customer-churn-prediction-keras-

Using deep learning to predict customer churn. source: http://www.business-science.io/business/2017/11/28/customer_churn_analysis_keras.html

Background Analysis

Machine Learning on Customer Churn From what we learned using ML on customer churn using telecom dataset.

1. Logistic regression and random forest performed better than decision tree for customer churn
2. Features such as tenure group, contract, paperless billing, monthly charges, internet service appear to play a role in customer churn based on running logistic regression model on data
3. There is no relationship between gender and churn
4. Customers in a month to month contract, with paperless billing and are within 12 months tenure, are more likely to churn; on the other hand, customers with one or two year contract, with longer than 12 months tenure, that are not using paperless billing are less likely to churn

• dataset: https://www.ibm.com/communities/analytics/watson-analytics-blog/guide-to-sample-datasets/

• source: https://datascienceplus.com/predict-customer-churn-logistic-regression-decision-tree-and-random-forest/

• source code for analysis: https://github.com/susanli2016/Data-Analysis-with-R/blob/master/customer_churn.Rmd

Devs Section

1. install R on your jupyter notebook follow these steps: https://discuss.analyticsvidhya.com/t/how-to-run-r-on-jupyter-ipython-notebooks/5512/2