Update README.md

README.md

@@ -16,7 +16,7 @@ The dataset features a broad spectrum of variables, from demographic information
 By combining the incidence and termination datasets, insurers gain a comprehensive understanding of both the initiation and conclusion of LTC insurance policies. This complete perspective is vital for actuaries and analysts to assess risk, set appropriate reserves, design tailored products, and determine pricing strategies that reflect the true cost of providing LTC coverage. It also aids in regulatory compliance and ensures that insurance products are both financially viable for the provider and beneficial for the consumer. Through careful analysis of this aggregated data, insurers can predict trends, modify underwriting practices, and make informed decisions to manage their portfolios effectively.
 
 
-### Dataset Sources [optional]
+### Dataset Sources
 
 <!-- Provide the basic links for the dataset. -->
 
@@ -25,20 +25,60 @@ By combining the incidence and termination datasets, insurers gain a comprehensi
 
 ## Uses
 
-<!-- Address questions around how the dataset is intended to be used. -->
+The long-term care (LTC) aggregated dataset is intended to serve as a foundational tool for actuaries and data scientists aiming to conduct comprehensive experience studies within the insurance sector. Experience studies are essential for understanding the past performance and projecting the future trends of insurance products, and this dataset provides the necessary detailed information to perform such analyses.
 
-### Direct Use
+Traditional Framework(Used mostly in the insurance companies now): <br>
+One of the primary uses of this dataset is to apply the chain ladder method, a traditional actuarial technique used to predict future claim costs and the reserve amounts needed to cover these potential claims. The chain ladder method relies on the assumption that historical claims development can predict future claims development, making it possible to estimate the reserves required for incurred but not reported (IBNR) claims as well as incurred but not enough reserved (IBNER) claims.
 
-<!-- This section describes suitable use cases for the dataset. -->
+Bayesian Framework(Innovative methods recommended): <br>
+The dataset can be utilized within a Bayesian framework to enhance the predictive modeling process. A Bayesian approach allows for the incorporation of prior knowledge or expert opinion into the statistical models, updating these beliefs with data from the dataset to generate a posterior distribution of the expected claims. This method is particularly useful when dealing with complex systems or when the available data is sparse or contains a high level of uncertainty.
+
+1. In the context of the LTC aggregated dataset, Bayesian hierarchical models can be applied to account for multiple levels of variability, such as between different policyholders, across various regions, and over time. These models can also help in understanding the effects of policy features and policyholder characteristics on the likelihood and timing of claims, providing a deeper insight into risk factors.
+
+2. Another significant use of the dataset in the Bayesian framework is the development of predictive distributions for various risk metrics. These could include the probability of claim terminations due to death or recovery, the expected number of claims within certain diagnosis categories, or the expected claim durations.
 
-[More Information Needed]
 
 
 ## Dataset Structure
 
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
+Based on the output of the `summary()` function for your dataset, here's a description of the dataset fields and additional information about the dataset structure:
+
+### Dataset Structure Description
+1. LTC Claim Incidence
+- **Group_Indicator**: Categorical variable indicating the group to which the policyholder belongs.
+- **Gender**: Categorical variable indicating the gender of the policyholder.
+- **Issue_Age_Bucket**: Categorical variable denoting the age range when the policy was issued.
+- **Incurred_Age_Bucket**: Categorical variable representing the age range when the claim was incurred.
+- **Issue_Year_Bucket**: Categorical variable indicating the year range when the policy was issued.
+- **Policy_Year**: Categorical variable indicating the specific year of the policy.
+- **Marital_Status**: Categorical variable indicating the marital status of the policyholder.
+- **Premium_Class**: Categorical variable indicating the classification of the premium.
+- **Underwriting_Type**: Categorical variable indicating the type of underwriting applied to the policy.
+- **Coverage_Type_Bucket**: Categorical variable indicating the coverage type category.
+- **Tax_Qualification_Status**: Categorical variable indicating the tax qualification status of the policy.
+- **Inflation_Rider**: Categorical variable indicating whether an inflation protection rider is attached to the policy.
+- **Rate_Increase_Flag**: Categorical variable indicating if there has been a rate increase on the policy.
+- **Restoration_of_Benefits**: Categorical variable indicating whether benefits have been restored.
+- **NH_Orig_Daily_Ben_Bucket**: Categorical variable indicating the original daily benefit amount for nursing home care.
+- **ALF_Orig_Daily_Ben_Bucket**: Categorical variable indicating the original daily benefit amount for assisted living facilities.
+- **HHC_Orig_Daily_Ben_Bucket**: Categorical variable indicating the original daily benefit amount for home health care.
+- **NH_Ben_Period_Bucket**: Categorical variable indicating the benefit period for nursing home care.
+- **ALF_Ben_Period_Bucket**: Categorical variable indicating the benefit period for assisted living facilities.
+- **HHC_Ben_Period_Bucket**: Categorical variable indicating the benefit period for home health care.
+- **NH_EP_Bucket**: Categorical variable indicating the elimination period for nursing home care.
+- **ALF_EP_Bucket**: Categorical variable indicating the elimination period for assisted living facilities.
+- **HHC_EP_Bucket**: Categorical variable indicating the elimination period for home health care.
+- **Region**: Categorical variable indicating the geographical region of the policy.
+- **Active_Exposure**: Numeric variable indicating the active exposure amount.
+- **Total_Exposure**: Numeric variable indicating the total exposure amount.
+- **Claim_Count**: Numeric variable indicating the count of claims.
+- **Count_NH**: Numeric variable indicating the count of nursing home claims.
+- **Count_ALF**: Numeric variable indicating the count of assisted living facility claims.
+- **Count_HHC**: Numeric variable indicating the count of home health care claims.
+- **Count_Unk**: Numeric variable indicating the count of claims with unknown categorization.
+
+2. LTC Claim Termination
 
-[More Information Needed]
 
 ## Dataset Creation
 
@@ -64,21 +104,7 @@ By combining the incidence and termination datasets, insurers gain a comprehensi
 
 [More Information Needed]
 
-### Annotations [optional]
-
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
-
-#### Annotation process
-
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
-
-[More Information Needed]
-
-#### Who are the annotators?
 
-<!-- This section describes the people or systems who created the annotations. -->
-
-[More Information Needed]
 
 #### Personal and Sensitive Information
 
@@ -98,10 +124,6 @@ By combining the incidence and termination datasets, insurers gain a comprehensi
 
 Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
 
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
-