https://github.com/sai-123-code/jobathon_mar2022

Churn predictions
https://github.com/sai-123-code/jobathon_mar2022

churn-prediction classification data-science f1-score hackthon jobathon machine-learning xgboost

Last synced: about 1 month ago
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Churn predictions

• Host: GitHub
• URL: https://github.com/sai-123-code/jobathon_mar2022
• Owner: sai-123-code
• License: gpl-3.0
• Created: 2022-03-14T12:31:36.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2022-03-14T12:51:17.000Z (almost 3 years ago)
• Last Synced: 2023-12-05T04:29:57.160Z (about 1 year ago)
• Topics: churn-prediction, classification, data-science, f1-score, hackthon, jobathon, machine-learning, xgboost
• Language: Jupyter Notebook
• Homepage:
• Size: 349 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# JOBATHON_MAR2022

## Problem Statement

Decreasing the Customer Churn is a key goal for any business. Predicting Customer Churn (also known as Customer Attrition) represents an additional potential revenue source for any business. Customer Churn impacts the cost to the business. Higher Customer Churn leads to a loss in revenue and the additional marketing costs involved with replacing those customers with new ones.

In this challenge, as a data scientist of a bank, you are asked to analyze the past data and predict whether the customer will churn or not in the next 6 months. This would help the bank to have the right engagement with customers at the right time. Objective

## Objective

Our objective is to build a machine learning model to predict whether the customer will churn or not in the next six months.

## Data Dictionary

You are provided with 3 files - train.csv, test.csv and sample_submission.csv

## Submission File Format

sample_submission.csv contains only 2 variables - id and Is_churn

## Evaluation metric

The evaluation metric for this hackathon is Macro F1-score.