A6_Silverstein_Scott
Scott Silverstein, Ahsan Ahmad, Rylan Tribush, Raunak Sharma
2024-04-23
- Chode
Chunk 1: Load packages, prepare and inspect the data
- A) Package loading, and Walmart_visits_7trips.csv Download Walmart_visits_7trips.csvimport and transformation. Show the overall structure of the input file. Transform factor variables, and show a summary of the input data file.
- B. (3 points) Understand this data set using correlation analysis (pairs.panels from psych)
- Code
chunk 2 - Use SimpleKMeans clustering to understand visits (42
points)
- a. Save the number of unique TripType in the imported data as TripType.levels. Remove TripType from input data.
- b. Generate clusters with the default (i.e. random) initial cluster assignment and the default distance function (Euclidean). The number of clusters equals to TripType.levels. Show the clustering information with the standard deviations and the centroids of the clusters.
- C. Keep the number of clusters at TripType.levels and the Euclidean distance function. Change the initial cluster assignment method to the Kmeans++ method. Cluster the visits again and show the standard deviations and the centroids of the clusters.
- c. Keep the number of clusters at TripType.levels and the initial cluster assignment method to be the Kmeans++ method. Change the distance function to “weka.core.ManhattanDistance”. Cluster the visits again and show the standard deviations and the centroids of the clusters.
- d. Choose your own distance function and initial cluster assignment method, increase or decrease the number of clusters. Cluster the visits again and show the standard deviations and the centroids of the clusters.
- Code
Chunk 3 - Market Basket Analysis with the Walmart dept baskets
- a. (7 points) Import Walmart_baskets_1week.csv Download Walmart_baskets_1week.csvusing the following read.transactions() with the “single” format (for long format) and save it in a sparse matrix called, e.g., Dept_baskets.
- b. Inspect the first 15 transactions.
- c. Use
the itemFrequencyPlot command to plot the most frequent 15 items in the
descending order of transaction frequency in percentage.
- i. Use the apriori command to generate about 50 to 100 association rules from the input data. Set your own minimum support and confidence threshold levels. Remember if the thresholds are too low, you will generate more rules than desired, or if you set them too high, you may not generate any or a sufficient number of rules. Show the rules in the descending order of their lift values.
- ii. Similar to the last task, use the apriori command now to generate about 100 - 200 association rules from the input data. Set your own minimum support and confidence threshold levels. Show the rules in the descending order of their lift values.
- Task II
Reflections
- 1. What were the minimum support level and the minimum confidence level you selected for the Association Rule Mining tasks? Given these levels, What is the rule with the highest lift given your final choices of these levels? What is the rule with the highest support level? What is the rule with the highest confidence level? Which rule out of these three (or fewer) do you recommend for sales executives to consider? What is the reason for your recommendation?
- 3. If you were explaining the results of these models to a supervisor what would you say about them? Attempt to do more than just state facts here, interpret the results. Coding is great, interpretation of output is even more important. Discuss each model. Write at least 150 words
library(knitr)## Warning: package 'knitr' was built under R version 4.3.2library(RWeka)## Warning: package 'RWeka' was built under R version 4.3.2library(rpart)
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A) Package loading, and Walmart_visits_7trips.csv Download Walmart_visits_7trips.csvimport and transformation. Show the overall structure of the input file. Transform factor variables, and show a summary of the input data file.
trips <- read.csv("walmart_visits_7trips.csv")
trips <- trips %>%
mutate(across(where(is.character), as.factor))
# Convert TripType from integer to factor
trips$TripType <- as.factor(trips$TripType)
# Verify the changes
str(trips)## 'data.frame': 12734 obs. of 9 variables:
## $ TripType : Factor w/ 7 levels "5","7","8","9",..: 7 7 7 7 7 7 7 7 7 7 ...
## $ DOW : Factor w/ 7 levels "Friday","Monday",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ UniqueItems : int 1 2 1 1 2 1 1 1 1 1 ...
## $ TotalQty : int 0 1 0 0 3 0 0 0 1 0 ...
## $ RtrnQty : int 1 1 1 1 0 1 1 1 0 1 ...
## $ NetQty : int -1 0 -1 -1 3 -1 -1 -1 1 -1 ...
## $ UniqDepts : int 1 1 1 1 2 1 1 1 1 1 ...
## $ OneItemDepts: int 1 0 1 1 2 1 1 1 1 1 ...
## $ RtrnDepts : int 1 1 1 1 0 1 1 1 0 1 ...B. (3 points) Understand this data set using correlation analysis (pairs.panels from psych)
pairs.panels(trips)
## C. (7 points) Build a descriptive C5.0 decision tree using the entire
data set (TripType is the target variable). Prune the tree so that the
number of tree leaves is smaller than 15 (use CF value to prune the
tree). Plot the tree and show summary of the model to view tree rules
and confusion matrix.
model <- C5.0(TripType ~ ., data = trips, control = C5.0Control(noGlobalPruning = FALSE, CF = 0.1))summary(model)##
## Call:
## C5.0.formula(formula = TripType ~ ., data = trips, control
## = C5.0Control(noGlobalPruning = FALSE, CF = 0.1))
##
##
## C5.0 [Release 2.07 GPL Edition] Tue Apr 23 19:00:28 2024
## -------------------------------
##
## Class specified by attribute `outcome'
##
## Read 12734 cases (9 attributes) from undefined.data
##
## Decision tree:
##
## NetQty <= 3:
## :...NetQty <= 0: 999 (1345)
## : NetQty > 0: 8 (6660/3899)
## NetQty > 3:
## :...NetQty > 19: 40 (1377/59)
## NetQty <= 19:
## :...UniqDepts > 2: 39 (2901/820)
## UniqDepts <= 2:
## :...UniqueItems <= 1: 5 (42/17)
## UniqueItems > 1:
## :...TotalQty <= 5: 7 (252/154)
## TotalQty > 5: 39 (157/106)
##
##
## Evaluation on training data (12734 cases):
##
## Decision Tree
## ----------------
## Size Errors
##
## 7 5055(39.7%) <<
##
##
## (a) (b) (c) (d) (e) (f) (g) <-classified as
## ---- ---- ---- ---- ---- ---- ----
## 25 60 763 246 12 (a): class 5
## 7 98 674 522 4 (b): class 7
## 25 2761 22 (c): class 8
## 11 2020 15 (d): class 9
## 8 39 7 2132 43 (e): class 39
## 1 54 1318 (f): class 40
## 2 18 435 67 1345 (g): class 999
##
##
## Attribute usage:
##
## 100.00% NetQty
## 26.32% UniqDepts
## 3.54% UniqueItems
## 3.21% TotalQty
##
##
## Time: 0.0 secsCode chunk 2 - Use SimpleKMeans clustering to understand visits (42 points)
a. Save the number of unique TripType in the imported data as TripType.levels. Remove TripType from input data.
# Save the number of unique TripType levels
TripType.levels <- length(unique(trips$TripType))
# Remove TripType from the input data
trips <- trips[, !(names(trips) %in% c("TripType"))]
str(trips)## 'data.frame': 12734 obs. of 8 variables:
## $ DOW : Factor w/ 7 levels "Friday","Monday",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ UniqueItems : int 1 2 1 1 2 1 1 1 1 1 ...
## $ TotalQty : int 0 1 0 0 3 0 0 0 1 0 ...
## $ RtrnQty : int 1 1 1 1 0 1 1 1 0 1 ...
## $ NetQty : int -1 0 -1 -1 3 -1 -1 -1 1 -1 ...
## $ UniqDepts : int 1 1 1 1 2 1 1 1 1 1 ...
## $ OneItemDepts: int 1 0 1 1 2 1 1 1 1 1 ...
## $ RtrnDepts : int 1 1 1 1 0 1 1 1 0 1 ...b. Generate clusters with the default (i.e. random) initial cluster assignment and the default distance function (Euclidean). The number of clusters equals to TripType.levels. Show the clustering information with the standard deviations and the centroids of the clusters.
