Can you keep the user?

Source: Udacity

Introduction

Defining churn

The approach

Analysis of the given variables

user traffic schema
Distribution of variables for active vs churned users
pairwise Pearson correlation of variables
Distribution of variables for active vs churned users
pairwise Pearson correlation of variables
Distribution of variables for active vs churned users
pairwise Pearson correlation of variables

Feature selection

Model Training

Grid Search parameters

After fine tuning the logistic regression model, the evaluation score was improved and the model was able to predict churn for 6 out of 8 users that cancelled their account while it hasn’t predicted churn for any active user. The results are impressive, considering the size of the dataset.

Churned users in test dataset — predictions versus label
Active users in test dataset — predictions versus label

Conclusion

Next steps

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