The code master_df.drop(["Film Number"], axis=1, inplace=True) you have written is right. What is happening is like you have removed the column perfectly but while converting to csv file or excel file the index column (whatever column you have mentioned with values like 0,1,2,3) get added in the output so please replace one more argument index=...
If I understood correctly
You have data like this
So in fact you actually have many 0 labels, more than 1 labels, which are all the products a client didn't buy
If you want to predict the next product a client will ...
A great web site with plenty of artistic projects to get inspirational ideas is Experiments with Google:
You can also access their github to make your own experiences.
Then, here is an article about 8 artistic projects:
This task could be treated as a one-class classification problem, where a binary classifier is trained only with positive instances. Instead of determining the optimal limit between classes, the model tries to determine what characterizes the positive class, considering everything else as negative.
Be careful with feature selection! Do not rely solely on feature selection techniques. They might be misleading sometimes. Here is the process I usually follow:
1.) The very first thing to do is build a baseline model where you consider all the
features and record the performance. This will give you a baseline score to
compare with. (Do not perform ...
Docker is a solution for computer programmers that makes the installation process easier. Its basic premise is that while it functions on this machine, it should work for those as well. From the other user's system, the same requirements and library versions are being used as on yours. Docker makes use of (reusable) tiers. Rather than running apt-get install,...
Just updating the other answers.
You could use this site:
to make it a dataframe:
points = pd.read_csv('/content/pontos.csv', sep='\n', delimiter=',', names=['x','y','class'])
Detailed information on the dataset is available in the "Data Description" text. Each number is indeed a separate case or patient if you will. Think of the numbers given as the patient ID numbers. So patients numbered 0, 2, 3, 5 etc are in the training dataset, patients numbered 1, 13, 15, 27 etc are in the public test - or validation dataset. ...