Merge pull request #733 from Quantum-Software-Development/FabianaCampanari-patch-1

Update README.md

Author: FabianaCampanari

README.md

@@ -1850,29 +1850,36 @@ The modulo operator (%) returns the remainder of a division. In simulations invo
 
 ## 2- [Why Use Modulo in Random Number Simulations]() ?
 
-In simulations, we often need random values within a specific interval — for example, simulating a dice roll (1 to 6) or selecting a random day of the week (0 to 6). Random number generators typically produce large numbers, so the modulo operation helps normalize these into the desired range.
+In simulations, we often need random values within a specific interval, for example, simulating a dice roll (1 to 6) or selecting a random day of the week (0 to 6). Random number generators typically produce large numbers, so the modulo operation helps normalize these into the desired range.
 
 <br>
 
 ## 3- [Example]():
 
 Imagine your random number generator gives you a number like 247. If you want to simulate a 6-sided dice roll:
 
+
+```pyhton
 dice_roll = (247 % 6) + 1  # Adding 1 to shift range from [0–5] to [1–6]
+```
+
+#### **This ensures the result is always [between 1 and 6]()**.
 
-This ensures the result is always between 1 and 6.
+<br>
 
-Simulation Use Case:
+## 4- [Simulation Use Case]():
 
 In a banking simulation, you might simulate customer behavior across 7 days of the week. If a random function returns a number like 123456, you can use:
 
+```python
 day_of_week = 123456 % 7  # Result is between 0 and 6
+```
 
-This maps any large number into the range of weekdays (e.g., 0 = Sunday, 6 = Saturday).
+####  **This maps any large number [into the range of weekdays]() (e.g., 0 = Sunday, 6 = Saturday)**.
 
 <br>
 
-## 4- [Summary]():
+## 5- [Summary]():
 
 In simulations, the % operator is a simple and efficient way to control the range of random outputs. It transforms raw random data into usable, context-specific values, essential for realistic and accurate simulation scenarios.