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# System prepended metadata

title: Subscription Prediction

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# Subscription Prediction 

>Main compiler: Cynthia

>Motivation: gain valuable insights for subscription prediction

>Research note:
>

[2020-03-19]
* prediction of transactions (remove ISP and auto-subscription users)
* xgboost model
    * data preprocess: shift variables according to their correlation
    * period A (12 weeks)
    * ![](https://i.imgur.com/PumaXLA.png)
    * period B (12 weeks)
    * ![](https://i.imgur.com/kfatw3c.png)
* pearson correlation with transactions (remove ISP and auto-subscription users) (2010~2019)
![](https://i.imgur.com/noimlCQ.png)
* pearson correlation with transactions (remove ISP and auto-subscription users) (2017~2019)
![](https://i.imgur.com/RUed8YK.png)
* pearson correlation with transactions (remove ISP and auto-subscription users) (2019~)
![](https://i.imgur.com/7bM6RKP.png)

[2020-02-27]
* total transactions is seasonal time series
* split into user type: old / new user
    * discovery: old -> seasonal, new -> irregular
* split into autorenew / non-autorenew
    * discovery: autorenew -> seasonal, non-autorenew or first transaction of autorenew -> irregular
    * autorenew:
        * ![](https://gitlab.kkinternal.com/researchcenter/rdc-image/raw/master/ds/cynthiayang/trans_renew.png)
    * non-autorenew or first transaction of autorenew:
        * ![](https://gitlab.kkinternal.com/researchcenter/rdc-image/raw/master/ds/cynthiayang/trans_nonrenew.png)



[2020-02-20]
* motivation:
    * predict the amount of conversion in advance (etc. couple months)
    * find the insights correlated to the amount

* dataset
    * internal
        * downloads, registration, churn, transaction, user count, stream count
    * external
        * downloads of competitive Product
        * ptt / dcard discussion
        * commercial cost
        * app store ratings
        * fan page posts and likes

    * process (internal only)
        * data exploration
            * discovery: feature importance changes a lot between 2017, 2018 and 2019
        * model
            * xgboost
            * VAR (vector autoregression)
            * rmse: about 10,000 (conversion: 40,000)

    * difficulties
        * features for now may not presentative enough for prediction
        * monthly data (36x80) is too few and may be biased
    * restart with weekly data
    * transactions prediction
        * model AR (lags: 24 weeks)
            * result: rmse: 41586.133
            * ![](https://gitlab.kkinternal.com/researchcenter/rdc-image/raw/master/ds/cynthiayang/trans_prediction.png)
            * difference prediction
            * ![](https://gitlab.kkinternal.com/researchcenter/rdc-image/raw/master/ds/cynthiayang/trans_prediction_diff.png)


