Revision history [back]

The process of preparing test and train data for a logistic regression model in Python that can predict the "next X days" can be divided into the following steps:

1. Import the necessary libraries - Pandas, NumPy, and Sklearn.
2. Load the dataset into a Pandas dataframe and clean/preprocess the data.
3. Split the dataset into two parts: the training set and the test set.
4. Identify the independent variables (features) and the dependent variable (target).
5. Select the features that are relevant to the prediction task.
6. Scale the feature values using standardization or normalization.
7. Split the training set into subsets for training and validation.
8. Train the logistic regression model on the training data using sklearn.
9. Evaluate the performance of the model on the validation data.
10. Use the trained model to predict the "next X days" on the test set.
11. Evaluate the performance of the model on the test data.
12. Fine-tune the model hyperparameters to improve performance.
13. Deploy the model to predict future outcomes.