Update README.md

Author: syamkakarla98

README.md

@@ -6,6 +6,7 @@ The things that you must have a decent knowledge on:
 ```
     * Python
     * MatLab
+    * Linear Algebra
 ```
 
 ### Installing
@@ -77,3 +78,11 @@ https://github.com/syamkakarla98/Dimensionality-reduction-and-classification-on-
    * By performing **PCA** on the corrected indian pines dataset results **100 Principal Components(PC'S)**.
    * since, the initial two Principal Components(PC'S) has **92.01839071674918** variance ratio. we selected two only.
    * Initially the dataset contains the dimensions **21025 X 200** is drastically reduced to **21025 X 2** dimensions.
+   * The time taken for classification before and after Principal Component Analysis(PCA) is:
+         
+         |   Dataset     |   Accuracy    | Time Taken |
+         | ------------- |:-------------:| ----------:|
+         |  Before PCA   |   72.748890   |  17.6010   |
+         |  After PCA    |   60.098187   | 0.17700982 |
+       
+   * Hence, the **time** has been reduced with a lot of difference and the **classification accuracy(C.A)** also reduced but the  C.A can increased little bit by varying the 'k' value.