set.seed(123)
# Set the number of clusters to the value stored in TripType.levels and use random initialization
trips_kmeans <- SimpleKMeans(trips, Weka_control(N=TripType.levels, init=0, V=TRUE))
trips_kmeans##
## kMeans
## ======
##
## Number of iterations: 14
## Within cluster sum of squared errors: 3983.3256867784276
##
## Initial starting points (random):
##
## Cluster 0: Friday,4,0,4,-4,2,1,2
## Cluster 1: Wednesday,2,2,0,2,2,2,0
## Cluster 2: Thursday,1,0,1,-1,1,1,1
## Cluster 3: Wednesday,1,1,0,1,1,1,0
## Cluster 4: Sunday,1,1,1,0,1,1,1
## Cluster 5: Saturday,5,5,0,5,3,2,0
## Cluster 6: Friday,4,4,0,4,4,4,0
##
## Missing values globally replaced with mean/mode
##
## Final cluster centroids:
## Cluster#
## Attribute Full Data 0 1 2 3 4 5 6
## (12734.0) (1926.0) (795.0) (2359.0) (2838.0) (2319.0) (1982.0) (515.0)
## ==============================================================================================================
## DOW Sunday Friday Wednesday Thursday Wednesday Sunday Saturday Friday
## Friday 1851.0 ( 14%)1569.0 ( 81%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%)282.0 ( 54%)
## Monday 1765.0 ( 13%)187.0 ( 9%)216.0 ( 27%)320.0 ( 13%)707.0 ( 24%)158.0 ( 6%) 35.0 ( 1%)142.0 ( 27%)
## Saturday 1923.0 ( 15%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%)1922.0 ( 96%) 1.0 ( 0%)
## Sunday 2014.0 ( 15%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%)2014.0 ( 86%) 0.0 ( 0%) 0.0 ( 0%)
## Thursday 1738.0 ( 13%) 0.0 ( 0%) 0.0 ( 0%)1737.0 ( 73%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 1.0 ( 0%)
## Tuesday 1703.0 ( 13%)170.0 ( 8%)210.0 ( 26%)302.0 ( 12%)760.0 ( 26%)147.0 ( 6%) 25.0 ( 1%) 89.0 ( 17%)
## Wednesday 1740.0 ( 13%) 0.0 ( 0%)369.0 ( 46%) 0.0 ( 0%)1371.0 ( 48%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%)
##
## UniqueItems 6.2033 2.6651 14.1799 4.7512 2.0247 7.9461 6.7735 26.7592
## +/-9.1936 +/-2.8401 +/-8.4465 +/-7.3608 +/-1.6575 +/-10.1846 +/-9.323 +/-14.0425
##
## TotalQty 7.1849 2.8411 16.7157 5.5019 2.1945 9.2902 7.8158 32.0175
## +/-11.2831 +/-3.91 +/-10.7118 +/-9.3125 +/-2.2108 +/-12.458 +/-11.3376 +/-17.2611
##
## RtrnQty 0.2431 0.4361 0.2113 0.178 0.1853 0.229 0.2619 0.1786
## +/-0.9149 +/-0.9064 +/-1.9588 +/-0.5966 +/-0.8215 +/-0.808 +/-0.8843 +/-0.4783
##
## NetQty 6.9417 2.405 16.5044 5.3239 2.0092 9.0612 7.554 31.8388
## +/-11.3513 +/-4.1859 +/-10.9241 +/-9.3264 +/-2.3983 +/-12.5132 +/-11.4112 +/-17.1912
##
## UniqDepts 3.1938 1.812 7.2327 2.7897 1.4137 3.8482 3.4026 10.0369
## +/-3.0952 +/-1.2409 +/-2.0374 +/-2.4401 +/-0.7464 +/-3.2775 +/-3.1987 +/-2.6882
##
## OneItemDepts 1.9179 1.3032 4.2264 1.9262 0.9683 2.1458 1.9581 4.666
## +/-1.6439 +/-0.9011 +/-1.4281 +/-1.3618 +/-0.6762 +/-1.669 +/-1.6589 +/-2.0273
##
## RtrnDepts 0.1756 0.3344 0.122 0.1412 0.1163 0.1686 0.1821 0.1553
## +/-0.4553 +/-0.5473 +/-0.5254 +/-0.4035 +/-0.358 +/-0.4686 +/-0.473 +/-0.3935C. Keep the number of clusters at TripType.levels and the Euclidean distance function. Change the initial cluster assignment method to the Kmeans++ method. Cluster the visits again and show the standard deviations and the centroids of the clusters.
set.seed(123)
trips_kmeans2 <- SimpleKMeans(trips, Weka_control(N=TripType.levels, init=2, V=TRUE))
# Print the clustering results
trips_kmeans2##
## kMeans
## ======
##
## Number of iterations: 2
## Within cluster sum of squared errors: 787.2085533735577
##
## Initial starting points (canopy):
##
## T2 radius: 0.735
## T1 radius: 0.918
##
## Cluster 0: Saturday,6.520627,7.496606,0.256397,7.240209,3.344648,1.965013,0.179634,{1915} <0>
## Cluster 1: Sunday,5.866912,6.731194,0.235139,6.496055,3.171489,1.904261,0.173067,{1901} <1>
## Cluster 2: Friday,4.990044,5.815265,0.221792,5.593473,2.775442,1.732854,0.160951,{1808} <2>
## Cluster 3: Wednesday,5.061778,5.773672,0.236143,5.537529,2.872402,1.82448,0.17321,{1732} <3>
## Cluster 4: Thursday,4.950408,5.711785,0.226954,5.484831,2.802217,1.786464,0.177946,{1714} <4>
## Cluster 5: Tuesday,5.248968,6.025369,0.227729,5.79764,2.905605,1.827139,0.158112,{1695} <5>
## Cluster 6: Monday,5.145895,5.949793,0.249262,5.700532,2.890136,1.802717,0.182516,{1693} <6>
##
##
## Missing values globally replaced with mean/mode
##
## Final cluster centroids:
## Cluster#
## Attribute Full Data 0 1 2 3 4 5 6
## (12734.0) (1923.0) (2014.0) (1851.0) (1740.0) (1738.0) (1703.0) (1765.0)
## ==============================================================================================================
## DOW Sunday Saturday Sunday Friday Wednesday Thursday Tuesday Monday
## Friday 1851.0 ( 14%) 0.0 ( 0%) 0.0 ( 0%)1851.0 (100%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%)
## Monday 1765.0 ( 13%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%)1765.0 (100%)
## Saturday 1923.0 ( 15%)1923.0 (100%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%)
## Sunday 2014.0 ( 15%) 0.0 ( 0%)2014.0 (100%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%)
## Thursday 1738.0 ( 13%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%)1738.0 (100%) 0.0 ( 0%) 0.0 ( 0%)
## Tuesday 1703.0 ( 13%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%)1703.0 (100%) 0.0 ( 0%)
## Wednesday 1740.0 ( 13%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%)1740.0 (100%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%)
##
## UniqueItems 6.2033 6.7894 7.7066 5.7736 5.296 5.538 5.4839 6.5439
## +/-9.1936 +/-9.7603 +/-10.7485 +/-8.5904 +/-7.8033 +/-8.5521 +/-8.1858 +/-9.7657
##
## TotalQty 7.1849 7.8248 8.922 6.7634 6.0575 6.4764 6.2959 7.6142
## +/-11.2831 +/-11.8634 +/-13.0421 +/-10.6401 +/-9.5957 +/-10.9471 +/-9.9807 +/-11.8781
##
## RtrnQty 0.2431 0.2579 0.2423 0.222 0.2379 0.2307 0.259 0.2521
## +/-0.9149 +/-0.8644 +/-0.8231 +/-0.7135 +/-0.9047 +/-0.6686 +/-1.4742 +/-0.7585
##
## NetQty 6.9417 7.5668 8.6797 6.5413 5.8195 6.2457 6.037 7.362
## +/-11.3513 +/-11.9357 +/-13.0922 +/-10.7088 +/-9.642 +/-10.9675 +/-10.1714 +/-11.9312
##
## UniqDepts 3.1938 3.4041 3.7115 3.013 2.931 2.9413 2.9571 3.2997
## +/-3.0952 +/-3.2653 +/-3.4818 +/-2.9741 +/-2.8278 +/-2.8288 +/-2.8278 +/-3.2188
##
## OneItemDepts 1.9179 1.9823 2.1018 1.8293 1.8402 1.8349 1.8426 1.9615
## +/-1.6439 +/-1.6761 +/-1.7462 +/-1.6318 +/-1.554 +/-1.5575 +/-1.622 +/-1.6717
##
## RtrnDepts 0.1756 0.181 0.1783 0.1621 0.1753 0.1818 0.165 0.1853
## +/-0.4553 +/-0.4703 +/-0.4702 +/-0.427 +/-0.4509 +/-0.4454 +/-0.4754 +/-0.4444c. Keep the number of clusters at TripType.levels and the initial cluster assignment method to be the Kmeans++ method. Change the distance function to “weka.core.ManhattanDistance”. Cluster the visits again and show the standard deviations and the centroids of the clusters.
