Update README.md

Author: ksator

README.md

@@ -19,71 +19,6 @@
   - [Recursive Feature Elimination](#recursive-feature-elimination)  
 
 
-# Introduction to arrays using numpy
-
-Arrays are used to store multiple values in one single variable.  
-An array is a kind of list.  
-All the elements in an array are the exact same type  
-
-Let's use the numpy python library to handle arrays  
-
-```
->>> import numpy as np
-```
-
-data type int64 
-```
->>> ti = np.array([1, 2, 3, 4])
->>> ti
-array([1, 2, 3, 4])
->>> ti.dtype
-dtype('int64')
->>> 
-```
-
-data type float64
-```  
->>> tf = np.array([1.5, 2.5, 3.5, 4.5])
->>> tf.dtype
-dtype('float64')
-```
-access to some elements
-```
->>> t = np.array ([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19])
->>> t
-array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19])
->>> t[:6]
-array([0, 1, 2, 3, 4, 5])
->>> t
-array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19])
-```
-multi dimensions array
-```
->>> tf2d = np.array([[1.5, 2, 3], [4, 5, 6]])
->>> tf2d
-array([[1.5, 2. , 3. ],
-       [4. , 5. , 6. ]])
->>> tf2d.dtype
-dtype('float64')
->>> tf2d.shape
-(2, 3)
->>> tf2d.ndim
-2
->>> tf2d.size
-6
-```
-random number (float) generation
-```
->>> np.random.rand(10)
-array([0.67966246, 0.26205002, 0.02549579, 0.11316062, 0.87369288,
-       0.16210068, 0.51009515, 0.92700258, 0.6370769 , 0.06820358])
-```
-```
->>> np.random.rand(3,2)
-array([[0.78813667, 0.92470323],
-       [0.63210563, 0.97820931],
-       [0.44739855, 0.03799558]])
-```
 
 # visualize a dataset using seaborn