Update index.md

index.md

@@ -140,6 +140,22 @@ EDA involved:
   Z = \frac{X - \mu}{\sigma}
   $$
 
+Where : 
+- **\( Z \):** The Z-score, representing the normalized value of the feature. It indicates how many standard deviations a particular value (\( X \)) is from the mean (\( \mu \)).
+- **\( X \):** The original value of the feature being normalized.
+- **\( \mu \):** The mean (average) value of the feature, calculated as:
+  $$
+  \mu = \frac{\sum_{i=1}^{n} X_i}{n}
+  $$
+  where:
+  - \( n \): Number of data points.
+  - \( X_i \): Individual feature values.
+
+- **\( \sigma \):** The standard deviation of the feature, measuring the spread or variability of the data around the mean, calculated as:
+  $$
+  \sigma = \sqrt{\frac{\sum_{i=1}^{n} (X_i - \mu)^2}{n}}
+  $$
+
 ### Clustering Process
 1. **Optimal K Selection**:
    - Used the **Elbow Method** and **Silhouette Scores** to determine \( k = 4 \).