I want to split dataset into train and test data. But I want to split that as rows. For example: I have a dataset of 100 rows. For splitting, I want to train first 90 rows and next 10 rows for test data. How can I be able to do that in Python? I want to do it for prediction in a regression type dataset.
If the partition is fixed, then you can use this code
- train_pct_index = int(0.9 * len(X))
- X_train, X_test = X[:train_pct_index], X[train_pct_index:]
- y_train, y_test = y[:train_pct_index], y[train_pct_index:]
If you want to split the dataset in fixed manner i.e. 1st 90 rows for training then just use python's slicing method.
If you want to split the dataset randomly, use scikit-learn's train_test_split.
Follow this link.
https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html.
I don't want to split randomly and I have said that in question. Please describe the slicing method. Md_Monirul_Islam
Try to use KNIME as KNIME has also python, R, Pearl and Java nodes.
Install KNIME and than use the pipeline that I attach. left click on partition and you can decide how much partition you need to arrange but I would suggest you to use %60 versus %40. The file reader is CSV reader so if your original file different you can change the file reader with other community reader nodes.
If the partition is fixed, then you can use this code
- train_pct_index = int(0.9 * len(X))
- X_train, X_test = X[:train_pct_index], X[train_pct_index:]
- y_train, y_test = y[:train_pct_index], y[train_pct_index:]
You can follow the attached link to know more about slicing. https://www.tutorialspoint.com/numpy/numpy_indexing_and_slicing.htm
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