Datasets:

Davichick
/

Credit-score-analysis

@@ -42,19 +42,19 @@ Once the data was clean, I moved to visualization to tell the story of the datas
 **Target Distribution:** I first checked the Credit Score distribution. I found that Standard is the most common rating, followed by Poor and Good.
-![Credit Score Distribution](credit_score_dist.png)
 **Demographics:** The Age histogram showed that the majority of customers are young adults in their 20s and 30s.
-![Age Distribution](age_dist.png)
 **Financial Overview:** I analyzed Annual Income. Because there were a few extreme earners, I filtered the visualization to under 250k to clearly see the distribution of the general population.
-![Annual Income Filtered](income_dist.png)
 **Correlation Heatmap:** I generated a heatmap to find connections between variables. This helped me identify that Interest Rate, Num_of_Loan, and Outstanding Debt have the strongest relationships with the credit score.
-![Correlation Heatmap](heatmap.jpg)
 ---
@@ -65,17 +65,17 @@ I answered this question through three specific analyses:
 ### 1. The Occupational Factor (Bar Chart)
 I compared credit scores across different professions. I found that the distribution is almost identical regardless of the job title. This taught me that what you do for a living matters less than how you manage your money.
-![Occupation Analysis](occ_vs_credit.png)
 ### 2. The Delay Threshold (Boxplot)
 I looked at the number of delayed payments. The data revealed a clear tipping point: customers with a Good score usually have fewer than 10 delays, while Poor customers often have a median of 17 or more. This is a critical factor for the bank's rating.
-![Delayed Payments Analysis](delay_boxplot.png)
 ### 3. The Debt Burden (KDE Plot with Baseline)
 I built a custom function to compare Outstanding Debt against the Overall Average (1426.22). The visual evidence showed that Good customers stay well below this average line, while Poor customers almost always carry a debt load significantly higher than the average.
-![Debt Comparison KDE](debt_kde.png)
 ---

 **Target Distribution:** I first checked the Credit Score distribution. I found that Standard is the most common rating, followed by Poor and Good.
+![Credit Score Distribution](Credit%20Score%20Distribution.png)
 **Demographics:** The Age histogram showed that the majority of customers are young adults in their 20s and 30s.
+![Age Distribution](Age%20Distribution.png)
 **Financial Overview:** I analyzed Annual Income. Because there were a few extreme earners, I filtered the visualization to under 250k to clearly see the distribution of the general population.
+![Annual Income Filtered](Annual%20Income%20Filtered.png)
 **Correlation Heatmap:** I generated a heatmap to find connections between variables. This helped me identify that Interest Rate, Num_of_Loan, and Outstanding Debt have the strongest relationships with the credit score.
+![Correlation Heatmap](Correlation%20Heatmap.png)
 ---
 ### 1. The Occupational Factor (Bar Chart)
 I compared credit scores across different professions. I found that the distribution is almost identical regardless of the job title. This taught me that what you do for a living matters less than how you manage your money.
+![Occupation Analysis](Occupation%20Analysis.png)
 ### 2. The Delay Threshold (Boxplot)
 I looked at the number of delayed payments. The data revealed a clear tipping point: customers with a Good score usually have fewer than 10 delays, while Poor customers often have a median of 17 or more. This is a critical factor for the bank's rating.
+![Delayed Payments Analysis](Delayed%20Payments%20Analysis.png)
 ### 3. The Debt Burden (KDE Plot with Baseline)
 I built a custom function to compare Outstanding Debt against the Overall Average (1426.22). The visual evidence showed that Good customers stay well below this average line, while Poor customers almost always carry a debt load significantly higher than the average.
+![Debt Comparison KDE](Debt%20Comparison%20KDE.png)
 ---