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Term Deposit Subscription Prediction 

Term Deposit Subscription Prediction 


A term deposit is a fixed-term investment that includes the deposit of money into an account at a financial institution. In the financial market, banks are focusing their efforts on attracting deposit customers by utilizing various marketing strategies to secure a stable funding source. Banks' strategy is focused on the lowest procurement cost which is attractive to everyone. In this context, the importance of discovering high-margin potential customers has become more important than ever, and as a result, finding customers is raising the need to leverage a data-driven science. Therefore, we will examine the effectiveness of the bank's marketing strategy through ClickAI by predicting if the client will subscribe a term deposit.

 - Predicting subscription to term deposits using data features that include marketing campaign periods.
 - Explore features that have a significant impact on whether or not to subscribe to a term deposit.

Create Model

1. Check Data

[bank.csv]
 - This dataset is composed of 4521 rows with information about contact type, last contacted date, number of times contacted, and  other customer demographic information such as age, job, education, marital status, credit, account balance, and loan of each individual customers.
 - The target variable 'previous' has two classes: 0(no) and 1(yes).

2. Build Model |  Create a term deposit prediction model using CLICK AI's structured data category classification

2.1 Model Development

[Main Page → Click on 'Create a new project.']

['Adding data' → Upload data file to Click AI.]

[Select uploaded data file from the dataset list → Click on 'Start AI.']

2.2 Model Configuration

[Select your preferred learning type, preferred method, and the value you would like to analyze/predict.]
 - The target variable 'y' has two values of 'yes' and 'no'. Select 'Structured data classification' as your learning type.
 - To develop a model focused on maximizing its accuracy, select 'with higher accuracy' as your preferred way.
 - Select 'y_bank.csv' as your value you want to predict.
 - Click on (?) for more details about each options.

2.3 Data Preprocessing

['Summary' → Decide which columns from the data set you would like to use to train the model.]
 - The columns will be automatically unchosen from being used as part of the training data.

[Data Preprocessing → Decide which columns you would like to preprocess.]
 - If you would like to preprocess data, continue by clicking on 'Preprocessing.'

[Choose preferred preprocessing methods and click 'confirm.']

['Data' → View your data file.]

2.4 Model Generation

[After all of the above steps are completed, press the 'Start' button to begin processing the AI model.]

[All of the processed models will appear in the list at the bottom of the page. You may sort the models according to RMSE, errorRate or Mase.]

3. View Details | Identify important factors of your model

- Let's find out the factors that have the biggest impact on the predictions.

[Training Model 27 → Click on 'Details.']
 - View details of the [Training Model 27] with the highest accuracy.

[Feature Importance → See which indices are important for the prediction model. Additional information are available throughout the different tabs.]

[ eXplainable AI ]
 - Contact frequency was analyzed to have the biggest impact on customers' subscriptions. Last contacted month and the success of the last campaign also displayed significant impacts on the predictions.
 - You may explore through additional details and statistics on the 'Details' page.

[API Connection→ You can connect model results through API connections.]
  - API sources are available for multiple programming languages.

4. Analyze Data | Analyze features to derive insights from data

[Training Model 27 → Click on 'Analyze']
 - View data analysis of the [Training Model 27] with the highest accuracy.

[AI+ Analysis]
 - The AI+ analysis feature provides analysis and visualization of the data, aimed to help  users better understand statistical information.
 - Top 5 indices highly associated with customer subscriptions are organized for users.

[Visualized information such as histograms and distribution charts are available for users.]

5. Predict Data | Make single or multiple predictions using your model

5.1 Single Prediction
 - Predict customer subscriptions for each individual cases by entering customer information.

[Training Model 27→ Click on 'Single prediction']
 - Make single predictions using the [Training Model 27] with the highest accuracy.
 - 'Fill in random values' feature automatically fills all of the parameters randomly. After checking that all of the parameters have been filled appropriately, click 'Run' to proceed.

[The predicted result is displayed on the right.]
 - You may also manually enter values to make predictions.
 - If a value is out of range, the value will appear red.

5.2 Collective Prediction 
- Enter data to the downloaded collective prediction template, and run customer subscription prediction for multiple customers.

[Training Model27 → Click on 'Collective prediction']
 - Make collective predictions using the [Training Model 27] with the highest accuracy.

[Click on 'Download template for prediction' to download the prediction template.]

[Enter values to the prediction template (left), and prepare a prediction data set (right) to feed into the prediction model.]

[Upload the prepared prediction data, and proceed by pressing on 'Next'.]

[Prediction results will be available on a new csv file. The last column displays the prediction results for each employee  information.]
 - Collective prediction may require some time. You can download the prediction results from the notifications tab after completion.

5.3 Auto-Labeling
 - Enter data to the downloaded auto-labeling template, and run customer subscription prediction for multiple cases.
 - Auto-labeling prediction will automatically preprocess data as needed.

[Training Model 27 → Click on 'Auto-labeling]
 - Make auto-labeling predictions using the [Training Model 27] with the highest accuracy.

[Click on 'Download template for prediction' to download the prediction template.]

[Enter values to the prediction template (left), and prepare a prediction data set (right) to feed into the prediction model.]

[Upload the prepared prediction data, and proceed by pressing on 'Next'.]

[Prediction results will be available on a new csv file. The last column displays the prediction results for each customer information.]
 - Auto-labeling prediction may require some time to complete. You can download the prediction results from the notifications tab after completion.

6. Share | Share your AI model with API

[Training Model 27 → Click on 'Details']

[Sharing your service app]
 - The URL link will direct you to shared page of the model.
 - Share your model using the URL link.
 - Others may have access to your model through the URL link. If somebody uses the model to make predictions, it will be counted from your prediction count.
 - 'Analyze' feature offers both explainable AI and visualizations on the shared link.
 - You can put your AI model on sale by using the AI store. For details, please check AI 스토어.

Consult with an expert now.

Consult with an expert now.