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https://github.com/chaitanyac22/telecom-churn-prediction
In this project, data analytics is used to analyze customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn, and identify the main indicators of churn. The project focuses on a four-month window, wherein the first two months are the ‘good’ phase, the third month is the ‘action’ phase, while the fourth month is the ‘churn’ phase. The business objective is to predict the churn in the last i.e. fourth month using the data from the first three months.
https://github.com/chaitanyac22/telecom-churn-prediction
class-imbalance classification data-analytics data-cleaning data-manipulation evaluation-metrics feature-engineering hyperparameter-tuning logistic-regression machine-learning model-building model-evaluation over-sampling pca random-forest-classifier rfe smote statistics telecom xgboost
Last synced: 13 days ago
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In this project, data analytics is used to analyze customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn, and identify the main indicators of churn. The project focuses on a four-month window, wherein the first two months are the ‘good’ phase, the third month is the ‘action’ phase, while the fourth month is the ‘churn’ phase. The business objective is to predict the churn in the last i.e. fourth month using the data from the first three months.
- Host: GitHub
- URL: https://github.com/chaitanyac22/telecom-churn-prediction
- Owner: ChaitanyaC22
- License: mit
- Created: 2021-03-11T13:10:58.000Z (almost 4 years ago)
- Default Branch: chai_main
- Last Pushed: 2021-07-09T18:30:11.000Z (over 3 years ago)
- Last Synced: 2023-04-24T16:37:09.691Z (almost 2 years ago)
- Topics: class-imbalance, classification, data-analytics, data-cleaning, data-manipulation, evaluation-metrics, feature-engineering, hyperparameter-tuning, logistic-regression, machine-learning, model-building, model-evaluation, over-sampling, pca, random-forest-classifier, rfe, smote, statistics, telecom, xgboost
- Language: Jupyter Notebook
- Homepage:
- Size: 27.7 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0