<div> <ul class="breadcrumb"> <li><a href="/">Home</a></li> <li><a href="/diy-platform">DIY Platform </a></li> <li><a href="/diy-platform/app-store-index/">App Store </a></li> <li> Churn Analysis </li> </ul> </div> # Churn Analysis Churn Analysis application predicts the customer churn score that indicates the probability of customer getting churned. Churn analysis is modeled as a typical classification problem. Training data is generated from the historical sales and payment data, a target label is assigned to each customer indicating whether he has churned or not. This training data is used to train a model which will be used to predict the churn score of new customers. ## How to build my first Churn Analysis app? ### Data Preparation **Sample dataset:** To get you started immediately, we have provided a sample training data set of telecommunication business, which contains 19 different features and a target label named **Churn**. The value of target label is either *True* or *False*, defining whether the customer has churned or not. > To prepare your own training dataset [Visit here](/diy-platform/building-churn-model/understanding-data-for-churn-applications) ### Build App 1. Go to the app store, build a churn analysis app by searching for *Churn Analysis* 2. You will be redirected to the app settings page where you can configure and run the app 3. Under **Quick Settings**, 3.1. Select an existing dataset or upload a new dataset and click **Save** 3.2. Select the target field, the field that indicates whether customer is churned or not, and click **Save** 4. Now click on **Run Now** to start the app. The app may run for 5 to 20 minutes (dependending on the size of input data) and then the trained model will be saved. 5. Use this model to get the churn prediction 5.1. Click on **Build App** on the model to build the inference app using that model 5.2. Provide the input in the given fields and click on **Get Results**. The predicted churn class and scores will be displayed on the right side of the screen. 6. [Optional] To use this model to predict more than one examples, [Visit here] [TODO] ### Output The output of the infer app will the predicted class (churn or non churn) and probability score of prediction. > Go ahead and build your first [Churn Analysis App](https://app.datoin.com/app-store)