set.seed(123)
trips_kmeans3 <- SimpleKMeans(trips, Weka_control(N=TripType.levels, init=2, A ="weka.core.ManhattanDistance", V=TRUE))
# Print the clustering results
trips_kmeans3##
## kMeans
## ======
##
## Number of iterations: 2
## Sum of within cluster distances: 4319.921389839508
##
## Initial starting points (canopy):
##
## T2 radius: 0.735
## T1 radius: 0.918
##
## Cluster 0: Saturday,6.520627,7.496606,0.256397,7.240209,3.344648,1.965013,0.179634,{1915} <0>
## Cluster 1: Sunday,5.866912,6.731194,0.235139,6.496055,3.171489,1.904261,0.173067,{1901} <1>
## Cluster 2: Friday,4.990044,5.815265,0.221792,5.593473,2.775442,1.732854,0.160951,{1808} <2>
## Cluster 3: Wednesday,5.061778,5.773672,0.236143,5.537529,2.872402,1.82448,0.17321,{1732} <3>
## Cluster 4: Thursday,4.950408,5.711785,0.226954,5.484831,2.802217,1.786464,0.177946,{1714} <4>
## Cluster 5: Tuesday,5.248968,6.025369,0.227729,5.79764,2.905605,1.827139,0.158112,{1695} <5>
## Cluster 6: Monday,5.145895,5.949793,0.249262,5.700532,2.890136,1.802717,0.182516,{1693} <6>
##
##
## Missing values globally replaced with mean/mode
##
## Final cluster centroids:
## Cluster#
## Attribute Full Data 0 1 2 3 4 5 6
## (12734.0) (1923.0) (2014.0) (1851.0) (1740.0) (1738.0) (1703.0) (1765.0)
## ==============================================================================================================
## DOW Sunday Saturday Sunday Friday Wednesday Thursday Tuesday Monday
## Friday 1851.0 ( 14%) 0.0 ( 0%) 0.0 ( 0%)1851.0 (100%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%)
## Monday 1765.0 ( 13%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%)1765.0 (100%)
## Saturday 1923.0 ( 15%)1923.0 (100%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%)
## Sunday 2014.0 ( 15%) 0.0 ( 0%)2014.0 (100%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%)
## Thursday 1738.0 ( 13%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%)1738.0 (100%) 0.0 ( 0%) 0.0 ( 0%)
## Tuesday 1703.0 ( 13%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%)1703.0 (100%) 0.0 ( 0%)
## Wednesday 1740.0 ( 13%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%)1740.0 (100%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%)
##
## UniqueItems 2 2 3 2 2 2 2 2
## +/-9.1936 +/-9.7603 +/-10.7485 +/-8.5904 +/-7.8033 +/-8.5521 +/-8.1858 +/-9.7657
##
## TotalQty 3 3 3 2 2 2 2 2
## +/-11.2831 +/-11.8634 +/-13.0421 +/-10.6401 +/-9.5957 +/-10.9471 +/-9.9807 +/-11.8781
##
## RtrnQty 0 0 0 0 0 0 0 0
## +/-0.9149 +/-0.8644 +/-0.8231 +/-0.7135 +/-0.9047 +/-0.6686 +/-1.4742 +/-0.7585
##
## NetQty 2 3 3 2 2 2 2 2
## +/-11.3513 +/-11.9357 +/-13.0922 +/-10.7088 +/-9.642 +/-10.9675 +/-10.1714 +/-11.9312
##
## UniqDepts 2 2 2 2 2 2 2 2
## +/-3.0952 +/-3.2653 +/-3.4818 +/-2.9741 +/-2.8278 +/-2.8288 +/-2.8278 +/-3.2188
##
## OneItemDepts 1 1 2 1 1 1 1 1
## +/-1.6439 +/-1.6761 +/-1.7462 +/-1.6318 +/-1.554 +/-1.5575 +/-1.622 +/-1.6717
##
## RtrnDepts 0 0 0 0 0 0 0 0
## +/-0.4553 +/-0.4703 +/-0.4702 +/-0.427 +/-0.4509 +/-0.4454 +/-0.4754 +/-0.4444d. Choose your own distance function and initial cluster assignment method, increase or decrease the number of clusters. Cluster the visits again and show the standard deviations and the centroids of the clusters.
set.seed(123)
new_cluster_count <- round(TripType.levels * 1.2)
trips_kmeans4 <- SimpleKMeans(trips, Weka_control(N=new_cluster_count, init=2, A ="weka.core.ManhattanDistance", V=TRUE))
trips_kmeans4 ##
## kMeans
## ======
##
## Number of iterations: 8
## Sum of within cluster distances: 3940.365619940344
##
## Initial starting points (canopy):
##
## T2 radius: 0.735
## T1 radius: 0.918
##
## Cluster 0: Saturday,6.520627,7.496606,0.256397,7.240209,3.344648,1.965013,0.179634,{1915} <0>
## Cluster 1: Sunday,5.866912,6.731194,0.235139,6.496055,3.171489,1.904261,0.173067,{1901} <1,7>
## Cluster 2: Friday,4.990044,5.815265,0.221792,5.593473,2.775442,1.732854,0.160951,{1808} <2>
## Cluster 3: Wednesday,5.061778,5.773672,0.236143,5.537529,2.872402,1.82448,0.17321,{1732} <3>
## Cluster 4: Thursday,4.950408,5.711785,0.226954,5.484831,2.802217,1.786464,0.177946,{1714} <4>
## Cluster 5: Tuesday,5.248968,6.025369,0.227729,5.79764,2.905605,1.827139,0.158112,{1695} <5>
## Cluster 6: Monday,5.145895,5.949793,0.249262,5.700532,2.890136,1.802717,0.182516,{1693} <6>
## Cluster 7: Sunday,38.285714,45.410714,0.366071,45.044643,12.732143,5.410714,0.267857,{112} <1,7>
##
##
## Missing values globally replaced with mean/mode
##
## Final cluster centroids:
## Cluster#
## Attribute Full Data 0 1 2 3 4 5 6 7
## (12734.0) (1825.0) (1596.0) (1777.0) (1691.0) (1681.0) (1651.0) (1688.0) (825.0)
## ==========================================================================================================================
## DOW Sunday Saturday Sunday Friday Wednesday Thursday Tuesday Monday Sunday
## Friday 1851.0 ( 14%) 0.0 ( 0%) 0.0 ( 0%)1777.0 (100%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 74.0 ( 8%)
## Monday 1765.0 ( 13%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%)1688.0 (100%) 77.0 ( 9%)
## Saturday 1923.0 ( 15%)1825.0 (100%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 98.0 ( 11%)
## Sunday 2014.0 ( 15%) 0.0 ( 0%)1596.0 (100%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%)418.0 ( 50%)
## Thursday 1738.0 ( 13%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%)1681.0 (100%) 0.0 ( 0%) 0.0 ( 0%) 57.0 ( 6%)
## Tuesday 1703.0 ( 13%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%)1651.0 (100%) 0.0 ( 0%) 52.0 ( 6%)
## Wednesday 1740.0 ( 13%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%)1691.0 (100%) 0.0 ( 0%) 0.0 ( 0%) 0.0 ( 0%) 49.0 ( 5%)
##
## UniqueItems 2 2 2 2 2 2 2 2 26
## +/-9.1936 +/-6.622 +/-3.4343 +/-6.38 +/-6.0338 +/-6.356 +/-6.2806 +/-6.8107 +/-13.9136
##
## TotalQty 3 2 2 2 2 2 2 2 31
## +/-11.2831 +/-8.2988 +/-4.3348 +/-8.0467 +/-7.552 +/-8.2405 +/-7.8886 +/-8.6048 +/-17.2505
##
## RtrnQty 0 0 0 0 0 0 0 0 0
## +/-0.9149 +/-0.8779 +/-0.8714 +/-0.7194 +/-0.9137 +/-0.6596 +/-1.4933 +/-0.7651 +/-0.6121
##
## NetQty 2 2 2 2 2 2 2 2 31
## +/-11.3513 +/-8.4082 +/-4.5068 +/-8.146 +/-7.616 +/-8.3028 +/-8.1314 +/-8.6881 +/-17.1666
##
## UniqDepts 2 2 2 2 2 2 2 2 11
## +/-3.0952 +/-2.5462 +/-1.5556 +/-2.3558 +/-2.3573 +/-2.2802 +/-2.3145 +/-2.5065 +/-2.6926
##
## OneItemDepts 1 1 1 1 1 1 1 1 5
## +/-1.6439 +/-1.4805 +/-1.1071 +/-1.4415 +/-1.4126 +/-1.3885 +/-1.4579 +/-1.476 +/-1.7711
##
## RtrnDepts 0 0 0 0 0 0 0 0 0
## +/-0.4553 +/-0.4739 +/-0.4858 +/-0.4258 +/-0.4526 +/-0.4382 +/-0.4769 +/-0.4443 +/-0.4316Code Chunk 3 - Market Basket Analysis with the Walmart dept baskets
a. (7 points) Import Walmart_baskets_1week.csv Download Walmart_baskets_1week.csvusing the following read.transactions() with the “single” format (for long format) and save it in a sparse matrix called, e.g., Dept_baskets.
Dept_baskets <- read.transactions("Walmart_baskets_1week.csv", format="single", sep = ",", header = TRUE, cols=c("VisitNumber","DepartmentDescription"))b. Inspect the first 15 transactions.
