Dharun235 commented 2 weeks ago

Description

This project utilizes a Random Forest Classifier for binary classification, which is effective for structured data and allows us to evaluate feature importance. Random Forest is suitable for this problem because it handles various customer data types (e.g., demographics, account details, and service usage) and helps identify key factors contributing to churn. The model provides a clear, interpretable outcome, highlighting the primary attributes associated with churn behavior.

Issue Resolved

205

Changes Made

Added the main.py, dataset.csv and readme file

Screenshots or Videos

Additional Details

Checklist

[x] My code follows the current project structure
[x] I have thoroughly reviewed and updated the requirements.txt file to include any new packages
[x] The predict.py file includes a properly implemented model_details() function, or the notebook contains this function to print a detailed model report. The model will not be accepted without this function, as it is essential for generating the necessary model details.
[x] I have added relevant tests (if necessary).
[x] I have added comments in the code where needed.
[x] This PR is submitted under Hacktoberfest.
[x] This PR is submitted under GirlScript Summer of Code (GSSoC-Extd).

Absk024 commented 2 weeks ago

i would like to contribute @yashasvini121