 5222 26 0 464826.4
## 5285 30 28 384904.5
## 5393 52 52 502927.3
## 5484 17 13 477526.9
## 5490 20 59 845675.0
## 5492 31 72 673672.2
## 5648 34 16 496330.5
## 5684 30 32 529407.3
## 5705 13 41 759909.8
## 5725 1 21 1029311.6
## 5818 52 14 606913.5
## 5856 26 29 287061.5
## 6005 37 34 613189.4
## 6007 7 13 492860.4
## 6021 45 35 466387.5
## 6079 8 9 421815.9
## 6089 43 14 572133.1
## 6141 62 51 1272033.9
## 6154 63 40 614777.8
## 6169 31 4 904620.3
## 6172 67 4 558921.9
## 6205 19 67 845243.3
## 6258 14 19 558691.9
## 6300 38 30 371571.8
## 6308 70 19 501637.3
## 6324 17 27 397379.1
## 6437 42 27 434542.3
## 6469 41 54 412274.2
## 6503 73 43 1316895.9
## 6540 6 8 359258.1
## 6574 53 51 486388.8
## 6599 25 60 702758.6
## 6610 81 69 753161.6
## 6616 35 36 918723.0
## 6665 45 29 446366.3
## 6674 11 9 295527.2
## 6736 5 53 752140.6
## 6744 20 46 873817.1
## 6794 12 38 837342.2
## 6825 27 14 381621.1
## 6932 42 65 794219.5
## 6990 14 13 584417.0
## 7075 32 11 352505.6
## 7193 41 11 817724.2
## 7220 76 72 917962.5
## 7232 25 38 746318.6
## 7265 15 24 229857.5
## 7268 22 23 465645.8
## 7430 28 53 543685.1
## 7550 16 30 567165.7
## 7557 42 16 746467.2
## 7579 4 7 784082.7
## 7593 30 3 319969.4
## 7703 26 12 311057.0
## 7713 8 3 727572.6
## 7737 15 66 778828.4
## 7799 20 29 861958.7
## 7812 16 31 362616.3
## 7824 9 47 392540.7
## 7847 59 12 509214.3
## 7849 24 3 1368052.0
## 7862 20 10 558296.2
## 7884 12 21 281050.5
## 7999 15 9 356869.0
## 8033 21 1 509450.1
## 8074 19 15 470695.1
## 8149 44 39 829386.2
## 8159 10 35 829967.7
## 8214 30 11 297628.7
## 8259 24 7 480938.7
## 8290 45 64 405384.4
## 8338 22 50 879630.9
## 8350 16 32 1064177.9Answer = Turns out a lot of restaurant got the lowest ratings, lets see where most of these 3 star rating restaurant located and their Cuisines.
table(Low_rate$Cuisine, Low_rate$Location)##
## Downtown Rural Suburban
## American 14 7 13
## French 6 19 21
## Indian 11 13 13
## Italian 13 11 9
## Japanese 11 11 13
## Mexican 11 5 10Answer = Based on this table we see that most 3 star ratings comes from French Restaurants in the Suburban area.
4. Which Cuisine got most of the highest rating ?
high_rate <- Rest_clean[Rest_clean$Rating == max(Rest_clean$Rating), ]
table(high_rate$Cuisine, high_rate$Location)##
## Downtown Rural Suburban
## American 18 14 13
## French 23 10 11
## Indian 10 11 12
## Italian 16 8 9
## Japanese 16 8 9
## Mexican 10 16 19Answer = From this table, we see that most 5 star reviewed restaurants are from French Restaurants at Downtown. 5. Which Cuisine makes more than 3rd Quarter of Revenue?
third <- Rest_clean[Rest_clean$Revenue >= 813094, ]
table(third$Cuisine, third$Location)##
## Downtown Rural Suburban
## American 88 0 0
## French 482 0 239
## Indian 6 0 0
## Italian 414 0 33
## Japanese 433 15 382
## Mexican 0 0 0Analysis = 1. Japanese restaurants at all location manage to make more than $813,094 in Revenue 2. French restaurants located in Downtown are the most restaurant that make more than $813,094 in Revenue 3. Mexican restaurants at all location doesn’t manage to make more than $813,094 in Revenue
6. Does Parking Availability affects revenue ?
aggregate(Revenue ~ Parking.Availability, Rest_clean, mean)## Parking.Availability Revenue
## 1 No 657020.4
## 2 Yes 655123.0Answer = It seems that Parking Availability does not affect Revenue because both “yes” and “no” Parking Availability have similar Revenue
7. Chef Experience Years in contributing to Service Quality Score
aggregate(Service.Quality.Score ~ Chef.Experience.Years, Rest_clean, mean)## Chef.Experience.Years Service.Quality.Score
## 1 1 5.538747
## 2 2 5.414673
## 3 3 5.572667
## 4 4 5.459957
## 5 5 5.762759
## 6 6 5.494533
## 7 7 5.385446
## 8 8 5.665757
## 9 9 5.336878
## 10 10 5.663962
## 11 11 5.623963
## 12 12 5.463736
## 13 13 5.538688
## 14 14 5.357428
## 15 15 5.587961
## 16 16 5.551448
## 17 17 5.371242
## 18 18 5.420940
## 19 19 5.501987Answer = Here we see that there isn’t a significance of change in Service Quality Score based on how long the experience of the chef
8. Then does Chef Experience Years contributes to Revenues
?
aggregate(Revenue ~ Chef.Experience.Years, Rest_clean, mean)## Chef.Experience.Years Revenue
## 1 1 642338.5
## 2 2 628105.1
## 3 3 638189.5
## 4 4 645565.4
## 5 5 655824.7
## 6 6 675276.8
## 7 7 640753.6
## 8 8 665036.6
## 9 9 652081.4
## 10 10 661643.0
## 11 11 662135.0
## 12 12 662264.5
## 13 13 675740.3
## 14 14 668616.3
## 15 15 645812.4
## 16 16 650993.2
## 17 17 667485.8
## 18 18 667927.6
## 19 19 658468.7barplot(xtabs(Revenue ~ Chef.Experience.Years, Rest_clean))
Answer = not so either as there isn’t a significance of change in Revenue based on how long the experience of the chef
#5. Conclusion 1. If your Restaurant located in Downtown area you might want to make French Cuisine 2. If your Restaurant located in Rural or Suburban area you might want to make Japanese Cuisine 3. A chef experience in years does not correlate to Service Quality Score nor does it correlates to Revenue
#6 Reference https://www.kaggle.com/datasets/anthonytherrien/restaurant-revenue-prediction-dataset