Chapter 13 Recommended Notebooks for 2021 June to 2021 December

We recommend the following Notebooks created between 2021 June to 2021 December [ This is chosen to reduce the dataset analysis purposes only ]

We choose the following criteria

• Medals - Silver

• We chose a Kernel which is NOT a Competition Notebook

• Performance Tier of the author is Expert or Master

• We chose Kernels whose Total Votes greater than 40, Total Comments greater than 10 and the Number of views is more than 3100

• We removed Kernels which had common data sources such as Titanic, Breast Cancer , Heart and Diabetes

kernels$MadePublicDate = as.Date(kernels$MadePublicDate,format = "%m/%d/%Y")

kernels_subset = kernels %>% 
  filter(between(MadePublicDate, as.Date("2021-06-01"),as.Date("2021-12-31")))

kernels_subset = kernels_subset %>%
  filter(TotalVotes > 40)

kernels_subset = kernels_subset %>%
  filter(TotalComments > 10)

kernels_subset = kernels_subset %>%
  filter(TotalViews > 3100)

kernels_subset$Medal = as.integer(kernels_subset$Medal)

kernels_subset_silver = kernels_subset %>%
  filter(Medal >= 2)

kvcs_silver <- kernels_subset_silver %>%
  left_join(kernel_version_competition ,  
            by = c("CurrentKernelVersionId" = "KernelVersionId"))

kvcs_silver = kvcs_silver %>%
  filter(is.na(SourceCompetitionId))

kvcs_silver = kvcs_silver %>%
  mutate(CompNoteBook = ifelse(is.na(SourceCompetitionId),0,1))

kvcs_silver_users = kvcs_silver %>% 
  left_join(users %>% select(AuthorUserId = Id, 
                             author_kaggle = UserName,
                             DisplayName,
                             RegisterDate,
                             PerformanceTier), by = "AuthorUserId")

kvcs_silver_users_experts = kvcs_silver_users %>%
  filter(PerformanceTier %in%  c(2,3))

kvcs_silver_users_experts = kvcs_silver_users_experts %>%
  filter(!str_detect(CurrentUrlSlug, c("titanic") ))

kvcs_silver_users_experts = kvcs_silver_users_experts %>%
  filter(!str_detect(CurrentUrlSlug, c("diabetes") ))

kvcs_silver_users_experts = kvcs_silver_users_experts %>%
  filter(!str_detect(CurrentUrlSlug, c("house") ))

kvcs_silver_users_experts = kvcs_silver_users_experts %>%
  filter(!str_detect(CurrentUrlSlug, c("heart") ))

kvcs_silver_users_experts = kvcs_silver_users_experts %>%
  filter(!str_detect(CurrentUrlSlug, c("breast") ))


kvcs_silver_users_experts = kvcs_silver_users_experts %>%
  mutate( URL = paste("https://www.kaggle.com/code/",author_kaggle,"/",CurrentUrlSlug,sep =""))

kvcs_versions_info_reduced = kvcs_silver_users_experts %>%
  select("URL","Medal",
         "TotalViews","TotalComments","TotalVotes",
  ) %>%
  arrange(desc(TotalVotes))



kvcs_versions_info_reduced %>%
  gt() %>%
  tab_header(
    title = "Recommended Notebooks for 2021 June to December")