# Inspecting the first 15 transactions
inspect(Dept_baskets[1:15])## items transactionID
## [1] {IMPULSE MERCHANDISE} 10009
## [2] {CANDY, TOBACCO, COOKIES,
## HOME MANAGEMENT,
## JEWELRY AND SUNGLASSES,
## MENS WEAR} 10034
## [3] {GIRLS WEAR, 4-6X AND 7-14,
## GROCERY DRY GOODS,
## IMPULSE MERCHANDISE,
## PERSONAL CARE} 10051
## [4] {OTHER DEPARTMENTS} 10118
## [5] {FINANCIAL SERVICES} 10167
## [6] {DAIRY,
## DSD GROCERY,
## FROZEN FOODS,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN,
## PERSONAL CARE,
## PRE PACKED DELI,
## SLEEPWEAR/FOUNDATIONS} 10178
## [7] {PHARMACY OTC} 10191
## [8] {FINANCIAL SERVICES} 10206
## [9] {INFANT CONSUMABLE HARDLINES} 1022
## [10] {CELEBRATION,
## FABRICS AND CRAFTS,
## OFFICE SUPPLIES,
## TOYS} 10250
## [11] {PHARMACY OTC} 10272
## [12] {FABRICS AND CRAFTS,
## GROCERY DRY GOODS,
## PRODUCE} 10273
## [13] {SERVICE DELI} 1029
## [14] {BEAUTY,
## DSD GROCERY,
## GROCERY DRY GOODS,
## HOME DECOR,
## HOME MANAGEMENT,
## HOUSEHOLD CHEMICALS/SUPP,
## HOUSEHOLD PAPER GOODS,
## LAWN AND GARDEN,
## PAINT AND ACCESSORIES,
## PERSONAL CARE,
## PETS AND SUPPLIES} 10296
## [15] {BEAUTY} 10302c. Use the itemFrequencyPlot command to plot the most frequent 15 items in the descending order of transaction frequency in percentage.
# Plot the most frequent 15 items
itemFrequencyPlot(Dept_baskets, topN = 15, type = "absolute", main = "Top 15 Frequent Items", col = "blue", las = 2)
## D. (20 points) Associate rule minin
i. Use the apriori command to generate about 50 to 100 association rules from the input data. Set your own minimum support and confidence threshold levels. Remember if the thresholds are too low, you will generate more rules than desired, or if you set them too high, you may not generate any or a sufficient number of rules. Show the rules in the descending order of their lift values.
rules <- apriori(Dept_baskets,
parameter = list(supp = 0.01, conf = 0.1, target = "rules", minlen = 2),
control = list(verbose = FALSE))
# Sorting the rules by lift in descending order
rules_sorted_by_lift <- sort(rules, by = "lift", decreasing = TRUE)
# top rules
inspect(head(rules_sorted_by_lift, n = 100))## lhs rhs support confidence coverage lift count
## [1] {DAIRY,
## FROZEN FOODS,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0140 0.6511628 0.0215 11.947941 28
## [2] {DAIRY,
## DSD GROCERY,
## FROZEN FOODS,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0125 0.6410256 0.0195 11.761938 25
## [3] {FROZEN FOODS,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0150 0.6382979 0.0235 11.711888 30
## [4] {DSD GROCERY,
## FROZEN FOODS,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0135 0.6279070 0.0215 11.521229 27
## [5] {FROZEN FOODS,
## PRODUCE} => {SEAFOOD} 0.0110 0.2095238 0.0525 11.325611 22
## [6] {FROZEN FOODS,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {PRE PACKED DELI} 0.0125 0.6097561 0.0205 11.188185 25
## [7] {DAIRY,
## FROZEN FOODS,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {PRE PACKED DELI} 0.0115 0.6052632 0.0190 11.105746 23
## [8] {DSD GROCERY,
## FROZEN FOODS,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {PRE PACKED DELI} 0.0115 0.6052632 0.0190 11.105746 23
## [9] {DAIRY,
## FROZEN FOODS,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0115 0.6052632 0.0190 11.105746 23
## [10] {DAIRY,
## DSD GROCERY,
## FROZEN FOODS,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {PRE PACKED DELI} 0.0105 0.6000000 0.0175 11.009174 21
## [11] {DAIRY,
## DSD GROCERY,
## FROZEN FOODS,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0100 0.5882353 0.0170 10.793308 20
## [12] {FROZEN FOODS,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0120 0.5853659 0.0205 10.740658 24
## [13] {DAIRY,
## FROZEN FOODS,
## PRE PACKED DELI} => {MEAT - FRESH & FROZEN} 0.0140 0.6666667 0.0210 10.666667 28
## [14] {FROZEN FOODS,
## PRE PACKED DELI,
## PRODUCE} => {MEAT - FRESH & FROZEN} 0.0125 0.6578947 0.0190 10.526316 25
## [15] {DAIRY,
## DSD GROCERY,
## FROZEN FOODS,
## PRE PACKED DELI} => {MEAT - FRESH & FROZEN} 0.0125 0.6578947 0.0190 10.526316 25
## [16] {DAIRY,
## FROZEN FOODS,
## PRE PACKED DELI,
## PRODUCE} => {MEAT - FRESH & FROZEN} 0.0115 0.6571429 0.0175 10.514286 23
## [17] {DSD GROCERY,
## FROZEN FOODS,
## PRE PACKED DELI,
## PRODUCE} => {MEAT - FRESH & FROZEN} 0.0115 0.6571429 0.0175 10.514286 23
## [18] {DAIRY,
## DSD GROCERY,
## FROZEN FOODS,
## PRE PACKED DELI,
## PRODUCE} => {MEAT - FRESH & FROZEN} 0.0105 0.6562500 0.0160 10.500000 21
## [19] {COMM BREAD,
## DAIRY,
## DSD GROCERY,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0125 0.5681818 0.0220 10.425354 25
## [20] {DSD GROCERY,
## FROZEN FOODS,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0105 0.5675676 0.0185 10.414084 21
## [21] {COMM BREAD,
## DAIRY,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0130 0.5652174 0.0230 10.370961 26
## [22] {DAIRY,
## FROZEN FOODS,
## GROCERY DRY GOODS,
## PRE PACKED DELI} => {MEAT - FRESH & FROZEN} 0.0115 0.6388889 0.0180 10.222222 23
## [23] {COMM BREAD,
## DAIRY,
## DSD GROCERY,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0130 0.5416667 0.0240 9.938838 26
## [24] {DSD GROCERY,
## FROZEN FOODS,
## PRE PACKED DELI} => {MEAT - FRESH & FROZEN} 0.0135 0.6136364 0.0220 9.818182 27
## [25] {FROZEN FOODS,
## PRE PACKED DELI} => {MEAT - FRESH & FROZEN} 0.0150 0.6122449 0.0245 9.795918 30
## [26] {COMM BREAD,
## DAIRY,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0135 0.5294118 0.0255 9.713977 27
## [27] {DAIRY,
## DSD GROCERY,
## FROZEN FOODS,
## GROCERY DRY GOODS,
## PRE PACKED DELI} => {MEAT - FRESH & FROZEN} 0.0100 0.6060606 0.0165 9.696970 20
## [28] {DAIRY,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0190 0.5277778 0.0360 9.683996 38
## [29] {COMM BREAD,
## DAIRY,
## DSD GROCERY,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {PRE PACKED DELI} 0.0100 0.5263158 0.0190 9.657170 20
## [30] {FROZEN FOODS,
## GROCERY DRY GOODS,
## PRE PACKED DELI} => {MEAT - FRESH & FROZEN} 0.0120 0.6000000 0.0200 9.600000 24
## [31] {COMM BREAD,
## DSD GROCERY,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0125 0.5208333 0.0240 9.556575 25
## [32] {COMM BREAD,
## DAIRY,
## DSD GROCERY,
## GROCERY DRY GOODS,
## PRE PACKED DELI} => {MEAT - FRESH & FROZEN} 0.0125 0.5952381 0.0210 9.523810 25
## [33] {DAIRY,
## DSD GROCERY,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0160 0.5161290 0.0310 9.470257 32
## [34] {COMM BREAD,
## DAIRY,
## DSD GROCERY,
## PRE PACKED DELI} => {MEAT - FRESH & FROZEN} 0.0130 0.5909091 0.0220 9.454545 26
## [35] {DAIRY,
## DSD GROCERY,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0185 0.5138889 0.0360 9.429154 37
## [36] {COMM BREAD,
## DAIRY,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {PRE PACKED DELI} 0.0100 0.5128205 0.0195 9.409551 20
## [37] {DSD GROCERY,
## FROZEN FOODS,
## GROCERY DRY GOODS,
## PRE PACKED DELI} => {MEAT - FRESH & FROZEN} 0.0105 0.5833333 0.0180 9.333333 21
## [38] {COMM BREAD,
## DAIRY,
## DSD GROCERY,
## PRE PACKED DELI,
## PRODUCE} => {MEAT - FRESH & FROZEN} 0.0105 0.5833333 0.0180 9.333333 21
## [39] {DAIRY,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0220 0.5057471 0.0435 9.279764 44
## [40] {COMM BREAD,
## DAIRY,
## GROCERY DRY GOODS,
## PRE PACKED DELI} => {MEAT - FRESH & FROZEN} 0.0130 0.5777778 0.0225 9.244444 26
## [41] {DAIRY,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {PRE PACKED DELI} 0.0145 0.5000000 0.0290 9.174312 29
## [42] {DAIRY,
## DSD GROCERY,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {PRE PACKED DELI} 0.0150 