Standardization and Normalization are not mandatory approaches and can be avoided in some cases, but some models like neural networks, SVM, etc. require the data to either be standardized or normalized. You can try running the model with and without standardization or normalization and evaluate the performance. If performance improves with standardization or normalization then keep it, if performance declines then remove it
Articles in this section
- Dropping Columns in a Pandas DataFrame
- Installing Pandas-Profiling in the Local System
- How to convert textual numbers to numeric numbers?
- How to open rar files or zip files on local system?
- Mounting Google Drive to Google Collaboratory Notebooks
- Binning data in Pandas
- Reading Python Notebook in Local Machine
- Pearson Correlation and Cosine Similarity
- Re-installation of Anaconda Distribution (using Admin rights)
- How to display a specific number of rows in a pandas dataframe?