Recommended Notebooks for 2021 June to December
URL	Medal	TotalViews	TotalComments	TotalVotes
https://www.kaggle.com/code/ankitkalauni/tokyo-olympic-2021-starter-clean-eda	2	4747	48	96
https://www.kaggle.com/code/sonalisingh1411/eda-on-train-test-dataset-price-prediction	2	4471	38	86
https://www.kaggle.com/code/imakash3011/customer-analysis-eda-report-clustering	2	6530	46	77
https://www.kaggle.com/code/mysarahmadbhat/types-of-transformations-for-better-distribution	2	3551	64	73
https://www.kaggle.com/code/miguelfzzz/store-customers-clustering-analysis	2	4338	22	72
https://www.kaggle.com/code/imakash3011/covid-19-india-eda-visualization-report	2	3414	60	71
https://www.kaggle.com/code/mysarahmadbhat/python-from-zero-to	2	5180	36	71
https://www.kaggle.com/code/kaanboke/beginner-friendly-end-to-end-ml-project-enjoy	2	3211	24	69
https://www.kaggle.com/code/jonaspalucibarbosa/chest-x-ray-pneumonia-cnn-transfer-learning	2	5180	27	66
https://www.kaggle.com/code/kartik2khandelwal/bitcoin-crash-prediction	2	3565	49	66
https://www.kaggle.com/code/miguelfzzz/olympics-tokyo-2020-cool-eda	2	4085	39	65
https://www.kaggle.com/code/maricinnamon/store-sales-time-series-forecast-visualization	2	4671	33	65
https://www.kaggle.com/code/kslarwtf/eda-clustering-updated	2	4404	45	64
https://www.kaggle.com/code/miguelfzzz/bellabeat-data-analysis-discovering-trends	2	3110	12	63
https://www.kaggle.com/code/ankitkalauni/covid-19-india-statewise-clean-eda-deaths-pred	2	3308	49	62
https://www.kaggle.com/code/gaganmaahi224/eda-detailed-explanation-of-knn-algorithm	2	3351	39	62
https://www.kaggle.com/code/yuyougnchan/look-at-this-note-numeric-variable-is-easy	2	3662	42	60
https://www.kaggle.com/code/zwartfreak/easiest-price-prediction-full-explanation	2	4773	36	59
https://www.kaggle.com/code/thomaskonstantin/exploring-and-predicting-drinking-water-potability	2	4285	39	58
https://www.kaggle.com/code/victoriamiller19/hypothesis-testing-explanation	2	3107	27	58
https://www.kaggle.com/code/vardhansiramdasu/summer-olympics-eda	2	3364	41	58
https://www.kaggle.com/code/mostafaalaa123/customer-personality	2	4615	22	57
https://www.kaggle.com/code/ludovicocuoghi/twitter-sentiment-analysis-with-bert-roberta	2	3346	39	57
https://www.kaggle.com/code/aryantiwari123/hotel-booking-eda-models	2	4616	56	56
https://www.kaggle.com/code/tensorchoko/g-research-crypto-forecasting-eda	2	3979	18	56
https://www.kaggle.com/code/frankmollard/a-story-about-unsupervised-learning	2	5311	14	53
https://www.kaggle.com/code/aditimulye/adult-income-dataset-from-scratch	2	3215	25	52
https://www.kaggle.com/code/mostafaalaa123/finished-quick-analysis-of-each-q	2	5383	38	51
https://www.kaggle.com/code/anandhuh/image-classification-using-cnn-for-beginners	2	5234	24	50
https://www.kaggle.com/code/frankmollard/nlp-a-gentle-introduction-lstm-word2vec-bert	2	3633	30	50
https://www.kaggle.com/code/ankitkalauni/customer-personality-clean-eda-k-means	2	3348	25	50
https://www.kaggle.com/code/prena0808/tokyo-olympics-data-analysis	3	3331	20	50
https://www.kaggle.com/code/paulrohan2020/ml-algorithms-from-scratch-with-pure-python	2	3717	37	48
https://www.kaggle.com/code/rankirsh/predicting-attrition-from-a-to-z	2	3895	33	47
https://www.kaggle.com/code/atasaygin/hotel-booking-demand-eda-and-of-guest-prediction	2	3213	20	46
https://www.kaggle.com/code/hijest/text-generation-for-beginners-thorough-tutorial	2	6054	11	45
https://www.kaggle.com/code/aryantiwari123/handwriting-recognition-deep-learning-tensorflow	2	3349	32	44
https://www.kaggle.com/code/imakash3011/water-quality-prediction-7-model	2	4013	45	43
https://www.kaggle.com/code/yogidsba/personal-loan-logistic-regression-decision-tree	2	7634	24	42
https://www.kaggle.com/code/jonaspalucibarbosa/default-of-credit-card-eda-catboost-w-ft-eng	2	3164	26	42
https://www.kaggle.com/code/anoopashware/food-demand-forecasting-predict-orders	2	3642	14	41