Customer churn is a common problem across businesses in many sectors. If you want to grow as a company, you have to invest in acquiring new clients. Every time a client leaves, it represents a significant investment lost. Both time and effort need to be channelled into replacing them. Being able to predict when a client is likely to leave, and offer them incentives to stay, can offer huge savings to a business.
1.Building a churn prediction model for the DTH company to identify and control subscriber churn. To build such a model most important step would be to identify important variables/factors from provided dataset which are influencing customer churn. 2.Then develop Machine Learning model using these variables and evaluate their accuracy and performance. 3.Finally, find the best model having best scores and provide business insights and recommendations to the DTH company
- Numpy
- Pandas
- Exploratory Data Analysis
- Supervised Machine learning algortihms
- Ensemble technique