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If I understand your question correctly, you have two groups of people: Group A, each of whom has purchased the product, say yogurt; and Group B, each of whom has not purchased yogurt. Your problem at hand is to find all people in Group B who will be likely to purchase your yogurt, if they have a similar profile as people in Group A. This seems to be a very ...

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Change your url to: url <- "https://www.amazon.in/Apple-iPhone-11-128GB-Black/product-reviews/B07XVLW7YK/ref=cm_cr_arp_d_paging_btm_next_2?ie=UTF8&reviewerType=all_reviews&pageNumber" Otherwise, as is what your current code is doing, you keep looping only the first page of reviews.

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You can forecast using spatio-temporal data by combining Graph Convolution Networks with LSTM models! The idea comes from a paper by Zhao et al. called "T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction" (https://ieeexplore.ieee.org/document/8809901), and is implemented in the StellarGraph module in Python (https://stellargraph....

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What you can do is creating another dataset with the mean and mean±stdev grouped by Tier. Then you can use that in geom_hline and ggplot will pick up facets from Tier. See below; library(dplyr) library(tidyr) library(ggplot2) Interactions %>% group_by(Tier) %>% summarise(mean = mean(TotalInteractions), mean-stdev = sum(mean(...

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Personally I like plyr: library(plyr) dataT <- data.frame(jage=trunc(rnorm(n = 100,mean = 40,sd = 15)), score =rnorm(100, 10, 3)) byAgeGroup <- ddply(dataT, 'jage', function(df) { data.frame( median=median(df$score), avg=mean(df$score), size=nrow(df) ) } ) If the goal is only to plot the median score by age, you could just use ...

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Extra-random Trees needs a target variable, so Isolation Forest generates a random target (sklearn, solitude). At prediction time, no y values are used, and the ExtraTrees doesn't actually make a prediction; instead, the samples are propagated to the leaves and the depth is extracted (sklearn). As for the tree-building process, sklearn at least doesn't make ...

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