0.5000000 0.0300 9.174312 30
## [43] {COMM BREAD,
## DAIRY,
## DSD GROCERY,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {PRE PACKED DELI} 0.0105 0.5000000 0.0210 9.174312 21
## [44] {DAIRY,
## DSD GROCERY,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {PRE PACKED DELI} 0.0125 0.5000000 0.0250 9.174312 25
## [45] {COMM BREAD,
## DAIRY,
## DSD GROCERY,
## GROCERY DRY GOODS,
## PRE PACKED DELI,
## PRODUCE} => {MEAT - FRESH & FROZEN} 0.0100 0.5714286 0.0175 9.142857 20
## [46] {DAIRY,
## GROCERY DRY GOODS,
## PRE PACKED DELI,
## PRODUCE} => {MEAT - FRESH & FROZEN} 0.0145 0.5686275 0.0255 9.098039 29
## [47] {COMM BREAD,
## DAIRY,
## PRE PACKED DELI,
## PRODUCE} => {MEAT - FRESH & FROZEN} 0.0105 0.5675676 0.0185 9.081081 21
## [48] {GROCERY DRY GOODS,
## PRE PACKED DELI,
## PRODUCE} => {MEAT - FRESH & FROZEN} 0.0150 0.5660377 0.0265 9.056604 30
## [49] {DAIRY,
## DSD GROCERY,
## PRE PACKED DELI,
## PRODUCE} => {MEAT - FRESH & FROZEN} 0.0150 0.5660377 0.0265 9.056604 30
## [50] {DAIRY,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {PRE PACKED DELI} 0.0175 0.4929577 0.0355 9.045096 35
## [51] {OFFICE SUPPLIES} => {FABRICS AND CRAFTS} 0.0105 0.2800000 0.0375 9.032258 21
## [52] {FABRICS AND CRAFTS} => {OFFICE SUPPLIES} 0.0105 0.3387097 0.0310 9.032258 21
## [53] {COMM BREAD,
## DSD GROCERY,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0130 0.4905660 0.0265 9.001212 26
## [54] {COMM BREAD,
## DAIRY,
## PRE PACKED DELI} => {MEAT - FRESH & FROZEN} 0.0135 0.5625000 0.0240 9.000000 27
## [55] {COMM BREAD,
## DAIRY,
## DSD GROCERY,
## FROZEN FOODS,
## PRODUCE} => {PRE PACKED DELI} 0.0110 0.4888889 0.0225 8.970438 22
## [56] {COMM BREAD,
## DAIRY,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {PRE PACKED DELI} 0.0105 0.4883721 0.0215 8.960956 21
## [57] {COMM BREAD,
## DSD GROCERY,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {PRE PACKED DELI} 0.0100 0.4878049 0.0205 8.950548 20
## [58] {DSD GROCERY,
## PRE PACKED DELI,
## PRODUCE} => {MEAT - FRESH & FROZEN} 0.0165 0.5593220 0.0295 8.949153 33
## [59] {COMM BREAD,
## DAIRY,
## GROCERY DRY GOODS,
## PRE PACKED DELI,
## PRODUCE} => {MEAT - FRESH & FROZEN} 0.0100 0.5555556 0.0180 8.888889 20
## [60] {COMM BREAD,
## DSD GROCERY,
## GROCERY DRY GOODS,
## PRE PACKED DELI,
## PRODUCE} => {MEAT - FRESH & FROZEN} 0.0100 0.5555556 0.0180 8.888889 20
## [61] {COMM BREAD,
## DSD GROCERY,
## PRE PACKED DELI,
## PRODUCE} => {MEAT - FRESH & FROZEN} 0.0105 0.5526316 0.0190 8.842105 21
## [62] {COMM BREAD,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0130 0.4814815 0.0270 8.834523 26
## [63] {DAIRY,
## GROCERY DRY GOODS,
## PRE PACKED DELI} => {MEAT - FRESH & FROZEN} 0.0190 0.5507246 0.0345 8.811594 38
## [64] {DSD GROCERY,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0170 0.4788732 0.0355 8.786665 34
## [65] {COMM BREAD,
## DAIRY,
## FROZEN FOODS,
## PRODUCE} => {PRE PACKED DELI} 0.0110 0.4782609 0.0230 8.775429 22
## [66] {COMM BREAD,
## DAIRY,
## FROZEN FOODS,
## GROCERY DRY GOODS,
## PRODUCE} => {PRE PACKED DELI} 0.0105 0.4772727 0.0220 8.757298 21
## [67] {COMM BREAD,
## DAIRY,
## DSD GROCERY,
## FROZEN FOODS,
## GROCERY DRY GOODS,
## PRODUCE} => {PRE PACKED DELI} 0.0105 0.4772727 0.0220 8.757298 21
## [68] {DAIRY,
## PRE PACKED DELI,
## PRODUCE} => {MEAT - FRESH & FROZEN} 0.0175 0.5468750 0.0320 8.750000 35
## [69] {COMM BREAD,
## DSD GROCERY,
## GROCERY DRY GOODS,
## PRE PACKED DELI} => {MEAT - FRESH & FROZEN} 0.0125 0.5434783 0.0230 8.695652 25
## [70] {DAIRY,
## DSD GROCERY,
## GROCERY DRY GOODS,
## PRE PACKED DELI,
## PRODUCE} => {MEAT - FRESH & FROZEN} 0.0125 0.5434783 0.0230 8.695652 25
## [71] {PRE PACKED DELI,
## PRODUCE} => {MEAT - FRESH & FROZEN} 0.0190 0.5428571 0.0350 8.685714 38
## [72] {DAIRY,
## DSD GROCERY,
## GROCERY DRY GOODS,
## PRE PACKED DELI} => {MEAT - FRESH & FROZEN} 0.0160 0.5423729 0.0295 8.677966 32
## [73] {DSD GROCERY,
## GROCERY DRY GOODS,
## PRE PACKED DELI,
## PRODUCE} => {MEAT - FRESH & FROZEN} 0.0130 0.5416667 0.0240 8.666667 26
## [74] {COMM BREAD,
## GROCERY DRY GOODS,
## PRE PACKED DELI,
## PRODUCE} => {MEAT - FRESH & FROZEN} 0.0100 0.5405405 0.0185 8.648649 20
## [75] {COMM BREAD,
## PRE PACKED DELI,
## PRODUCE} => {MEAT - FRESH & FROZEN} 0.0105 0.5384615 0.0195 8.615385 21
## [76] {COMM BREAD,
## DSD GROCERY,
## FROZEN FOODS,
## PRODUCE} => {PRE PACKED DELI} 0.0110 0.4680851 0.0235 8.588718 22
## [77] {DAIRY,
## DSD GROCERY,
## PRE PACKED DELI} => {MEAT - FRESH & FROZEN} 0.0185 0.5362319 0.0345 8.579710 37
## [78] {COMM BREAD,
## DSD GROCERY,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {PRE PACKED DELI} 0.0105 0.4666667 0.0225 8.562691 21
## [79] {COMM BREAD,
## FROZEN FOODS,
## GROCERY DRY GOODS,
## PRODUCE} => {PRE PACKED DELI} 0.0105 0.4666667 0.0225 8.562691 21
## [80] {COMM BREAD,
## DSD GROCERY,
## FROZEN FOODS,
## GROCERY DRY GOODS,
## PRODUCE} => {PRE PACKED DELI} 0.0105 0.4666667 0.0225 8.562691 21
## [81] {DSD GROCERY,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0205 0.4659091 0.0440 8.548791 41
## [82] {DSD GROCERY,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {PRE PACKED DELI} 0.0165 0.4647887 0.0355 8.528234 33
## [83] {COMM BREAD,
## DAIRY,
## DSD GROCERY,
## GROCERY DRY GOODS,
## PRODUCE} => {PRE PACKED DELI} 0.0175 0.4605263 0.0380 8.450024 35
## [84] {COMM BREAD,
## FROZEN FOODS,
## PRODUCE} => {PRE PACKED DELI} 0.0110 0.4583333 0.0240 8.409786 22
## [85] {INFANT CONSUMABLE HARDLINES} => {INFANT APPAREL} 0.0120 0.2264151 0.0530 8.385744 24
## [86] {INFANT APPAREL} => {INFANT CONSUMABLE HARDLINES} 0.0120 0.4444444 0.0270 8.385744 24
## [87] {DSD GROCERY,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {PRE PACKED DELI} 0.0130 0.4561404 0.0285 8.369548 26
## [88] {PRODUCE,
## SEAFOOD} => {FROZEN FOODS} 0.0110 0.8461538 0.0130 8.336491 22
## [89] {COMM BREAD,
## GROCERY DRY GOODS,
## PRE PACKED DELI} => {MEAT - FRESH & FROZEN} 0.0130 0.5200000 0.0250 8.320000 26
## [90] {COMM BREAD,
## DAIRY,
## DSD GROCERY,
## PRODUCE} => {MEAT - FRESH & FROZEN} 0.0210 0.5185185 0.0405 8.296296 42
## [91] {DAIRY,
## DSD GROCERY,
## FROZEN FOODS,
## PRODUCE} => {PRE PACKED DELI} 0.0160 0.4507042 0.0355 8.269802 32
## [92] {COMM BREAD,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0135 0.4500000 0.0300 8.256881 27
## [93] {DSD GROCERY,
## GROCERY DRY GOODS,
## PRE PACKED DELI} => {MEAT - FRESH & FROZEN} 0.0170 0.5151515 0.0330 8.242424 34
## [94] {GROCERY DRY GOODS,
## PRE PACKED DELI} => {MEAT - FRESH & FROZEN} 0.0200 0.5128205 0.0390 8.205128 40
## [95] {COMM BREAD,
## DAIRY,
## DSD GROCERY,
## FROZEN FOODS} => {PRE PACKED DELI} 0.0125 0.4464286 0.0280 8.191350 25
## [96] {COMM BREAD,
## FROZEN FOODS,
## GROCERY DRY GOODS,
## PRODUCE} => {MEAT - FRESH & FROZEN} 0.0115 0.5111111 0.0225 8.177778 23
## [97] {COMM BREAD,
## DAIRY,
## DSD GROCERY,
## FROZEN FOODS,
## PRODUCE} => {MEAT - FRESH & FROZEN} 0.0115 0.5111111 0.0225 8.177778 23
## [98] {COMM BREAD,
## DSD GROCERY,
## FROZEN FOODS,
## GROCERY DRY GOODS,
## PRODUCE} => {MEAT - FRESH & FROZEN} 0.0115 0.5111111 0.0225 8.177778 23
## [99] {COMM BREAD,
## DSD GROCERY,
## FROZEN FOODS,
## PRODUCE} => {MEAT - FRESH & FROZEN} 0.0120 0.5106383 0.0235 8.170213 24
## [100] {COMM BREAD,
## DSD GROCERY,
## PRE PACKED DELI} => {MEAT - FRESH & FROZEN} 0.0130 0.5098039 0.0255 8.156863 26ii. Similar to the last task, use the apriori command now to generate about 100 - 200 association rules from the input data. Set your own minimum support and confidence threshold levels. Show the rules in the descending order of their lift values.
