Datasets:

Davichick
/

Credit-score-analysis

@@ -60,7 +60,7 @@ For the **Annual Income** graph, I decided to filter the view to only show peopl
 ### Intrest Rate vs Credit score
 I created a Boxplot to compare Interest Rates against Credit Scores, and I found a very interesting result. I can clearly see that people with a "Poor" credit score pay much higher interest rates compared to those with a "Good" score. This confirms a strong relationship between these two variables, and it helps me understand one of the main factors that drives a person's credit rating.
-![Delayed Payments Analysis](Delayed%20Payments%20Analysis.png)
 ### Heatmap (data correlation)
 I generated this heatmap to visualize the correlations between all the numeric features in my data. The colors help me spot patterns instantly. For example, red squares indicate a strong positive relationship, while blue squares show a negative one. This tool is essential for me because it highlights which variables, like interest rates or number of loans, have the most significant impact on each other.
@@ -81,7 +81,7 @@ I found that credit score distribution is almost identical across all profession
 ### 2. The Delay Threshold
 Using a Boxplot, I discovered a clear tipping point: customers with a "Good" rating rarely have more than 8-10 delayed payments. Once a customer crosses 15-17 delays, they almost certainly fall into the "Poor" category.
-![Interest Rate vs Credit Score](Interest%20Rate%20vs%20Credit%20Score.png)
 ### 3. The Debt Burden
 I compared **Outstanding Debt** against the **Overall Average (1426.22)**. The blue "Good" curve is peaked far to the left of the average line, while the green "Poor" curve is shifted heavily to the right. This is the "Debt Trap" in visual form.

 ### Intrest Rate vs Credit score
 I created a Boxplot to compare Interest Rates against Credit Scores, and I found a very interesting result. I can clearly see that people with a "Poor" credit score pay much higher interest rates compared to those with a "Good" score. This confirms a strong relationship between these two variables, and it helps me understand one of the main factors that drives a person's credit rating.
+![Interest Rate vs Credit Score](Interest%20Rate%20vs%20Credit%20Score.png)
 ### Heatmap (data correlation)
 I generated this heatmap to visualize the correlations between all the numeric features in my data. The colors help me spot patterns instantly. For example, red squares indicate a strong positive relationship, while blue squares show a negative one. This tool is essential for me because it highlights which variables, like interest rates or number of loans, have the most significant impact on each other.
 ### 2. The Delay Threshold
 Using a Boxplot, I discovered a clear tipping point: customers with a "Good" rating rarely have more than 8-10 delayed payments. Once a customer crosses 15-17 delays, they almost certainly fall into the "Poor" category.
+![Delayed Payments Analysis](Delayed%20Payments%20Analysis.png)
 ### 3. The Debt Burden
 I compared **Outstanding Debt** against the **Overall Average (1426.22)**. The blue "Good" curve is peaked far to the left of the average line, while the green "Poor" curve is shifted heavily to the right. This is the "Debt Trap" in visual form.