Update ReadMe.md

ReadMe.md

@@ -44,7 +44,7 @@ I decided to handle these outliers by capping them. Replacing extreme, unrealist
 Once the data was clean, I moved to the visualization stage to tell the story of the data.
 
 ### Target and Demographics
-I first looked at the **Credit Score Distribution**. The "Standard" rating is the most common, while "Good" credit is the rarest, showing how difficult it is to reach the top tier.
+I started my visualization process by looking at the distribution of the **Credit Score**, which is my target variable. This chart shows me exactly how many people fall into each category, such as "Good", "Standard", or "Poor". It is very important for me to understand this balance now, because this is the specific value I want to predict later in the project.
 
 ![Credit Score Distribution](Credit%20Score%20Distribution.png)
 
@@ -53,10 +53,21 @@ When I analyzed the **Age Distribution**, the graph appeared to end around age 6
 ![Age Distribution](Age%20Distribution.png)
 
 ### Financial Insights
-For **Annual Income**, I filtered the view to under 250,000 to focus on the 99% of the population. My **Correlation Heatmap** was a key discovery tool. It highlighted that Interest Rate and Outstanding Debt have the strongest negative correlation with the credit score. As debt goes up, the score almost always goes down.
+For the **Annual Income** graph, I decided to filter the view to only show people earning under 250,000. I did this so I could actually see the shape and distribution of the data. If I hadn't filtered the chart, the few extremely rich people with millions in income would have forced the rest of the data into one tiny and unreadable line.Making it impossible for me to see any patterns.
+
+![Annual Income Filtered](Annual%20Income%20Filtered.png)
+
+### 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.
 
 ![Correlation Heatmap](Correlation%20Heatmap.png)
 
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 ## Phase 3: Research Question & Findings