rules2 <- apriori(Dept_baskets,
parameter = list(supp = 0.005, conf = 0.05, target = "rules", minlen = 2),
control = list(verbose = FALSE))
# Sorting the rules by lift in descending order
rules_sorted_by_lift2 <- sort(rules2, by = "lift", decreasing = TRUE)
# Inspecting the top rules
inspect(head(rules_sorted_by_lift2, n = 120))## lhs rhs support confidence coverage lift count
## [1] {DSD GROCERY,
## FROZEN FOODS,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {SEAFOOD} 0.0055 0.3437500 0.0160 18.58108 11
## [2] {FROZEN FOODS,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {SEAFOOD} 0.0060 0.3428571 0.0175 18.53282 12
## [3] {DAIRY,
## FROZEN FOODS,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {SEAFOOD} 0.0055 0.3333333 0.0165 18.01802 11
## [4] {DAIRY,
## DSD GROCERY,
## FROZEN FOODS,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {SEAFOOD} 0.0050 0.3333333 0.0150 18.01802 10
## [5] {FROZEN FOODS,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {SEAFOOD} 0.0065 0.3170732 0.0205 17.13909 13
## [6] {DAIRY,
## FROZEN FOODS,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {SEAFOOD} 0.0060 0.3157895 0.0190 17.06970 12
## [7] {DSD GROCERY,
## FROZEN FOODS,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {SEAFOOD} 0.0060 0.3157895 0.0190 17.06970 12
## [8] {DAIRY,
## DSD GROCERY,
## FROZEN FOODS,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {SEAFOOD} 0.0055 0.3142857 0.0175 16.98842 11
## [9] {DSD GROCERY,
## FROZEN FOODS,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN} => {SEAFOOD} 0.0055 0.2972973 0.0185 16.07012 11
## [10] {DAIRY,
## DSD GROCERY,
## FROZEN FOODS,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN} => {SEAFOOD} 0.0050 0.2941176 0.0170 15.89825 10
## [11] {FROZEN FOODS,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN} => {SEAFOOD} 0.0060 0.2926829 0.0205 15.82070 12
## [12] {IMPULSE MERCHANDISE,
## INFANT CONSUMABLE HARDLINES} => {INFANT APPAREL} 0.0070 0.4242424 0.0165 15.71268 14
## [13] {FROZEN FOODS,
## PRE PACKED DELI,
## PRODUCE} => {SEAFOOD} 0.0055 0.2894737 0.0190 15.64723 11
## [14] {DAIRY,
## FROZEN FOODS,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN} => {SEAFOOD} 0.0055 0.2894737 0.0190 15.64723 11
## [15] {DAIRY,
## FROZEN FOODS,
## PRE PACKED DELI,
## PRODUCE} => {SEAFOOD} 0.0050 0.2857143 0.0175 15.44402 10
## [16] {DAIRY,
## DSD GROCERY,
## FROZEN FOODS,
## MEAT - FRESH & FROZEN} => {SEAFOOD} 0.0055 0.2820513 0.0195 15.24602 11
## [17] {DAIRY,
## FROZEN FOODS,
## MEAT - FRESH & FROZEN} => {SEAFOOD} 0.0060 0.2790698 0.0215 15.08485 12
## [18] {DSD GROCERY,
## FROZEN FOODS,
## MEAT - FRESH & FROZEN} => {SEAFOOD} 0.0060 0.2790698 0.0215 15.08485 12
## [19] {FROZEN FOODS,
## MEAT - FRESH & FROZEN} => {SEAFOOD} 0.0065 0.2765957 0.0235 14.95112 13
## [20] {HORTICULTURE AND ACCESS} => {LAWN AND GARDEN} 0.0095 0.4871795 0.0195 14.54267 19
## [21] {LAWN AND GARDEN} => {HORTICULTURE AND ACCESS} 0.0095 0.2835821 0.0335 14.54267 19
## [22] {INFANT CONSUMABLE HARDLINES,
## PERSONAL CARE} => {INFANT APPAREL} 0.0055 0.3666667 0.0150 13.58025 11
## [23] {COMM BREAD,
## DAIRY,
## FROZEN FOODS,
## GROCERY DRY GOODS,
## PRODUCE} => {SEAFOOD} 0.0055 0.2500000 0.0220 13.51351 11
## [24] {COMM BREAD,
## DAIRY,
## DSD GROCERY,
## FROZEN FOODS,
## GROCERY DRY GOODS,
## PRODUCE} => {SEAFOOD} 0.0055 0.2500000 0.0220 13.51351 11
## [25] {COMM BREAD,
## FROZEN FOODS,
## GROCERY DRY GOODS,
## PRODUCE} => {SEAFOOD} 0.0055 0.2444444 0.0225 13.21321 11
## [26] {COMM BREAD,
## DAIRY,
## DSD GROCERY,
## FROZEN FOODS,
## PRODUCE} => {SEAFOOD} 0.0055 0.2444444 0.0225 13.21321 11
## [27] {COMM BREAD,
## DSD GROCERY,
## FROZEN FOODS,
## GROCERY DRY GOODS,
## PRODUCE} => {SEAFOOD} 0.0055 0.2444444 0.0225 13.21321 11
## [28] {DSD GROCERY,
## FROZEN FOODS,
## MEAT - FRESH & FROZEN,
## PERSONAL CARE} => {PRE PACKED DELI} 0.0060 0.7058824 0.0085 12.95197 12
## [29] {COMM BREAD,
## DAIRY,
## FROZEN FOODS,
## PRODUCE} => {SEAFOOD} 0.0055 0.2391304 0.0230 12.92597 11
## [30] {DAIRY,
## FROZEN FOODS,
## PRE PACKED DELI} => {SEAFOOD} 0.0050 0.2380952 0.0210 12.87001 10
## [31] {HOME MANAGEMENT,
## HOUSEHOLD CHEMICALS/SUPP} => {COOK AND DINE} 0.0070 0.5833333 0.0120 12.82051 14
## [32] {FROZEN FOODS,
## GROCERY DRY GOODS,
## PRODUCE,
## SEAFOOD} => {MEAT - FRESH & FROZEN} 0.0060 0.8000000 0.0075 12.80000 12
## [33] {COMM BREAD,
## DSD GROCERY,
## FROZEN FOODS,
## PRODUCE} => {SEAFOOD} 0.0055 0.2340426 0.0235 12.65095 11
## [34] {DAIRY,
## DSD GROCERY,
## FROZEN FOODS,
## MEAT - FRESH & FROZEN,
## PERSONAL CARE} => {PRE PACKED DELI} 0.0055 0.6875000 0.0080 12.61468 11
## [35] {FROZEN FOODS,
## HOUSEHOLD PAPER GOODS,
## PRE PACKED DELI,
## PRODUCE} => {MEAT - FRESH & FROZEN} 0.0055 0.7857143 0.0070 12.57143 11
## [36] {DAIRY,
## FROZEN FOODS,
## GROCERY DRY GOODS,
## PRODUCE,
## SEAFOOD} => {MEAT - FRESH & FROZEN} 0.0055 0.7857143 0.0070 12.57143 11
## [37] {DAIRY,
## DSD GROCERY,
## FROZEN FOODS,
## PRODUCE,
## SEAFOOD} => {MEAT - FRESH & FROZEN} 0.0055 0.7857143 0.0070 12.57143 11
## [38] {DSD GROCERY,
## FROZEN FOODS,
## GROCERY DRY GOODS,
## PRODUCE,
## SEAFOOD} => {MEAT - FRESH & FROZEN} 0.0055 0.7857143 0.0070 12.57143 11
## [39] {DSD GROCERY,
## FROZEN FOODS,
## HOUSEHOLD PAPER GOODS,
## PRE PACKED DELI,
## PRODUCE} => {MEAT - FRESH & FROZEN} 0.0055 0.7857143 0.0070 12.57143 11
## [40] {COMM BREAD,
## FROZEN FOODS,
## PRODUCE} => {SEAFOOD} 0.0055 0.2291667 0.0240 12.38739 11
## [41] {DAIRY,
## FROZEN FOODS,
## HOUSEHOLD PAPER GOODS,
## PRE PACKED DELI,
## PRODUCE} => {MEAT - FRESH & FROZEN} 0.0050 0.7692308 0.0065 12.30769 10
## [42] {DAIRY,
## DSD GROCERY,
## FROZEN FOODS,
## GROCERY DRY GOODS,
## PRODUCE,
## SEAFOOD} => {MEAT - FRESH & FROZEN} 0.0050 0.7692308 0.0065 12.30769 10
## [43] {DAIRY,
## DSD GROCERY,
## FROZEN FOODS,
## HOUSEHOLD PAPER GOODS,
## PRE PACKED DELI,
## PRODUCE} => {MEAT - FRESH & FROZEN} 0.0050 0.7692308 0.0065 12.30769 10
## [44] {CANDY, TOBACCO, COOKIES,
## DAIRY,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0050 0.6666667 0.0075 12.23242 10
## [45] {FROZEN FOODS,
## MEAT - FRESH & FROZEN,
## PERSONAL CARE} => {PRE PACKED DELI} 0.0060 0.6666667 0.0090 12.23242 12
## [46] {CANDY, TOBACCO, COOKIES,
## DAIRY,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0050 0.6666667 0.0075 12.23242 10
## [47] {CANDY, TOBACCO, COOKIES,
## DAIRY,
## DSD GROCERY,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0050 0.6666667 0.0075 12.23242 10
## [48] {CANDY, TOBACCO, COOKIES,
## DSD GROCERY,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0050 0.6666667 