Update README.md

README.md

@@ -7,8 +7,7 @@ license: mit
 
 This dataset, called **Adult Census Income Dataset Optimized**, is an optimized version of the **Adult Census Income Dataset**. The latter comes from the **UCI Machine Learning Repository** and is commonly used in classification tasks to predict whether a person earns more or less than $50,000 per year based on various demographic characteristics.
 
-We optimized the dataset by adding two new columns: **capital_gain_loss_ratio** and **age_group**, to enrich the quality of the features. These additions aim to improve model performance, provided they are properly integrated and preprocessed. The work was successful because...
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+We optimized the dataset by adding two new columns: **capital_gain_loss_ratio** and **age_group**, to enrich the quality of the features. These additions aim to improve model performance, provided they are properly integrated and preprocessed. 
 ### Data Sources
 The data was extracted from the **U.S. Census Survey**, a demographic census conducted by the United States Census Bureau. It was used by the **Census Bureau** to estimate the income of adults based on different personal and social characteristics. The dataset is available on the **UCI Machine Learning Repository** and is commonly used in machine learning research, particularly for classification tasks.
 
@@ -70,14 +69,14 @@ AI can be used to extract long-term trends from this dataset, such as the evolut
 
 2. **workclass**  
    - **Description**: The individual's work category. It describes the type of employment status, such as:  
-     - Private: Private sector employee  
-     - Self-emp-not-inc: Unincorporated self-employed  
-     - Self-emp-inc: Incorporated self-employed  
-     - Federal-gov: Federal government employee  
-     - Local-gov: Local government employee  
-     - State-gov: State government employee  
-     - Without-pay: Unpaid  
-     - Never-worked: Never worked  
+ 	- Private: Private sector employee  
+ 	- Self-emp-not-inc: Unincorporated self-employed  
+ 	- Self-emp-inc: Incorporated self-employed  
+ 	- Federal-gov: Federal government employee  
+ 	- Local-gov: Local government employee  
+ 	- State-gov: State government employee  
+ 	- Without-pay: Unpaid  
+ 	- Never-worked: Never worked  
    - **Type**: string
 
 3. **fnlwgt**  
@@ -130,9 +129,9 @@ AI can be used to extract long-term trends from this dataset, such as the evolut
 
 15. **age_group**  
    - **Description**: This column groups individuals into three age groups:  
-     - Young: Individuals aged 0-30 years.  
-     - Middle-aged: Individuals aged 30-60 years.  
-     - Senior: Individuals aged 60-100 years.  
+ 	- Young: Individuals aged 0-30 years.  
+ 	- Middle-aged: Individuals aged 30-60 years.  
+ 	- Senior: Individuals aged 60-100 years.  
    - **Type**: int64  
    - **Purpose**: This column allows for population segmentation by age, useful for demographic analyses and income trend studies.
 
@@ -143,6 +142,9 @@ AI can be used to extract long-term trends from this dataset, such as the evolut
 
 17. **income**  
    - **Description**: The individual's annual income. It can be either:  
-     - ">50K": Income greater than $50,000  
-     - "<=50K": Income less than or equal to $50,000  
+ 	- ">50K": Income greater than $50,000  
+ 	- "<=50K": Income less than or equal to $50,000  
    - **Type**: string (target variable for prediction models).
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