Update README.md

README.md

@@ -22,7 +22,7 @@ tags:
 
 # 🏗️ Actuarial Model Point Generator
 
-A flexible Gradio app to generate fully customized **synthetic seriatim model points** for use in actuarial testing, clustering, or analytics.
+A flexible Gradio app to generate fully customized **synthetic seriatim model points** for use in actuarial testing, clustering, or analytics. This tool now also provides immediate data insights through interactive summaries and visualizations.
 
 [![Open in Spaces](https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg)](https://huggingface.co/spaces/alidenewade/actuarial-model-point-generator)
 
@@ -30,7 +30,7 @@ A flexible Gradio app to generate fully customized **synthetic seriatim model po
 
 ## 🌟 What’s New
 
-This version adds **complete UI control** over generation logic:
+This version enhances data generation with **complete UI control** and adds **interactive data analysis features**:
 
 - 👥 Number of policies (100 to 50,000)
 - 🎲 Random seed for reproducibility
@@ -39,6 +39,11 @@ This version adds **complete UI control** over generation logic:
 - 📆 Multiple selectable policy terms (5–30 years)
 - 🧑 Include or exclude sex (M/F)
 - 📦 Choose between fixed or variable policy count
+- 📊 **In-depth Data Insights**:
+    - Descriptive statistics for key numerical fields.
+    - Distribution plot for 'Sum Assured' with a fitted normal curve.
+    - Frequency tables for categorical data like 'Sex' and 'Policy Term'.
+    - All summaries presented in an easy-to-use Tabbed interface below the main data table.
 
 ---
 
@@ -56,29 +61,41 @@ Each generated row represents a policy and includes the following columns:
 
 ---
 
+## 📊 Data Analysis & Insights
+
+Beyond data generation, the app now provides immediate analytical feedback on the generated dataset through dedicated tabs:
+
+-   **Numerical Summary Tab**: Get a quick overview of the numerical columns (`age_at_entry`, `sum_assured`, `duration_mth`, `policy_count`) with key descriptive statistics like mean, standard deviation, min/max values, and quartiles.
+-   **Distribution Plot Tab**: Visualize the distribution of the `sum_assured` column. A histogram shows the actual generated data's distribution (which is uniform based on inputs), and a normal bell curve is fitted and overlaid for illustrative comparison, helping to understand the data's spread and central tendency.
+-   **Categorical Summary Tab**: Understand the composition of your synthetic portfolio with frequency counts and percentages for `sex` and `policy_term` distributions.
+
+---
+
 ## ✅ How to Use
 
-1.  Adjust your filters on the left.
-2.  Click **“Generate Model Points”**.
-3.  Preview the results in the table.
-4.  Click **“Download Excel”** to save the data.
+1.  Adjust your generation parameters using the filters on the left panel.
+2.  Click the **“Generate Model Points”** button.
+3.  Preview the generated data in the main table.
+4.  Explore the interactive summaries (descriptive statistics, distribution plots, categorical breakdowns) in the tabs located below the main data table.
+5.  Click the **“Download Excel”** button to save the generated data table.
 
 ---
 
 ## 🧠 Use Cases
 
-- Cluster-based model point selection
-- Stress testing & actuarial simulations
-- Product mix scenario planning
-- Teaching or training actuarial students
-- Model validation tools
+- Cluster-based model point selection and analysis.
+- Stress testing actuarial models and validating assumptions.
+- Developing and testing new insurance products or features.
+- Scenario planning for different product mixes or demographic profiles.
+- Educational tool for teaching actuarial concepts and data analysis.
+- Rapidly creating datasets for model validation or machine learning tasks in an insurance context.
 
 ---
 
 ## 📦 File Export
 
 The download button exports the generated data table to an Excel file (`.xlsx`). The **`policy_id`** is included as the first column, and the DataFrame index is omitted from the file.
-Warnings will be shown if inputs are invalid (e.g., min age ≥ max age).
+Warnings will be shown if input parameters are invalid (e.g., minimum age ≥ maximum age).
 
 ---
 
@@ -91,10 +108,10 @@ cd actuarial-model-point-generator
 
 # Install dependencies
 pip install -r requirements.txt
+```
 
-# Run the app
+## Run the app
 python app.py
-```
 
 ## 🙌 Acknowledgements
 Huge thanks to the Lifelib community for their open-source contributions to life actuarial modeling in Python.