0.0075 12.23242 10
## [49] {BAKERY,
## DAIRY,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0050 0.6666667 0.0075 12.23242 10
## [50] {CANDY, TOBACCO, COOKIES,
## DAIRY,
## DSD GROCERY,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0050 0.6666667 0.0075 12.23242 10
## [51] {DSD GROCERY,
## FROZEN FOODS,
## MEAT - FRESH & FROZEN,
## PERSONAL CARE,
## PRODUCE} => {PRE PACKED DELI} 0.0050 0.6666667 0.0075 12.23242 10
## [52] {FROZEN FOODS,
## PRE PACKED DELI} => {SEAFOOD} 0.0055 0.2244898 0.0245 12.13458 11
## [53] {COMM BREAD,
## GROCERY DRY GOODS,
## PERSONAL CARE} => {SEAFOOD} 0.0050 0.2222222 0.0225 12.01201 10
## [54] {GROCERY DRY GOODS,
## PRODUCE,
## SEAFOOD} => {MEAT - FRESH & FROZEN} 0.0060 0.7500000 0.0080 12.00000 12
## [55] {DAIRY,
## FROZEN FOODS,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0140 0.6511628 0.0215 11.94794 28
## [56] {DAIRY,
## FROZEN FOODS,
## MEAT - FRESH & FROZEN,
## PERSONAL CARE} => {PRE PACKED DELI} 0.0055 0.6470588 0.0085 11.87264 11
## [57] {COMM BREAD,
## DAIRY,
## DSD GROCERY,
## FROZEN FOODS,
## GROCERY DRY GOODS} => {SEAFOOD} 0.0060 0.2181818 0.0275 11.79361 12
## [58] {DAIRY,
## DSD GROCERY,
## FROZEN FOODS,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0125 0.6410256 0.0195 11.76194 25
## [59] {DAIRY,
## DSD GROCERY,
## PERSONAL CARE,
## PRODUCE} => {SEAFOOD} 0.0050 0.2173913 0.0230 11.75088 10
## [60] {DAIRY,
## FROZEN FOODS,
## GROCERY DRY GOODS,
## SEAFOOD} => {MEAT - FRESH & FROZEN} 0.0055 0.7333333 0.0075 11.73333 11
## [61] {DAIRY,
## DSD GROCERY,
## FROZEN FOODS,
## SEAFOOD} => {MEAT - FRESH & FROZEN} 0.0055 0.7333333 0.0075 11.73333 11
## [62] {DAIRY,
## GROCERY DRY GOODS,
## PRODUCE,
## SEAFOOD} => {MEAT - FRESH & FROZEN} 0.0055 0.7333333 0.0075 11.73333 11
## [63] {DSD GROCERY,
## GROCERY DRY GOODS,
## PRODUCE,
## SEAFOOD} => {MEAT - FRESH & FROZEN} 0.0055 0.7333333 0.0075 11.73333 11
## [64] {DSD GROCERY,
## FROZEN FOODS,
## HOUSEHOLD PAPER GOODS,
## PRE PACKED DELI} => {MEAT - FRESH & FROZEN} 0.0055 0.7333333 0.0075 11.73333 11
## [65] {FROZEN FOODS,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0150 0.6382979 0.0235 11.71189 30
## [66] {DAIRY,
## FROZEN FOODS,
## PRODUCE} => {SEAFOOD} 0.0090 0.2142857 0.0420 11.58301 18
## [67] {COMM BREAD,
## DAIRY,
## DSD GROCERY,
## FROZEN FOODS} => {SEAFOOD} 0.0060 0.2142857 0.0280 11.58301 12
## [68] {COMM BREAD,
## DAIRY,
## FROZEN FOODS,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0085 0.6296296 0.0135 11.55284 17
## [69] {DSD GROCERY,
## FROZEN FOODS,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0135 0.6279070 0.0215 11.52123 27
## [70] {COMM BREAD,
## DSD GROCERY,
## FROZEN FOODS,
## GROCERY DRY GOODS} => {SEAFOOD} 0.0065 0.2131148 0.0305 11.51972 13
## [71] {GROCERY DRY GOODS,
## PRODUCE,
## SEAFOOD} => {PRE PACKED DELI} 0.0050 0.6250000 0.0080 11.46789 10
## [72] {CANDY, TOBACCO, COOKIES,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0050 0.6250000 0.0080 11.46789 10
## [73] {CANDY, TOBACCO, COOKIES,
## DSD GROCERY,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0050 0.6250000 0.0080 11.46789 10
## [74] {FROZEN FOODS,
## MEAT - FRESH & FROZEN,
## PERSONAL CARE,
## PRODUCE} => {PRE PACKED DELI} 0.0050 0.6250000 0.0080 11.46789 10
## [75] {CANDY, TOBACCO, COOKIES,
## DAIRY,
## PRE PACKED DELI} => {MEAT - FRESH & FROZEN} 0.0050 0.7142857 0.0070 11.42857 10
## [76] {CANDY, TOBACCO, COOKIES,
## DAIRY,
## GROCERY DRY GOODS,
## PRE PACKED DELI} => {MEAT - FRESH & FROZEN} 0.0050 0.7142857 0.0070 11.42857 10
## [77] {CANDY, TOBACCO, COOKIES,
## DAIRY,
## DSD GROCERY,
## PRE PACKED DELI} => {MEAT - FRESH & FROZEN} 0.0050 0.7142857 0.0070 11.42857 10
## [78] {DAIRY,
## FROZEN FOODS,
## HOUSEHOLD PAPER GOODS,
## PRE PACKED DELI} => {MEAT - FRESH & FROZEN} 0.0050 0.7142857 0.0070 11.42857 10
## [79] {FROZEN FOODS,
## PERSONAL CARE,
## PRE PACKED DELI,
## PRODUCE} => {MEAT - FRESH & FROZEN} 0.0050 0.7142857 0.0070 11.42857 10
## [80] {DAIRY,
## DSD GROCERY,
## FROZEN FOODS,
## GROCERY DRY GOODS,
## SEAFOOD} => {MEAT - FRESH & FROZEN} 0.0050 0.7142857 0.0070 11.42857 10
## [81] {DAIRY,
## DSD GROCERY,
## GROCERY DRY GOODS,
## PRODUCE,
## SEAFOOD} => {MEAT - FRESH & FROZEN} 0.0050 0.7142857 0.0070 11.42857 10
## [82] {CANDY, TOBACCO, COOKIES,
## DAIRY,
## DSD GROCERY,
## GROCERY DRY GOODS,
## PRE PACKED DELI} => {MEAT - FRESH & FROZEN} 0.0050 0.7142857 0.0070 11.42857 10
## [83] {DAIRY,
## DSD GROCERY,
## FROZEN FOODS,
## HOUSEHOLD PAPER GOODS,
## PRE PACKED DELI} => {MEAT - FRESH & FROZEN} 0.0050 0.7142857 0.0070 11.42857 10
## [84] {DSD GROCERY,
## FROZEN FOODS,
## PERSONAL CARE,
## PRE PACKED DELI,
## PRODUCE} => {MEAT - FRESH & FROZEN} 0.0050 0.7142857 0.0070 11.42857 10
## [85] {FROZEN FOODS,
## PRODUCE} => {SEAFOOD} 0.0110 0.2095238 0.0525 11.32561 22
## [86] {FROZEN FOODS,
## PERSONAL CARE,
## PRE PACKED DELI} => {MEAT - FRESH & FROZEN} 0.0060 0.7058824 0.0085 11.29412 12
## [87] {DSD GROCERY,
## FROZEN FOODS,
## PRODUCE,
## SEAFOOD} => {MEAT - FRESH & FROZEN} 0.0060 0.7058824 0.0085 11.29412 12
## [88] {DAIRY,
## DSD GROCERY,
## PRODUCE,
## SEAFOOD} => {MEAT - FRESH & FROZEN} 0.0060 0.7058824 0.0085 11.29412 12
## [89] {DSD GROCERY,
## FROZEN FOODS,
## PERSONAL CARE,
## PRE PACKED DELI} => {MEAT - FRESH & FROZEN} 0.0060 0.7058824 0.0085 11.29412 12
## [90] {COMM BREAD,
## DAIRY,
## FROZEN FOODS,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0080 0.6153846 0.0130 11.29146 16
## [91] {COMM BREAD,
## DAIRY,
## DSD GROCERY,
## FROZEN FOODS,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0080 0.6153846 0.0130 11.29146 16
## [92] {FROZEN FOODS,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {PRE PACKED DELI} 0.0125 0.6097561 0.0205 11.18819 25
## [93] {DAIRY,
## FROZEN FOODS,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {PRE PACKED DELI} 0.0115 0.6052632 0.0190 11.10575 23
## [94] {DSD GROCERY,
## FROZEN FOODS,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {PRE PACKED DELI} 0.0115 0.6052632 0.0190 11.10575 23
## [95] {DAIRY,
## FROZEN FOODS,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0115 0.6052632 0.0190 11.10575 23
## [96] {MEAT - FRESH & FROZEN,
## PRE PACKED DELI} => {SEAFOOD} 0.0050 0.2040816 0.0245 11.03144 10
## [97] {DAIRY,
## DSD GROCERY,
## FROZEN FOODS,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {PRE PACKED DELI} 0.0105 0.6000000 0.0175 11.00917 21
## [98] {COMM BREAD,
## DAIRY,
## DSD GROCERY,
## FROZEN FOODS,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0075 0.6000000 0.0125 11.00917 15
## [99] {FROZEN FOODS,
## HOUSEHOLD