I am trying to perform some preprocessing on my data for a sales prediction. i used make_column_selector to select specific columns in order to apply different encoders to different column. i was trying to create a make selector column object to access column in the feature variable X. it works well for the numerical columns but the category column are the ones giving issues. averytime i use the selector object to select column in the data for the category, i get “TypeError: unhashable type: ‘list'”

I know my question sounds absurd but I dont know how else to put it, I want to drop rows with outliers in two different columns and some of the outliers are present in both columns so after I drop them in the first column it drops them fine but when I try to drop them in the second column I get error.

function passing in features argument
this is predictive analytics NYC Taxi ride problem.
I have been doing a Data Science/ML course throught Great Learning. I have come across a function problem where I have defined the the argument previously but it is returning an error saying not defined. Any assistance is much appreciated.

I was working on a datascience project got stuck

Accroding to you guys, which one is best models to predict the engagment post in social medials like facebook for exemple.

At the moment i’m developing a machine learning project. Its a supervised learning problem. I have as input horse_data(size,weight,peformance,…) and as a output the ingredient of the reciepe. Now i want to predict for a given horsedata the ingredients.

I am trying to model nitrate concentrations in the streams in Bavaria in Germany using Random Forest model. I am using Python and primarily sklearn for the same. I have data from 490 water quality stations. I am following the methodology in the paper from LongzhuQ.Shen et al which can be found here: https://www.nature.com/articles/s41597-020-0478-7

To implement a Random forest for classification, you can use the DecisionTreeClassifier from sklearn as base trees. Data to check using load_breast_cancer, constructor parameters n_estimators – number of trees max_features – percentage of features (columns), for training each tree max_samples – percentage of samples (rows), for training each tree max_depth – depth of the tree […]