That is problem commonly called time series anomaly detection / outlier detection. Most systems start with combinations of static and dynamic thresholding. Dynamic thresholding for example can use percentile value as the threshold. One common algorithm is Isolation Forest where the features can including different length moving average windows.
One of the ...
Sklearn's train_test_split only permits with same no of rows for X and Y.
In your case
Y shape is (17257 , 1 ) and X shape is (28762, 19)
All you have to do is reshape X and Y to both have same no of rows(observations)
Reshape X to be (17257,19)
Reshape Y to be (28762,1)
This issue is caused by the fact that the number of observations in your x and y variables are not the same. As you can see, in your x variable (which is the same as ohe_data) you have 28762 observations whereas your y variable only has 17257 observations. Since we don't see the code before that we can't say what is causing this difference.
Your data is multidimensional, it is possible that any two dimensional projection overlaps while still existing an hyperplane on the original dimensionality that separates the two classes well
Say for instance you have 3 data points from 2 labels in 2d that are linearly separable
X:(0,-1) O:(1,2) X:(4,3)
In the x axis they look ...
Usually you should develop multiple models simultaneously. As the No Free Lunch Theorem states there is no way to know which model will perform better, before modeling. In practice you can make some educated guesses, but there is no need to rush them.
If your output is continuous you shouldn't use a classification model like logistic regression. Although the ...
Decision Tree, KNN, & Random Forest (Methods that are suitable for overlapping data)
This statement is false. All those methods are good when the decision surface (separating surface) has a highly nonlinear form. They act as a non-parametric local approximation - all parameters are not in fact parameters of the decision function but are meta parameters ...