PAPER GOODS,
## PRE PACKED DELI} => {MEAT - FRESH & FROZEN} 0.0055 0.6875000 0.0080 11.00000 11
## [100] {DAIRY,
## FROZEN FOODS,
## PERSONAL CARE,
## PRE PACKED DELI} => {MEAT - FRESH & FROZEN} 0.0055 0.6875000 0.0080 11.00000 11
## [101] {DAIRY,
## DSD GROCERY,
## FROZEN FOODS,
## PERSONAL CARE,
## PRE PACKED DELI} => {MEAT - FRESH & FROZEN} 0.0055 0.6875000 0.0080 11.00000 11
## [102] {COMM BREAD,
## DAIRY,
## FROZEN FOODS,
## GROCERY DRY GOODS} => {SEAFOOD} 0.0060 0.2033898 0.0295 10.99404 12
## [103] {HOUSEHOLD PAPER GOODS,
## PRE PACKED DELI,
## PRODUCE} => {MEAT - FRESH & FROZEN} 0.0065 0.6842105 0.0095 10.94737 13
## [104] {DAIRY,
## FROZEN FOODS,
## HOUSEHOLD CHEMICALS/SUPP,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {HOUSEHOLD PAPER GOODS} 0.0050 1.0000000 0.0050 10.86957 10
## [105] {FROZEN FOODS,
## GROCERY DRY GOODS,
## HOUSEHOLD CHEMICALS/SUPP,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {HOUSEHOLD PAPER GOODS} 0.0050 1.0000000 0.0050 10.86957 10
## [106] {DAIRY,
## DSD GROCERY,
## FROZEN FOODS,
## HOUSEHOLD CHEMICALS/SUPP,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {HOUSEHOLD PAPER GOODS} 0.0050 1.0000000 0.0050 10.86957 10
## [107] {DSD GROCERY,
## FROZEN FOODS,
## GROCERY DRY GOODS,
## HOUSEHOLD CHEMICALS/SUPP,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {HOUSEHOLD PAPER GOODS} 0.0050 1.0000000 0.0050 10.86957 10
## [108] {COMM BREAD,
## DSD GROCERY,
## FROZEN FOODS} => {SEAFOOD} 0.0065 0.2000000 0.0325 10.81081 13
## [109] {FROZEN FOODS,
## GROCERY DRY GOODS,
## PRODUCE} => {SEAFOOD} 0.0075 0.2000000 0.0375 10.81081 15
## [110] {DAIRY,
## DSD GROCERY,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {SEAFOOD} 0.0060 0.2000000 0.0300 10.81081 12
## [111] {DAIRY,
## FROZEN FOODS,
## GROCERY DRY GOODS,
## PRODUCE} => {SEAFOOD} 0.0070 0.2000000 0.0350 10.81081 14
## [112] {DAIRY,
## DSD GROCERY,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {SEAFOOD} 0.0050 0.2000000 0.0250 10.81081 10
## [113] {MEAT - FRESH & FROZEN,
## SEAFOOD} => {PRE PACKED DELI} 0.0050 0.5882353 0.0085 10.79331 10
## [114] {CANDY, TOBACCO, COOKIES,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0050 0.5882353 0.0085 10.79331 10
## [115] {BAKERY,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0050 0.5882353 0.0085 10.79331 10
## [116] {DAIRY,
## FROZEN FOODS,
## HOUSEHOLD PAPER GOODS,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0050 0.5882353 0.0085 10.79331 10
## [117] {DAIRY,
## FROZEN FOODS,
## HOUSEHOLD PAPER GOODS,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {PRE PACKED DELI} 0.0050 0.5882353 0.0085 10.79331 10
## [118] {DAIRY,
## DSD GROCERY,
## FROZEN FOODS,
## HOUSEHOLD PAPER GOODS,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0050 0.5882353 0.0085 10.79331 10
## [119] {DAIRY,
## DSD GROCERY,
## FROZEN FOODS,
## GROCERY DRY GOODS,
## MEAT - FRESH & FROZEN} => {PRE PACKED DELI} 0.0100 0.5882353 0.0170 10.79331 20
## [120] {DAIRY,
## DSD GROCERY,
## FROZEN FOODS,
## HOUSEHOLD PAPER GOODS,
## MEAT - FRESH & FROZEN,
## PRODUCE} => {PRE PACKED DELI} 0.0050 0.5882353 0.0085 10.79331 10Task II Reflections
1. What were the minimum support level and the minimum confidence level you selected for the Association Rule Mining tasks? Given these levels, What is the rule with the highest lift given your final choices of these levels? What is the rule with the highest support level? What is the rule with the highest confidence level? Which rule out of these three (or fewer) do you recommend for sales executives to consider? What is the reason for your recommendation?
In the analysis of association rules generated from the dataset using the apriori algorithm, several key metrics were considered: lift, support, and confidence. For the initial set of rules, the minimum support was set at 0.005 (.5%) and the minimum confidence at 0.05 (5%). The rule that stood out the most was (dsd grocery, frozen foods, grocery dry goods, meat - fresh & frozen, produce => Seafood, which exhibited the highest lift value of 18.58108. This indicates the strength of an association relative to the frequency of the itemsets occurring independently. Therefore, this is suggesting that there is a strong relationship between the purchased items and seafood.
Based on these findings, the rule with the highest lift is particularly recommended for sales executives to consider for implementation. This rule not only signifies a strong association but also spans across multiple departments, suggesting a broad impact on customer purchasing behavior. By strategically placing seafood promotions or displays near departments such as dsd grocery, frozen foods, and grocery dry goods, stores might enhance the visibility and likelihood of seafood purchases. This approach leverages the natural shopping patterns observed in the data, potentially leading to increased sales and better customer satisfaction by aligning store layouts and promotions with actual consumer behavior.
3. If you were explaining the results of these models to a supervisor what would you say about them? Attempt to do more than just state facts here, interpret the results. Coding is great, interpretation of output is even more important. Discuss each model. Write at least 150 words
I would focus on interpreting the patterns and insights that can drive actionable business decisions.
For the clustering analysis, the initial model segregated customers into clusters based on the unique trip types, revealing distinct patterns in shopping behavior by day of the week. For instance, Fridays and weekends showed significant customer activity, which suggests these days are prime targets for strategic marketing campaigns or promotions to boost sales. Refining the model with different initialization methods and distance metrics provided clearer segmentation, enhancing our understanding of customer preferences and behaviors on specific days.
The association rule mining illuminated purchasing behaviors, uncovering strong relationships between certain categories of items. Notably, the high lift value found between a set of grocery categories and Seafood suggests a cross-selling opportunity. This will inform my supervisor what to pair withg seafood in terms of promotion.
Overall, these models don’t just segment our customer base or predict purchasing patterns; they provide strategic insights that can be leveraged to enhance marketing effectiveness, optimize store layout, and ultimately, drive revenue growth by aligning sales strategies with actual customer behavior.