Telegram Group & Telegram Channel
πŸš€ Model Comparison for Loan Classification

4 years ago, I built and compared several classification models to predict loan applicants as Creditworthy or Non-Creditworthy. After performing data cleansing, handling missing values, and tuning parameters, I evaluated the models using precision, recall, and F1-score.

πŸ” The Random Forest Classifier stood out with an AUC of 80% and an accuracy of 79%, successfully classifying 418 loans as Creditworthy and 82 as Non-Creditworthy.

Looking back, it's been a great learning experience, and I encourage exploring different tuning parameters and cross-validation techniques to improve model performance even further.
Check out the full source code on GitHub! πŸ’»
https://medium.com/@epythonlab/best-practices-of-classification-models-towards-predicting-loan-type-c510d9b0dff6



group-telegram.com/epythonlab/1972
Create:
Last Update:

πŸš€ Model Comparison for Loan Classification

4 years ago, I built and compared several classification models to predict loan applicants as Creditworthy or Non-Creditworthy. After performing data cleansing, handling missing values, and tuning parameters, I evaluated the models using precision, recall, and F1-score.

πŸ” The Random Forest Classifier stood out with an AUC of 80% and an accuracy of 79%, successfully classifying 418 loans as Creditworthy and 82 as Non-Creditworthy.

Looking back, it's been a great learning experience, and I encourage exploring different tuning parameters and cross-validation techniques to improve model performance even further.
Check out the full source code on GitHub! πŸ’»
https://medium.com/@epythonlab/best-practices-of-classification-models-towards-predicting-loan-type-c510d9b0dff6

BY Epython Lab




Share with your friend now:
group-telegram.com/epythonlab/1972

View MORE
Open in Telegram


Telegram | DID YOU KNOW?

Date: |

In the past, it was noticed that through bulk SMSes, investors were induced to invest in or purchase the stocks of certain listed companies. Channels are not fully encrypted, end-to-end. All communications on a Telegram channel can be seen by anyone on the channel and are also visible to Telegram. Telegram may be asked by a government to hand over the communications from a channel. Telegram has a history of standing up to Russian government requests for data, but how comfortable you are relying on that history to predict future behavior is up to you. Because Telegram has this data, it may also be stolen by hackers or leaked by an internal employee. Telegram users are able to send files of any type up to 2GB each and access them from any device, with no limit on cloud storage, which has made downloading files more popular on the platform. Sebi said data, emails and other documents are being retrieved from the seized devices and detailed investigation is in progress. As a result, the pandemic saw many newcomers to Telegram, including prominent anti-vaccine activists who used the app's hands-off approach to share false information on shots, a study from the Institute for Strategic Dialogue shows.
from tr


Telegram Epython Lab
FROM American