customer-satisfaction-model

An airline satisfaction prediction project built with Lightgbm and streamlit

Power of LightGBM

LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages:

• Faster training speed and higher efficiency.

• Lower memory usage.

• Better accuracy.

• Support of parallel, distributed, and GPU learning.

• Capable of handling large-scale data.

Tree based learning algorithms plays a big role on binary classification projects. The LightGBM differs other tree based algorithms on growing horizontally meaning that Light GBM grows tree leaf-wise while other algorithm grows level-wise. It will choose the leaf with max delta loss to grow. When growing the same leaf, Leaf-wise algorithm can reduce more loss than a level-wise algorithm.

Resources

• https://medium.com/@pushkarmandot/https-medium-com-pushkarmandot-what-is-lightgbm-how-to-implement-it-how-to-fine-tune-the-parameters-60347819b7fc
• https://lightgbm.readthedocs.io/en/v3.3.2/